Report SWE 496 Group 3 (1) PDF
Document Details
Uploaded by Deleted User
KSU
Tags
Related
- Simplified Guide for AEs: Software Engineering, DevOps, and Cybersecurity PDF
- original_1489125567_Chapter_1_Introduction_to_software_product_engineering.pdf
- Chapter 2: Software Engineering Lifecycle PDF
- Software Engineering.pdf
- Software Engineering 9th Edition PDF
- Gls University Software Engineering PDF
Summary
This document appears to be a graduation project report for a Software Engineering class, Part I (SWE 496). It includes a table of contents, list of figures, and an abstract. The document also includes references.
Full Transcript
Your Project Title Graduation Project, Part-I (SWE 496) Software Engineering Department CCIS, KSU Project Advisor: Dr. Sarah Alhozaimy Submitted by Najla Aljarba, 443200432 Lina Alsuhaibani, 443200729 Maram Alamri, 443200518 Rawan Alqhtani,...
Your Project Title Graduation Project, Part-I (SWE 496) Software Engineering Department CCIS, KSU Project Advisor: Dr. Sarah Alhozaimy Submitted by Najla Aljarba, 443200432 Lina Alsuhaibani, 443200729 Maram Alamri, 443200518 Rawan Alqhtani, 441200407 Sarah, 443200652 Date submitted ABSTRACT Write your abstract here. 1 Table of Contents 1. INTRODUCTION................................................................................................................................................ 1 2. DOMAIN ANALYSIS.......................................................................................................................................... 1 3. RISK/CONSTRAINTS.........................................................................................................................................2 4. PROJECT PLAN..................................................................................................................................................3 5. QUALITY ASSURANCE PLAN........................................................................................................................ 3 6. REQUIREMENTS............................................................................................................................................... 4 6.1 FUNCTIONAL REQUIREMENTS...............................................................................................................................4 6.2 NON-FUNCTIONAL REQUIREMENTS...................................................................................................................... 4 7. PROBLEM COMPLEXITY................................................................................................................................4 8. SYSTEM USE-CASES......................................................................................................................................... 4 8.1 USE CASE1....................................................................................................................................................... 5 8.2 USE CASE2....................................................................................................................................................... 5 9. ANALYSIS CLASS...............................................................................................................................................6 10. INTERACTION DIAGRAM...........................................................................................................................6 11. DESIGN CLASS............................................................................................................................................... 8 12. SYSTEM ARCHITECTURE.......................................................................................................................... 8 13. USER INTERFACE MOCKUP...................................................................................................................... 8 14. DATABASE SCHEMA.....................................................................................................................................8 15. ALGORITHMS................................................................................................................................................ 9 16. EXPECTED DEPLOYMENT......................................................................................................................... 9 17. TEST SCENARIO............................................................................................................................................ 9 18. PROJECT STATUS..........................................................................................................................................9 19. CONCLUSION............................................................................................................................................... 10 20. REFERENCE..................................................................................................................................................10 2 List of Figures Figure 1. Smart room use cases 4 Figure 2. Successful order placement 5 Figure 3. Successful order placement 6 Figure 4. Successful face recognition log in 7 Figure 5. System deployment diagram 8 3 List of Tables Table 2. Test Table 8 4 1. Introduction Problem In light of the technical development witnessed in this era and the increasing number of Internet users, phishing URLs have become a major threat to cybersecurity, as these URLs seek to deceive individuals in order to steal their sensitive information such as passwords, credit card numbers, and personal information. These phishing methods are constantly evolving, making it difficult for traditional systems to detect them effectively, which indicates the need to develop advanced techniques to improve the ability to detect phishing URLs. Proposed solution Artificial intelligence (AI) offers promising capabilities in combating phishing URLs by analyzing data faster and more accurately than traditional methods. AI techniques, particularly through machine learning, can rapidly and accurately assess the likelihood of URLs being phishing attempts. By training on extensive datasets that include both phishing and legitimate URLs. These models can learn patterns and characteristics that distinguish phishing URLs, such as unusual patterns in addresses, or URLs that use domain names similar to well-known domains. Aim and Objective The application aims to develop an advanced system to detect phishing URLs using artificial intelligence, as it will rely on machine learning techniques to analyze URLs and recognize patterns and details that indicate the presence of a phishing threat. The application will include a set of tools and features that help protect users from phishing URLs, including alerts to users when a potentially phishing URL is detected. Additionally, the application will offer educational content to raise awareness, equipping users with knowledge on how to spot phishing attempts. Reports and analyses of detected phishing activities will also be provided, offering insights into trends and threats. Document overview The rest of this report is organized as follows. Section 2 provides Domain Analysis of the project, Section 3 describes expected Risk/Constraints that we need to deal with, Section 4 presents the project work plan, Section 5 provides the Quality Assurance Plan, Section 6 provides functional requirements and non functional requirements, Section 7 describes problem complexity of the project, Section 8 lists 3 critical use cases that represent significant and central functionality of the final system, Section 9 represents the analysis class that identify all the boundary class, control class and entity class for all the use cases, Section 10 is about interaction diagram in terms of sequence between classes, Section 11 represents the design class, Section 12 describes System Architecture, Section 13 provides user interface mockups for critical scenarios, Section 14 defines the database schema and how data will be stored, Section 15 gives brief overview of special algorithms that were used, Section 16 represents the system deployment diagram, Section 17 specifies test scenarios, Section 18 is about Project Status that illustrates the current status of the project, the different issues we have worked on and the issues that we plan to work on in the subsequent iterations, Section 19 gives the conclusion of this report, Section 20 is about references that were used. 1 2. Domain Analysis In our project, the domain under study is “Phishing Detection”, a field rich in solutions and resources. And in this section, we present a set of existing tools and applications similar to ours in order to analyze the field and have a good understanding of it. We will be discussing each tool’s features, and comparing them. PhishTank The first phishing detection tool is PhishTank. Which is an anti-phishing website that is highly community driven. PhishTank gathers data about phishing sites, and also allows users to report suspected phishing URLs. This data is then verified and made public for awareness purposes, and used to specify if user submitted URLs are phishing or legitimate. PhishTank’s community driven nature is a big advantage of this platform, the many phishing URLs submitted enhance the database's accuracy and timeliness. The platform also offers open access to its database of phishing URLs, as well as integration possibilities for developers looking for phishing detection APIs. However, PhishTank also has some downsides, One being the lack of advanced features like AI detection, and rather relying on user submissions for phishing detection. Google’s safe browsing site status Google’s safe browsing site status is another example of a platform that provides phishing detection services. This tool depends on a technology developed by google that examines billions of URLs per day using machine learning, this technology helps google maintain a continuously up-to-date list of websites and their statuses. Google’s site status allows the user to paste a URL of a website to check its status, whether malicious or legitimate, this is determined by the up-to-date list that google keeps of websites and their statuses. One huge feature of this platform is that when dangerous phishing websites are detected google shows warnings on google search and web browsers. Easy DMARC’s Phishing URL checker Another commonly used tool is Easy DMARC’s Phishing URL checker, which is a website that scans pasted links by using an AI algorithm to determine whether it is safe or not. In addition to the AI algorithm, the tool also compares the phishing to a database of known phishing websites for detection. The website provides a report of each scanned URL containing its original URL, redirected URL, and URL status. The website also has a feature that shows the most recently scanned URLs by all users. Novoshield Novoshield is another solution that exists for phishing. This tool doesn’t specify the used phishing detection technology, but it promises to be a lightweight mobile browser extension that prevents phishing attacks. Users can download the Novoshield app and install the extension on their Safari browser and every time they come across a suspicious link a warning message is shown. On the app, users can view a report of the number of suspicious URLs they came across. One downside of this tool is that in order to use it you have to pay a subscription fee. Through our analysis, we noticed a need for a phishing detection system that is rich in features. Our easy-to-use mobile application will offer in-app AI phishing detection, as well as a browser extension and other features that will aid both general users and cybersecurity professionals. 2 Tool PhishTank Google safe Easy Novoshield Our App Browsing site DMARC’s status URL checker Feature Platform Website Website Website IOS Application IOS, Android and Safari Application and Extension chrome Extension AI for ✔ ✔ ✔ Not Specified Detection User-Submitted ✔ ✔ URL Reporting Stores Phishing ✔ ✔ ✔ ✔ URLs in a Database URL Reports ✔ ✔ ✔ ✔ ✔ Dashboard ✔ Phishing ✔ ✔ ✔ protection in browser Supports ✔ ✔ Arabic Language Supports ✔ ✔ ✔ ✔ ✔ English Language Free ✔ ✔ ✔ ✔ 3 3. Risk/Constraints Risk Title Description Likelihood Impact Priority Mitigation Responsible Strategy Parties Maintainability Continuous High High High Automate periodic Challenges updating of the AI updates and model and phishing retraining of AI URL database to models with fresh detect new threats. data. Use version Without consistent control and updates, the system structured code for may become easy future outdated. modifications. Skill Gaps The team may lack Medium Medium Medium Identify skill gaps specific skills, such early and provide as advanced AI training to bring modeling or in the required phishing detection expertise. Create a algorithms, which knowledge-sharin could slow down g environment progress. within the team. User Adoption Users may find the Medium High High Design an and Usability platform too intuitive user complicated or interface. provides cumbersome to step-by-step use, reducing its instructions on adoption. how to use the software. Collect user feedback to improve UX regularly. Accessibility The application Medium High High test across a Issues may not function variety of devices. well across Regularly review different devices UI/UX (desktop, mobile, accessibility tablets). features and usability testing for inclusivity. Scalability Issues The system may Medium High High Use cloud-based struggle to handle infrastructure that increasing numbers allows dynamic 4 of users and URLs scaling. Conduct for analysis, load testing to leading to slow identify potential performance or bottlenecks early. crashes. 4. Project Plan 5. Quality Assurance Plan Formal reviews As these documents originate from the early phases of the project, reviews are especially critical in the Software Quality Assurance (SQA) process. They help identify errors early, preventing these mistakes from being carried into later stages, where they become significantly harder, more complicated, and costly to correct. Before meeting with the project advisor for the formal review, team members are expected to go over the document carefully and provide their feedback. They should also take time to review and apply any feedback from the advisor to make sure the document is polished and meets the project’s requirements. ref Documentation Management Effective documentation management is key to making sure all project documents are created accurately, kept up-to-date, and easily accessible to team members and stakeholders throughout 5 the project lifecycle. Using tools like Google Docs helps with version control and makes collaboration smoother. It's also important to regularly review documents to confirm their accuracy and alignment with the latest project requirements, timelines, and progress. Team members should consistently review and update documents to keep everything current and to support overall project clarity and efficiency. ref Risk management Risk management is a useful technique in software development, used to facilitate the early detection of possible problems, increasing understanding before they turn into emergencies. This method increases the chances of finishing a project successfully while reducing the effects of inevitable risks. Effective risk management involves the identification, analysis, prioritization, and planning of responses to mitigate the likelihood or impact of risks. This ongoing process enables the project team to adjust to changes and retain a greater level of control over the development process.ref Testing Testing plays a crucial role in the Quality Assurance Plan. Testing is to identify as many errors as possible in the software, ensure that the software reaches an acceptable level of quality after error correction and retesting, and perform all required tests efficiently and effectively within schedule constraints. ref Training team members: To enhance our team's capabilities in Artificial Intelligence (AI) development, Each team member will complete online courses focused on Artificial Intelligence (AI) development to gain advanced expertise in the field. These courses are designed to cover a range of topics, from foundational concepts to advanced techniques, ensuring that everyone gains a robust understanding of the field. The courses will include key Topics such as machine learning, natural language processing, computer vision, and ethical considerations in AI. This training will enable our team to leverage AI effectively, driving innovation and improving the team's overall proficiency in AI development. The training is designed to be flexible and self-paced, allowing each individual to tailor their learning experience according to their current knowledge level and specific interests. To evaluate the effectiveness of our training initiative, we will implement metrics to measure individual progress and team improvement. Regular assessments and feedback sessions will help us gauge the impact of the courses on our work and identify areas for further development. Walkthroughs: A walkthrough is a form of software peer review that involves examining design documents, code, or other project artifacts. During a walkthrough, team members collaboratively review 6 each artifact, offering suggestions and feedback to identify potential issues and enhance the quality of the final system. This informal review process helps the team improve the accuracy, functionality, and overall quality of the software, ensuring it meets the desired standards and requirements before finalization. 6. Information Gathering 6.1 Questionnaire A key part of the development process involved collecting input from potential users through a questionnaire. The questionnaire is designed to identify the expectations, behavior, and preferences of users regarding phishing detection tools and related needs for education in online security. This survey comprises two parts: one for regular internet users and the other for cybersecurity experts. It was structured with both multiple-choice and open-ended questions to capture as broad a range of views as possible. We distributed the questionnaire to a group of casual internet users and cybersecurity experts, and received 52 responses. These responses provided the ground to establish some key system requirements and map out features that would satisfy the needs of the user base best. Question 6: Interest in a list of frequently reported phishing URLs The concept of a shared feature was very well-received. As many as 79.6% of participants would be interested in seeing, within the app, a list of the most reported phishing URLs that gets updated regularly, while 20.4% had no interest in it. This major interest reflects a clear demand for a shared, accessible resource to help users stay vigilant and avoid known threats. 7 Question 7: Preference for an awareness page on phishing and online safety All participants agreed that an awareness page within the app will be very beneficial, where there might be some suggestions on how to spot phishing attempts and how to be safe online. This overwhelming response pretty strongly suggests that users see real value in educational content. If implemented, this would save the end-user by upgrading their knowledge and developing the ability to handle online threats more confidently for safer user experiences 8 Question 8: Preferred type of awareness content When asked about the most useful type of content they would want to receive under an awareness program, users displayed a lot of inclination toward getting engaging and interactive formats. The results of their choices are as follows: 67.3% claimed that real examples of phishing attempts were the most useful. 59.2% chose educational video clips. 42.9% required interactive quizzes in order to test and improve their phishing detection ability. 30.6% preferred the presence of infographics or explanatory images to make phishing techniques clear. 9 24.5% chose to identify phishing attempts by using step-by-step instructions. These suggest that the user appreciates entertaining and informative formats. Real-life examples, videos, and interactive quizzes were favored most because variety seems to be one of the keys. A combination of video content, quizzes, and relatable examples can make the awareness section informative and user-friendly, which will facilitate the users in recognizing and responding to phishing threats in a more approachable and practical manner. 7. Requirements 7.1 Functional Requirements 6.1.1 User requirements SR1. The user shall be able to Sign-up using his/her first name, last name, email address, and password. SR2. The user shall be able to verify email during sign-up. SR3. The user shall be able to login using his/her email address and password. SR4. The user shall be able to reset his/her password. SR5. The user shall be able to view his/her account information (first name, last name, email address, and password). SR6. The user shall be able to edit his/her account information (first name, last 10 name, email address, and password). SR7. SR8. The user shall be able to delete his/her account. SR9. The user shall be able to logout of his/her account. SR10. The user shall be able to enter a URL for phishing detection. SR11. The user shall be able to view a result of the submitted URL (phishing or legitimate). SR12. The user shall be able to view a report of the phishing URL after submitting it, specifying the number of times it was reported, first and last reported date, type of phishing attack, and risk level. SR13. The user shall be able to save the phishing URL report in PDF format. SR14. The user shall be able to share the phishing URL report in PDF format. SR15. The user shall be able to report phishing URLs. SR16. The user shall be able to take a picture of a QR code in the app for phishing detection. SR17. The user shall be able to upload a photo of a QR code for phishing detection. SR18. The user shall be able to view a result of the submitted QR code (phishing or legitimate). SR19. The user shall be able to view a report of the phishing QR code after submitting it, specifying the number of times it was reported, first and last reported date, type of phishing attack, and risk level. SR20. The user shall be able to save the phishing QR code report in PDF format. SR21. The user shall be able to share the phishing QR code report in PDF format. SR22. The user shall be able to report phishing QR code. SR23. The user shall be able to view a dashboard that displays statistics related to his/her recently scanned and reported URLs. SR24. The user shall be able to view a PDF report summarizing his/her activity, which includes all scanned and reported URLs and QR codes, for a specified date range. SR25. The user shall be able to save the summary report in PDF format. SR26. The user shall be able to share the summary report in PDF format via external applications. SR27. The user shall be able to view a list of top-reported phishing URLs. SR28. The user shall be able to share a PDF file of the top-reported phishing URLs via external applications. SR29. The user shall be able to view a report of any of the top-reported. SR30. The user shall be able to share the report of the top-reported URL in PDF format via external applications. SR31. The user shall be able to save the report of the top-reported URL in PDF format. SR32. The user shall be able to watch phishing awareness videos. SR33. The user shall be able to view phishing awareness articles. SR34. The user shall be able to solve phishing awareness quizzes. SR35. The user shall be able to install the app’s chrome extension. 11 6.1.2 Admin requirements SR36. The admin shall be able to login using his/her email and password. SR37. The admin shall be able to reset his/her password. SR38. The admin shall be able to view his/her account information (first name, last name, email, and password). SR39. The admin shall be able to edit his/her account information ((first name, last name). SR40. The admin shall be able to delete his/her account. SR41. The admin shall be able to view usage data without tying it to users' identity. SR42. The admin shall be able to add awareness content for users. SR43. The admin shall be able to view awareness content. SR44. The admin shall be able to edit awareness content. SR45. The admin shall be able to delete awareness content. SR46. The admin shall be able to view the list of top-reported phishing URLs. SR47. The admin shall be able to edit the list of top-reported phishing URLs. SR48. The admin shall be able to delete any URLs from the list of top-reported phishing URLs. SR49. The admin shall be able to logout of his/her account. 6.1.3 System requirements SR50. The system shall be able to analyze extracted URLs if it is phishing or legitimate. SR51. The system shall send a verification email after the user registers, and automatically verify the email when the user confirms it. SR52. The system shall be able to display a result indicating whether the URL is phishing or not. SR53. The system shall be able to store phishing URLs in the database automatically. SR54. The system shall be able to generate detailed reports of phishing URLs detected. SR55. The system shall be able to display detailed reports of phishing URLs detected. SR56. The system shall be able to download reports in PDF formats. SR57. The system shall be able to update the list of top-reported phishing URLs. SR58. The system shall be able to download the list of top-reported phishing URLs in PDF formats. SR59. The system shall be able to detect phishing URLs in browser or in the page through the chrome extension. SR60. The system shall be able to detect phishing QR code in the page through the chrome extension SR61. The system shall be able to send a notification to the user updating him/her of the latest awareness content. 7.2 Non-Functional Requirements 6.2.1 Performance: 12 SR62. The system shall be able to detect phishing URLs and display results within 3 seconds of scanning or search action. SR63. The system shall be able to scale to support up to 5000 users simultaneously without experiencing performance degradation. 6.2.2 Reliability: SR64. The system shall maintain an availability of 99.9%, excluding scheduled maintenance window. SR65. The system shall be able to keep all users passwords encrypted. 6.2.3 Usability: SR66. The user shall be able to learn to use the system within 20 minutes of training. SR67. New users shall be able to complete the registration process within 1 minute. 8. Design Constraints SR68. The system shall work on both iOS and Android operating systems.(edition?) SR69. The system shall be able to integrate with ChatGPT. SR70. The system’s database shall be stored on firebase. 9. Problem Complexity Developing a mobile software that detects phishing URLs using AI and provides educational tools to increase users' awareness about online fraud has many complexities. Technical challenges include collecting comprehensive and diverse data that includes multiple types of phishing URLs. Accurately classifying links into legitimate and fraudulent links is a major challenge. Failure of the software to detect phishing URLs may result in the loss of users' personal data or money. On the other hand, failure to properly classify legitimate URLs may hinder users' access to services, causing them to lose confidence in the software and reduce their use or abandon it altogether. In addition, there are currently no global standards for classifying phishing URLs, which means developing an ad hoc solution without clear standards. It is expected that the age ranges and technical knowledge of users will vary, which poses a challenge in customizing educational tools for diverse levels of understanding and designing content to be engaging and practical. 13 10. System Use-Cases 14 Figure 1. System Name use cases Diagram 15 10.1 Use Case 1: View URL Result. Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: View result. Primary actor: User Secondary actor(s): ChatGPT Description: This use case describes how the user can view the detection results after entering a URL or Qr code for detection. Relationships: Includes: None. Extends: View phishing report. Pre-conditions: 1. The user shall be signed-in. 2. The user shall have entered a URL or Qr code for detection. Steps: Primary Actor System 1. User selects “show result”. 2. System displays whether the submitted URL or QR code is phishing or legitimate. Alternative and exceptional flows: 1.1 If the entered URL or QR code is invalid, the system shall display an error message and prompt the user to re-enter valid data. 2.1 If the URL or Qr code is detected as phishing, then the use case extends to: View phishing report. 2.2 If the system is unable to process the URL or QR code, the system shall display an error message indicating that the detection could not be completed and suggest trying again later. Post-conditions: Successful condition: The system shall display whether the entered URL or QR code is phishing or legitimate. Failure condition:The system fails to detect whether the entered URL or QR code is phishing or legitimate. 16 10.2 Use Case 2: View Phishing Report Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: View phishing report Primary actor: User Secondary actor(s): Description: This use case describes how the user can view the report of any URL or Qr code that is detected as phishing. Relationships: Includes: None. Extends: Share phishing report, Save phishing report. Pre-conditions: 1. The user shall be signed-in. 2. The URL or Qr code is detected to be phishing or the user selects a specific URL from the top-reported list. Steps: Primary Actor System 1. User selects “View phishing report ”. 2. The system displays the PDF phishing report specifying the number of times it was reported, first and last reported date, type of phishing attack, and risk level. 3.use case extends to: Share phishing report, Save phishing report. Alternative and exceptional flows: 2.1 If the report generation fails, the system shall display an error message indicating that the report could not be generated and suggest trying again later. Post-conditions: Successful condition: The system shall display the phishing report. Failure condition::The system fails to display the phishing report. 17 10.3 Use Case 3: View Top-Reported URLs List Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: View top-reported URLs list. Primary actor: User Secondary actor(s): Description:This use case describes how the user can view the top-reported URLs list. Relationships: Includes: None. Extends: Select specific top-reported URL, share the top-reported URLs. Pre-conditions: 1. The user shall be signed-in. Steps: Primary Actor System 1. The user selects the top-reported URLs list section. 2. The system displays the top-reported URLs list page. Alternative and exceptional flows: 2.1 If the system cannot retrieve the top-reported URLs , an error message is displayed. Post-conditions: None. Successful condition: The system shall display the top-reported URLs list page. Failure condition:The system fails to display the top-reported URLs list page. 18 10.4 Use Case 4: Take a picture of QR code Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: Take a picture of QR code. Primary actor: User Secondary actor(s): Description:This use case describes how the user can take a picture of a QR code to check if it leads to a Phishing URL. Relationships: Includes: View result. Extends: None. Pre-conditions: 1. The user shall be signed-in. Steps: Primary Actor System 1. The user clicks on the QR code icon in the home page. 2. The system displays a prompt to either choose “Camera” to take a photo or “Gallery” to upload an existing photo of a QR code. 3. The user selects Camera. 4. The system opens the camera view and prompts the user to align the QR code within the frame. 5. The user taps the capture button. 6. The system captures the image. 7. The system processes the image and extracts the URL from the QR code. 8. The system displays the extracted URL. 9. Use case View result is performed. Alternative and exceptional flows: 7.1 If the system fails to detect a QR code in the captured image, an error message is displayed where the user is prompted to try again. 7.2 If the system fails to extract a URL from the QR code, an error message is displayed, where the user is prompted to try again. Post-conditions: Successful condition: The user has successfully captured an image of the QR code and viewed the extracted URL. Failure condition: The user was unable to capture an image of the QR code, and view its extracted URL. 19 10.5 Use Case 5: Upload a picture of QR code Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: Upload a picture of QR code. Primary actor: User Secondary actor(s): Description:This use case describes how the user can upload a picture of a QR code to check if it leads to a Phishing URL. Relationships: Includes: View result. Extends: None. Pre-conditions: 1. The user shall be signed-in. Steps: Primary Actor System 1. The user clicks on the QR code icon in the home page. 2. The system displays a prompt to either choose “Camera” to take a photo or “Gallery” to upload an existing photo of a QR code. 3. The user selects Gallery. 4. The system opens the device’s gallery and prompts the user to align the QR code within the frame. 5. The user selects a photo. 6. The system processes the image and extracts the URL from the QR code. 7. The system displays the extracted URL. 8. Use case View result is performed. Alternative and exceptional flows: 7.1 If the system fails to detect a QR code in the uploaded image, an error message is displayed where the user is prompted to try again. 7.2 If the system fails to extract a URL from the QR code, an error message is displayed, where the user is prompted to try again. Post-conditions: Successful condition: The user has successfully uploaded an image of the QR code and viewed the extracted URL. 20 Failure condition: The user was unable to upload an image of the QR code, and view its extracted URL. 10.6 Use Case 6: Save the phishing URL report in PDF format Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: Save the phishing URL report in PDF format. Primary actor: User Secondary actor(s): Description:This use case describes how the user can save the phishing URL report in PDF format. Relationships: Includes: View phishing report. Extends: None. Pre-conditions: 1.the user shall view the report of the phishing URL. Steps: Primary Actor System 1. The User selects “Save phishing report ”. 2. The System genrate phishing URL report in PDF format. 3. The User Save the phishing report in PDF format. Alternative and exceptional flows: 2.1 If the system fails to generate phishing URL report in PDF format, an error message is displayed where the user is prompted to try again. Post-conditions: None. Successful condition: The user has successfully saved the phishing URL report in PDF format. Failure condition: The user was unable to save the phishing URL report in PDF format. 21 10.7 Use Case 7: View Summary report Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: View Summary report. Primary actor: User Secondary actor(s): Description:This use case describes how the user can view a PDF report summarizing their activity, including all scanned and reported URLs and QR codes, for a specified date range. Relationships: Includes: None. Extends: Save summary report, share summary report. Pre-conditions: 1. The user shall be signed-in. 2. The user shall have activity records (scanned or reported URLs/QR codes). Steps: Primary Actor System 1. User navigates to the dashboard page. 2. System displays all reports. 3. User enters the desired start and end dates. 4. System filters and displays all reports within the selected date range. 5. User selects "Generate Summary Report." 6. System displays the PDF report for the user to view. Alternative and exceptional flows: 4.1 If the entered start date is after the end date, the system displays an error message indicating that the date range is invalid and prompts the user to correct it. 4.2 If no activity exists for the selected date range, the system displays a message stating that no data is available for the selected period. 6.1 If the report generation fails, the system displays an error message indicating that the report could not be generated and suggests trying again later. Post-conditions: Successful condition: The system shall display a PDF report summarizing all scanned and reported URLs and QR codes for the specified date range. Failure condition: The system fails to generate the PDF report due to an error or lack of data. 22 10.8 Use Case 8: Edit Top-Reported URLs List Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: Edit top-reported URLs list. Primary actor: Admin Secondary actor(s): Description:This use case describes how the Admin can Editthe top-reported URLs list. Relationships: Includes: None. Extends: None. Pre-conditions: 1. The admin shall be signed-in. 2. The admin shall have viewed the top-reported URLs list. Steps: Primary Actor System 1. The admin clicks on the edit button. 2. The system displays the edit page for top-reported URLs list. 3. The admin selects specific URL 4. The system displays the URL information. 5. The admin edit URL information. 6. The system saves changes to URL information. Alternative and exceptional flows: 6.1 If the system cannot save changes to URL information , an error message is displayed. Post-conditions: Successful condition: The system shall save changes to URL information. Failure condition:The system fails to save changes to URL information. 23 10.9 Use Case 9: Add awareness content Use Case Description System: Ai-Enhanced Phishing Detector. Use Case name: Add awareness content Primary actor: Admin Secondary actor(s): Description:This use case describes how the Admin can add awareness content. Relationships: Includes: None. Extends: None. Pre-conditions: 1. The admin shall be signed-in. Steps: Primary Actor System 1. The admin clicks on “Add awareness content”. 2. The system displays awareness content form. 3. The admin fills out the form and submits it. 4. The system adds awareness content. Alternative and exceptional flows: 4.1 If the system cannot add awareness content , an error message is displayed. Post-conditions: Successful condition: The system shall add awareness content. Failure condition:The system fails to add awareness content. 11. Analysis Class Here you identify all the boundary class, control class and entity class for all the use cases. You should also identify the attributes and methods in the classes. 24 Figure 2. Successful order placement 12. Interaction Diagram 25 12.1.1 View URL result use case Figure 3. Successful order placement 12.1.2 View phishing report use case 26 Figure 4. Successful face recognition log in 27 12.1.3 View phishing report use case Figure 5. Successful face recognition log in 28 12.1.4 View awareness content use case Figure 6. Successful face recognition log in 29 12.1.5 View summary report use case Figure 6. Successful face recognition log in 30 13. Design Class https://lucid.app/lucidchart/0ddfbcb7-7bdf-4a56-8293-981b0015b0e0/edit?viewp ort_loc=-6675%2C-1314%2C4020%2C2352%2C0_0&invitationId=inv_59f404b 9-90f7-4789-a330-134ee26f9a4e 31 14. System Architecture https://lucid.app/lucidchart/c43f24db-cc18-4fc6-a809-df286ff64828/edit?viewport _loc=-309%2C819%2C4972%2C2909%2C0_0&invitationId=inv_0ca52355-1060 -4a0f-a642-45e17126b5a9 Choosing the Model-View-Controller (MVC) architecture for an AI-Enhanced Phishing URL Detection System can bring significant benefits in terms of maintainability, scalability, and clear separation of concerns. Clear Separation of Concerns: Each component has a specific responsibility, which helps manage complex tasks and apply updates without interfering with other parts of the system. For instance, updating the phishing detection model (Model) does not affect the View, where results are displayed to the user. Easier Testing and Maintenance: MVC’s structure allows individual testing for each layer. The Model can be tested independently with various phishing URL datasets, the View can be tested for proper UI functionality, and the Controller can be tested to handle request flow and error scenarios. Enhanced Modularity: MVC makes it easier to integrate additional features, such as new detection methods, without impacting the existing system structure. For example, updating phishing awareness resources only requires adjustments in the Model or View, not the entire system. The MVC architecture is ideal for the AI-Enhanced Phishing URL Detection System because it allows clear separation of responsibilities, maintains flexibility for future updates, and enhances scalability. This structure will support efficient handling of phishing data, seamless user interaction, and future-proofing against emerging threats. 32 33 15. User Interface Mockup Sign up and Sign in pages 34 35 User Homepage (scan URL) 36 User Dashboard page User Top Reported URLs pages 37 User Awareness Page 38 User Account settings 39 Admin Top Reported page Admin Awareness 40 Admin Account settings 41 42 16. Database Schema 43 17. Algorithms //1 Phishing attacks are considered a grave threat in today’s digital world, using deceptive techniques to steal sensitive information like financial details, and personal data. Our proposed solution is to combat this using ChatGPT, a tool powered by OpenAI’s GPT (Generative Pre-trained Transformer) architecture, which will be employed via an API as an intelligent tool to identify and differentiate phishing URLs from legitimate ones. GPT is a deep learning model that was trained using large amounts of text data, which enables it to understand human-like text and recognise patterns and contextual language relationships. Through the API, ChatGPT can be integrated into our application, and utilized to analyze linguistic patterns in phishing URLs, such as altered spellings, misleading subdomains, and suspicious keywords. ChatGPT adapts dynamically to evolving phishing tactics, unlike traditional detection systems that rely on static rules. In this section, we will highlight how the model will be trained, including the data preparation process, how it identifies key detection features, as well as the process for testing and fine-tuning the model to enhance its accuracy. //2 We will train the model with a labeled dataset that contains both phishing and legitimate URLs to help to get ChatGPT prepared to recognize phishing URLs. For ChatGPT to effectively learn and differentiate between malicious and legitimate URLs, a balanced dataset is essential. We used resources such as OpenPhish, PhishTank, and Hugging Face datasets to gather both phishing and legitimate URLs. The data is arranged in two columns: one for the URL and another identifying it as "legitimate " or "phishing." It is critical to use a diverse and representative dataset that captures various phishing tactics and legitimate URL structures to help ChatGPT accurately identify subtle differences between malicious and safe URLs. Phishers employ many strategies to make harmful URLs appear legitimate, which can trick users into unsafe interactions. Without this variety, ChatGPT’s ability to understand and detect common phishing tactics would be limited. For example, phishers often use slight misspellings or minor character changes in URLs, like “g00gle.com” instead of “google.com,” and by training on these variations, ChatGPT learns to recognize such suspicious alterations. Attackers may also use homograph attacks, substituting characters with lookalikes from other languages, such as the Cyrillic “а” in place of the English “a.” Including these examples in the training data allows ChatGPT to detect these subtle tricks as well. Also, without a sufficient number of legitimate sites in the dataset, ChatGPT could become overly cautious, marking genuine sites as suspicious. A balanced dataset lets ChatGPT recognize standard patterns, identify trusted domains, and effectively distinguish between phishing and legitimate URLs, creating a more reliable and accurate detection tool. //3 To detect phishing URLs, ChatGPT will analyze key features within each URL by examining aspects like unusual domain structures, unexpected subdomains or suspicious Words: 44 Unusual Domain Names: Most phishing URLs try to create confusion among users by changing the tiniest bit of any website name. They would replace "amazon.com" with "amaz0n.com," adding a "0" instead of an "o". These minor changes will be detected, and ChatGPT will send alerts about them being unsafe. Unexpected subdomains: the URLs may contain spurious subdomains, like "login.bank-secure.com." ChatGPT finds URLs with an abnormal or irrelevant subdomain and identifies trusted subdomain patterns. Suspicious Words: Displaying words like "login," "verify," or "secure" within a phishing URL entices the visitor to input personal information. In such instances, something like "secure-account.com" might appear in the URL even though the URL is not actually secure. Such words can be identified by ChatGPT, which raises an alarm whenever they occur in suspicious places. 4// Training ChatGPT to Identify Phishing URLs and Provide Feedback ChatGPT can be enhanced to not only classify URLs as phishing or legitimate but also to provide informative feedback and warnings for users: - Contextual Analysis: Train the model to detect suspicious patterns in URLs, such as: - Misspelled domains (e.g., "paypa1.com" instead of "paypal.com"). - Suspicious keywords like "secure" or "login" in odd contexts. - Overly complex or hidden links that redirect users unexpectedly. 5// The testing phase validates ChatGPT's ability to classify URLs accurately: 1. Test Dataset:Use a labeled dataset containing phishing and legitimate URLs, sourced from real-world examples. 2. Performance Metrics: Evaluate: - Accuracy: The percentage of correctly identified URLs. - False Positives:Legitimate URLs flagged as phishing. - False Negatives: Phishing URLs identified as legitimate. Feedback Loops for Continuous Improvement Feedback loops help refine ChatGPT's phishing detection capabilities: 1. Error Analysis: Review misclassifications to understand why they occurred. 2. Incorporate Feedback:Retrain ChatGPT with updated data, focusing on identified gaps. 3. Iterative Refinement: Regular testing and re-training cycles improve accuracy over time. Example: - If the model struggles with shortened URLs (e.g., "bit.ly/secure-login"), include more such examples in its training data. 45 6// Strategy for Updating ChatGPT Phishing techniques evolve, requiring ChatGPT to adapt constantly: 1. Data Collection: Gather new phishing and legitimate URLs regularly from platforms like OpenPhish or PhishTank. 2. Pattern Recognition: Train ChatGPT to detect emerging phishing trends, such as: - Redirects for URLs with unusual structures. 3. User Interaction Monitoring: Analyze real-world user interactions to identify cases where ChatGPT's classifications need improvement. Retraining ChatGPT Periodic retraining ensures the model stays effective against new threats: 1. Fresh Datasets:Regularly update training data with the latest phishing tactics. 2. Fine-Tuning: Use focused retraining sessions to adapt ChatGPT without disrupting its overall performance. 3. Post-Update Testing: Validate the updated model to ensure it handles new phishing techniques effectively before deployment. 18. Expected Deployment Global Overview Figure []. System deployment diagram Mobile Device and Chrome extensions: End-users interact with the phishing detection system through these devices. Firewall: Protects the server layer from unauthorized access through the public internet. 46 Internet: The public network that connects the mobile device and chrome extensions to backend server Application server: The main core backend, where business logic for URL and QR code phishing detection is handled. This server is responsible for processing requests, running the detection model, and generating reports. Firebase server: Stores data such as user information, phishing URLs, reports, and activity logs and manages user login and credentials for secure access , also sends notifications to users. 19. Test Scenario 47 Test case ID 1 Test Title User registration Test summary/ description Check if a user can successfully register with valid inputs. Precondition The user is on the registration page. Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "Sarah" Last name = "Ahmad" Email = “[email protected]” Password=”Sarah@12345” Expected output The user is successfully registered Test Status Pass Test case ID 2 48 Test Title User registration Test summary/ description Check if a user can register with invalid first name. Precondition The user is on the registration page. Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "S" Last name = "Ahmad" Email = “[email protected]” Password=”Sarah@12345” Confirmed password=”Sarah@12345” Expected output The user is not successfully registered “First name should have at least two letters” Test Status Pass Test case ID 3 Test Title User registration Test summary/ description Check if a user can register with invalid last name. Precondition The user is on the registration page. Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "Sarah" Last name = "A" Email = “[email protected]” 49 Password=”Sarah@12345” Confirmed password=”Sarah@12345” Expected output The user is not successfully registered “Last name should have at least two letters” Test Status Pass Test case ID 4 Test Title User registration Test summary/ description Check if a user can register with invalid email. Precondition The user is on the registration page. Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "Sarah" Last name = "Ahmad" Email = “sarahgmail.com” Password=”Sarah@12345” Confirmed password=”Sarah@12345” Expected output The user is not successfully registered “Please enter a valid email address” Test Status Pass Test case ID 5 Test Title User registration Test summary/ description Check if a user can register with invalid password. Precondition The user is on the registration page. 50 Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "Sarah" Last name = "Ahmad" Email = “[email protected]” Password=”sarah12345” Confirmed password=”sarah12345” Expected output The user is not successfully registered “Please enter a valid password (rules)” Test Status Pass Test case ID 6 Test Title User registration Test summary/ description Check if a user can register with incompatible passwords. Precondition The user is on the registration page. Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Register" button. Test data First name = "Sarah" Last name = "Ahmad" Email = “[email protected]” Password=”Sarah@@12345” Confirmed password=”Sarah@12345” Expected output The user is not successfully registered “Passwords don’t match” Test Status Pass 51 Test case ID 7 Test Title User registration Test summary/ description Check if a user can register with an empty field. Precondition The user is on the registration page. Test steps 1. User leaves first name empty 2. User leaves last name empty 3. User leaves email empty 4. User leaves password empty 5. User leaves confirms password empty 6. User clicks on the "Register" button. Test data First name = "" Last name = "" Email = “” Password=”” Confirmed password=”” Expected output The user is not successfully registered “This field cannot be empty” Test Status Pass Test case ID 8 Test Title Sign In Test summary/ description Check if a user can successfully sign in with valid inputs. Precondition The user has registered and verified their email and on the sign in page. Test steps 1. User enters email 2. User enters password 3. User clicks on the “Sign in” button. Test data Email = “[email protected]” Password=”Sarah@12345” 52 Expected output The user is logged in successfully Test Status Pass Test case ID 9 Test Title Sign In Test summary/ description Check if a user can sign in with an email that does not exist. Precondition The user has registered and verified their email and on the sign in page. Test steps 4. User enters email 5. User enters password 6. User clicks on the “Sign in” button. Test data Email = “[email protected]” Password=”Sarah@12345” Expected output The user is not successfully signed in “Incorrect email or password Test Status Pass Test case ID 10 Test Title Sign In Test summary/ description Check if a user can sign in with invalid password. Precondition The user has registered and verified their email and on the sign in page. Test steps 7. User enters email 8. User enters password 9. User clicks on the “Sign in” button. Test data Email = “[email protected]” Password=”Abcd@12345” 53 Expected output The user is not successfully signed in “Incorrect email or password” Test Status Pass Test case ID 11 Test Title Sign In Test summary/ description Check if a user can sign in with invalid email. Precondition The user has registered and verified their email and on the sign in page. Test steps 10. User enters email 11. User enters password 12. User clicks on the “Sign in” button. Test data Email = “sarahgmail.com” Password=”Sarah@12345” Expected output The user is not successfully signed in “Please enter a valid email address” Test Status Pass Test case ID 12 Test Title Sign In 54 Test summary/ description Check if a user can sign in with an empty field. Precondition The user has registered and verified their email and on the sign in page. Test steps 1. User leaves email empty 2. User leaves password empty 3. User clicks on the “Sign in” button. Test data Email = “” Password=”” Expected output The user is not successfully signed in “This field cannot be empty” Test Status Pass Test case ID Test Title Forgot Password 55 Test summary/ description Verify that the user can request a password reset email using the "Forgot Password" feature Precondition The user has registered and verified their email and on the sign in page. Test steps 1. User clicks "Forgot Password" on the login page. 2. The "Reset Your Password" screen appears, prompting the user to enter their email address. 3. User enters a valid registered email address in the email input field. 4. User clicks the "Next" button. 5. User sets a new password from the link sent in the email. 6. User clicks "Back to Login”. 7. User enters their email address and the newly set password, and clicks the "Sign In" button. Test data Email = “[email protected]” Password=”NewPassword12345” Expected output The user is logged in successfully Test Status Pass Test case ID Test Title Forgot Password 56 Test summary/ description Verify that the system handles unregistered email inputs during the "Forgot Password" process and allows the user to re-enter the correct email address to proceed. Precondition The user has registered and verified their email and on the sign in page. Test steps 1. User clicks "Forgot Password" on the login page. 2. The "Reset Your Password" screen appears, prompting the user to enter their email address. 3. User enters an unregistered email address in the email input field. 4. User clicks the "Next" button. 5. User doesn’t receive email and clicks "Re-enter your email address”. 6. The input field is cleared, and the user enters a valid registered email address. 7. User clicks the "Next" button. 8. User sets a new password from the link sent in the email. 9. User clicks "Back to Login”. 10. User enters their email address and the newly set password, and clicks the "Sign In" button. Test data Email = “[email protected]” Email = “[email protected]” Password=”NewPassword12345” Expected output The user is logged in successfully Test Status Pass Test case ID 57 Test Title Forgot Password Test summary/ description Verify that the system handles invalid email inputs during the "Forgot Password" process and allows the user to re-enter a valid email address to proceed. Precondition The user has registered and verified their email and on the sign in page. Test steps 1. User clicks "Forgot Password" on the login page. 2. The "Reset Your Password" screen appears, prompting the user to enter their email address. 3. User enters an invalid email address in the email input field. 4. User clicks the "Next" button. Test data Email = “sarah@@gmail.com” Email = “[email protected]” Password=”NewPassword12345” Expected output An error message appears, “Please enter a valid email address”. Test Status Pass 58 Test case ID 13 Test Title Check a URL Test summary/ description This test case verifies the functionality that allows users to check a URL they suspect is phishing. Precondition User is logged in and on the Home page. Test steps 1. Enter a valid phishing URL in the URL submission field. 2. Click on the "Submit" button. Test data URL=”http://procontrolbit.top/?u=a44354&” Expected output URL result is displayed, URL is flagged as phishing. Test Status Pass Test case ID 14 Test Title Check a URL Test summary/ description This test case verifies the functionality that allows users to check a URL they suspect is phishing. Precondition User is logged in and on the Home page. Test steps 1. Enter a valid clean URL in the URL submission field. 2. Click on the "Submit" button. Test data URL=”https://github.com/” Expected output URL result is displayed, URL is flagged as clean. Test Status Pass 59 Test case ID 15 Test Title Check a URL Test summary/ description This test case verifies the functionality that allows users to check a URL they suspect is phishing. Precondition User is logged in and on the Home page. Test steps 1. Enter an invalid URL in the URL submission field. 2. Click on the "Submit" button. Test data URL=”https://githu@com” Expected output Error message is shown “Please enter a valid URL” Test Status Pass Test case ID 16 Test Title Check a URL Test summary/ description This test case verifies the functionality that allows users to check a URL they suspect is phishing. Precondition User is logged in and on the Home page. Test steps 1. Leave the URL submission field empty. 2. Click on the "Submit" button. Test data URL=”” Expected output Error message is shown “This field cannot be empty.” Test Status Pass 60 Test case ID 17 Test Title Check a QR code by taking a photo of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by taking a photo of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Camera” from the bottom menu and takes a picture of valid phishing QR code. 3. Click on the "Submit" button. Test data Expected output Qr code result is displayed, URL is flagged as phishing. Test Status Pass 61 Test case ID 18 Test Title Check a QR code by taking a photo of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by taking a photo of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Camera” from the bottom menu and takes a picture of a valid clean QR code. 3. Click on the "Submit" button. Test data Expected output Qr code result is displayed, URL is flagged as clean. Test Status Pass Test case ID 19 Test Title Check a QR code by taking a photo of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by taking a photo of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Camera” from the bottom menu and takes a picture of an invalid QR code. 3. Click on the "Submit" button. Test data Expected output Error message is shown “Please submit a valid QR code” Test Status Pass 62 Test case ID 20 Test Title Check a QR code by taking a photo of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by taking a photo of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. Clicks on the "Submit" button without providing any QR code. Test data QR Code:(no QR code submitted) Expected output Error message is shown “Please submit a valid QR code” Test Status Pass 63 Test case ID Test Title Check a QR code by uploading a picture of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by uploading a picture of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Gallery” from the bottom menu and selects a picture of valid phishing QR code. 3. Click on the "Submit" button. Test data Expected output Qr code result is displayed, URL is flagged as phishing. Test Status Pass 64 Test case ID Test Title Check a QR code by uploading a picture of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by uploading a picture of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Gallery” from the bottom menu and selects a picture of a valid clean QR code. 3. Click on the "Submit" button. Test data Expected output Qr code result is displayed, URL is flagged as clean. Test Status Pass 65 Test case ID Test Title Check a QR code by uploading a picture of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by uploading a picture of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Gallery” from the bottom menu and selects a picture of an invalid QR code. 3. Click on the "Submit" button. Test data Expected output Error message is shown “Please submit a valid QR code” Test Status Pass 66 Test case ID Test Title Check a QR code by uploading a picture of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by uploading a picture of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. User selects “Gallery” from the bottom menu and selects a file that doesn’t match the allowed picture formats. 3. Click on the "Submit" button Test data Invalid file type… Expected output Error message is shown “File type not supported” Test Status Pass 67 Test case ID Test Title Check a QR code by uploading a picture of it. Test summary/ description This test case verifies the functionality that allows users to check a QR code they suspect is phishing by uploading a picture of it. Precondition User is logged in and on the Home page. Test steps 1. User Clicks the QR code icon 2. Clicks on the "Submit" button without providing any QR code. Test data QR Code:(no QR code submitted) Expected output Error message is shown “Please submit a valid QR code” Test Status Pass Test case ID 21 Test Title Edit Account Information Test summary/ description This test case verifies the functionality that allows users to edit their account information Precondition User is logged in and on the account settings page. Test steps 1. User enters new first name 2. User enters new last name 3. User enters new email 4. User enters new password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "Norah" Last name = "Omar" Email = “[email protected]” Password=”Norah@12345” 68 Confirmed password=”Norah@12345” Expected output The user’s account information is successfully updated Test Status Pass Test case ID 21 Test Title Edit Account Information Test summary/ description This test case verifies the functionality that allows users to edit their account information Precondition User is logged in and on the account settings page. Test steps 7. User enters new first name 8. User enters new last name 9. User enters new email 10. User enters new password 11. User confirms password 12. User clicks on the "Save Changes" button. Test data First name = "Norah" Last name = "Omar" Email = “[email protected]” Password=”Norah@12345” Confirmed password=”Norah@12345” Expected output The user’s account information is successfully updated Test Status Pass Test case ID 22 Test Title Edit Account Information 69 Test summary/ description Check if a user can edit their account information with invalid first name. Precondition User is logged in and on the account settings page Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "N" Last name = "Omar" Email = “[email protected]” Password=”Norah@12345” Confirmed password=”Norah@12345” Expected output The user is not successfully edited account “First name should have at least two letters” Test Status Pass Test case ID 23 Test Title Edit Account Information Test summary/ description Check if a user can edit their account information with invalid last name. Precondition User is logged in and on the account settings page Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "Norah" 70 Last name = "O" Email = “[email protected]” Password=”Norah@12345” Confirmed password=”Norah@12345” Expected output The user is not successfully edited account “Last name should have at least two letters” Test Status Pass Test case ID 24 Test Title Edit Account Information Test summary/ description Check if a user can edit their account information with invalid email. Precondition User is logged in and on the account settings page Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "Norah" Last name = "Omar" Email = “norahgmail.com” Password=”Norah@12345” Confirmed password=”Norah@12345” Expected output The user is not successfully edited account “Please enter a valid email address” Test Status Pass Test case ID 25 Test Title Edit Account Information 71 Test summary/ description Check if a user can edit their account information with invalid password. Precondition User is logged in and on the account settings page Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "Norah" Last name = "Omar" Email = “[email protected]” Password=”Norah12345” Confirmed password=”Norah12345” Expected output The user is not successfully edited account “Please enter a valid password (rules)” Test Status Pass Test case ID 26 Test Title Edit Account Information Test summary/ description Check if a user can edit their account information with incompatible passwords. Precondition User is logged in and on the account settings page Test steps 1. User enters first name 2. User enters last name 3. User enters email 4. User enters password 5. User confirms password 6. User clicks on the "Save Changes" button. Test data First name = "Norah" 72 Last name = "Omar" Email = “[email protected]” Password=”Norah@@12345” Confirmed password=”Norah@12345” Expected output The user is not successfully edited account “Passwords don’t match” Test Status Pass Test case ID 27 Test Title Edit Account Information Test summary/ description Check if a user can edit their account information with an empty field. Precondition User is logged in and on the account settings page Test steps 1. User leaves first name empty 2. User leaves last name empty 3. User leaves email empty 4. User leaves password empty 5. User leaves confirms password empty 6. User clicks on the "Save Changes" button. Test data First name = "" Last name = "" Email = “” Password=”” Confirmed password=”” Expected output The user is not successfully edited account “This field cannot be empty” Test Status Pass 73 Test case ID Test Title Download Chrome Extension Test summary/ description Verify that the user can download the Chrome extension from the provided button and is redirected to the correct download page. Precondition The user is logged in and is on the home page. Test steps 1. User clicks download. 2. User is redirected to the Chrome Web Store. 3. User clicks "Add to Chrome". Test data None required for this test. Expected output The extension installs successf
