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Phishing websites Detection Using Machine Learning with urls analysis By: Mohamed Nabil Salwa Youssef Attia Introduction Phishing attack is a type of social engineering attack in which the attacker makes the target reveal their confidential information such as credentials, cred...

Phishing websites Detection Using Machine Learning with urls analysis By: Mohamed Nabil Salwa Youssef Attia Introduction Phishing attack is a type of social engineering attack in which the attacker makes the target reveal their confidential information such as credentials, credit card number. Phishing is one approach to stealing personal information through the Internet. Preventing phishing is a demand in the present situation In phishing, attackers sendPhishing a deceptive attack message to trap online users by revealing sensitive information on phishing websites. Social Malware based engineering Cont’d Phishing can be conducted through emails, calls, messages and websites What is APWG(anti phishing working Group) UNIFYING THE GLOBAL RESPONSE TO CYBERCRIME THROUGH DATA EXCHANGE, RESEARCH AND PUBLIC AWARENESS APWG, a not-for-profit industry association focused on eliminating the identity theft and frauds that result from the growing problem of phishing, crimeware, and e-mail spoofing, offers membership to financial institutions, online retailers, ISPs, solutions providers, the law enforcement community, government agencies, multilateral treaty organizations, and NGOs. Anti-Phishing Working Group Detected 260,642 attacks the highest monthly attack count in the APWG history, according to 2021 Q3 report.  Phishing Techniques An email is sent to billions of individuals requesting that they fill Email/Spam out sensitive information. Attackers target a specific organization or individual to snitch Spear Phishing personal information from the target. Session Criminals snitch user’s details through the web session control mechanism. Criminals use sniffer, and it collects the transmitted data Hijacking between the communication devices illegally. The phisher changes some content parts on the dedicated Content website page in content injection. It was done to deceive the Injection user into stealing confidential information Hackers are located between phishing and reliable websites. Web-Based Phisher stalks details during the transactions between the user Delivery and the legit website. Phishers snitch personal information without the user's knowledge.  Phishing Some phishing baits use search engines to route consumers to product websites. It provides low cost services or products. through Search Phishing sites capture user information when they attempt to Engines purchase things using debit or credit card information. A phisher sends a malicious website link to the users in link Link manipulation. When the user opens that link, it takes to a Manipulation phishing website instead of the website mentioned. Vishing Phone phishing is another name for voice phishing. The phisher (Voice Phishing) contacts victims with a false caller ID to steal critical information. Keyloggers use spying tools to detect the inputs given from the Keyloggers keyboard.  Smishing A Text SMS is sent to the user’s mobile with the phishing link (SMS Phishing) Trojan A Trojan is a malicious software that can control our system. Malware will be sent to the users by email or Text SMS. The Malware malware starts downloading into our system when we click on the link. Malvertising refers to deceptive advertising. It includes active scripts Malvertising that are intended to download malware Malware gets installed by using social engineering. The malware Ransomware prevents access to the implanted device or files until a ransom is paid. Main Contribution The implementation of a phishing website detection system based on analyzing web page URLs and extraction features from the URLs to evaluate if the website is phishing or not.  Manipulation techniques in urls 1) Typosquatting involves registering domain names that are misspellings of popular websites (for example, goggle.com instead of google.com). If you make a typo while entering a URL, you can end up on a fake site like this without even realizing it. 2) Link masking :is used by legitimate website owners who want to hide long and complex URLs by masking them under simple and short phrases. Unfortunately,  Cont’d 5) Doppelganger domain is similar to typosquatting, but instead of changing a character, it omits one, and facebook.com becomes facebok.com 6) Redirects are websites that have only one function — redirecting visitors to another page. 7) URL shorteners are often used by individual users and EXAMPLE Legitimate: www.bankofamerica.com Phishing: www.bankofameica.com SYSTEM ARCHITECTURE Cont’d Dataset Description: Datasets were divided into two categories Legitimate websites which are acquired from the Alexa database, contains 3000 legitimate URLs, and Phishing websites which are acquired from PhishTank, contains 3000 phishing URLs. Data Preprocessing: checks for the duplicate URLs and null values present in the dataset. Feature Extraction: URLs have specific patterns and characteristics that can be referred to as features Machine Learning Models In the system implementation, eight machine learning algorithms were designed in the study. These are Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), XG Boost, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), ADA Boost, and Multilayer Perceptron. EXPERIMENTAL RESULTS CONCLUSION This study uses URLs as a dataset to detect phishing websites. From this dataset, ten features were extracted and utilized to identify if the website was phishing or not. Eight machine learning models are designed: Random Forest, Decision Tree, Logistic Regression, Support Vector Machines, XG Boost, K-Nearest Neighbor, ADA Boost, and Multilayer Perceptron. Results Compared using Accuracy, Precision, Recall, and F1 score. The Multilayer perceptron algorithm has the highest accuracy of 85.41% and a better F1 score of 85.17% FUTURE WORK Further studies can include increasing the data size, extracting more features, selecting the optimal features, and applying various deep learning techniques. Our Work Using Larger dataset consists of 549346 rows Apply Deep Learning Models like CNN & LSTM-CNN References Phishing Websites Detection using Machine Learning with URL Analysis (2023) Any Questions? Hacking: Concepts and Types And How to Protect Yourself Bassem Khaled Tasneem Salaheldin Under supervision of Prof. Dr. Ibrahim gomaa  Introduction In today's interconnected world, where technology permeates every aspect of our lives, the risk of cyber threats looms larger than ever. Hacking, once considered the realm of a few tech- savvy individuals, has evolved into a sophisticated and widespread phenomenon with potentially devastating consequences. From personal data breaches to large-scale cyberattacks on governments and corporations, the threat landscape is constantly evolving, posing significant challenges to individuals, businesses, and governments worldwide. Introduction The term "hacking" has become both ubiquitous and enigmatic. Hacking is the art of exploration, a digital frontier where individuals push the boundaries of technology, often blurring the lines between legality, ethics, and curiosity. Hacking encompasses a broad spectrum of activities, from the noble pursuit of bolstering cybersecurity defenses to the nefarious exploits of cybercriminals. While media portrayals often sensationalize hacking as a clandestine and malevolent endeavor, the reality is far more nuanced. Hacking is a tool – a double-edged sword wielded by individuals with varied intentions and motivations.  scope In this presentation, we will delve into the multifaceted world of hacking and hackers. Exploring the various types of cyber threats and the individuals or groups behind them How to protect yourself from hacking attacks.  Abstract This presentation endeavors to shed light on the multifaceted world of hacking, delineating its various types, the motivations that drive hackers, and the strategies to safeguard oneself against potential breaches. By delving into real-world case studies, emerging trends, and the scope of vulnerabilities, we aim to equip our audience with a comprehensive understanding of the challenges posed by hacking and the imperative of robust cybersecurity measures. we embark on a journey through the labyrinth of cyber threats, guided by the Types of hacking Cross-Site Scripting Ethical Hacking Malware Phishing SQL Injection (SQLi) (XSS) Denial-of-Service (DoS) and Distributed Man-in-the-Middle Brute Force Attack Social Engineering Ransomware Denial-of-Service (MitM) (DDoS) Types of Hackers State- White Hat Black Hat Hacktivists Sponsored Hackers Hackers Hackers Government-backed use their skills to individuals or groups hackers engage in improve cybersecurity. who hack for political espionage or cyber malicious hackers who warfare on behalf of They may work for or social reasons. They exploit vulnerabilities their respective organizations, may target for personal gain. They nations. They may conducting security organizations or may steal sensitive target foreign assessments and governments to information, engage in governments, penetration tests to promote a particular financial fraud, or corporations, or identify and address cause or ideology, critical infrastructure disrupt services for vulnerabilities before often using hacking as to gather intelligence their own benefit. malicious actors a form of protest or or disrupt operations exploit them. activism. How to protect yourself Use Strong Passwords: Create complex, unique passwords for each account and consider using a password manager to securely store them. Keep Software Updated: Regularly update operating systems, applications, and antivirus software to patch security vulnerabilities and protect against known threats. Enable Two-Factor Authentication (2FA): Add an extra layer of security to your accounts by requiring a second form of verification, such as a code sent to your phone. Be Cautious Online: Exercise caution when clicking on links or downloading attachments, especially from unknown or suspicious sources. Be wary of phishing attempts and verify the legitimacy of websites before entering sensitive information.  Other types of cybercrime Software Piracy The unauthorized distribution, reproduction, or use of copyrighted software without the permission of the copyright holder. Making cracks involves reverse engineering or modifying software to bypass licensing mechanisms, allowing users to use the software without paying for it. Copyright Infringement: Reproducing, distributing, or publicly performing copyrighted works without authorization. Using VPNs to distribute cracked software often involves sharing copyrighted material without the copyright holder's permission. Trafficking in Illegal Goods: Distributing cracked software or making cracks can be considered trafficking in illegal goods, as it involves the dissemination of unauthorized copies of copyrighted software.  Difference between hacking and cybercrime Hacking involves exploring and manipulating computer systems, networks, or software, with motivations ranging from ethical improvement of security to malicious exploitation for personal gain or harm. It can be legal, such as in ethical hacking, or illegal, as in unauthorized intrusion into systems. Cybercrime, on the other hand, encompasses a broader range of illicit activities committed using computers or digital devices, including unauthorized access, data theft, financial Motivation of hacking Financial Gain Espionage Activism Thrill-seeking monetary profit, stealing State-sponsored hackers and target organizations, simply for the challenge or sensitive information such as corporate spies may conduct governments, or individuals to excitement it brings, seeking credit card details, banking cyber espionage to gather promote political or social recognition within the hacking credentials, or intellectual intelligence, steal trade causes, such as freedom of community or adrenaline rush property to sell on the dark secrets, or gain a competitive speech, privacy rights, or from circumventing security web or use for extortion. advantage in economic or environmental activism. measures. geopolitical conflicts. Case studies Equifax Data Breach (2017): Hackers exploited a vulnerability in Equifax's website to gain unauthorized access to sensitive personal information, including names, social security numbers, and credit card details, affecting over 147 million consumers. WannaCry Ransomware Attack (2017): The WannaCry ransomware spread rapidly across the globe, infecting hundreds of thousands of computers in over 150 countries. The attack encrypted files and demanded ransom payments in Bitcoin, crippling critical infrastructure, businesses, and healthcare systems. SolarWinds Supply Chain Attack (2020): Hackers compromised SolarWinds' software update mechanism to distribute malicious updates containing a backdoor, allowing unauthorized access to thousands of organizations worldwide, including government agencies and Fortune 500 companies.  Case Study: Sony Pictures Entertainment Cyberattack Background: In November 2014, Sony Pictures Entertainment (SPE) was targeted by a group of hackers identifying themselves as the "Guardians of Peace" (GOP). The attackers gained unauthorized access to SPE's network and began exfiltrating large volumes of sensitive data, including emails, employee records, financial information, and unreleased films.  Case Study: Sony Pictures Entertainment Cyberattack Key Events: Initial Intrusion: The hackers exploited vulnerabilities in SPE's network infrastructure to gain access to the company's systems. They employed a variety of techniques, including malware, phishing attacks, and social engineering, to infiltrate SPE's network undetected. Data Exfiltration: Once inside, the attackers exfiltrated vast amounts of sensitive data, including confidential corporate information, unreleased films, and personal information of employees, including salaries, Social Security numbers, and medical records. Publication of Stolen Data: The attackers threatened SPE with the release of stolen data unless certain demands were met. When SPE refused to comply, the hackers began releasing the stolen data in waves, leading to widespread media coverage and public scrutiny Reputational Damage and Fallout: The release of sensitive corporate information, internal emails, and personal data of employees resulted in significant reputational damage for Sony Pictures Entertainment. The leaked emails revealed controversial exchanges between senior executives, including racially insensitive remarks and disparaging comments about Hollywood personalities and celebrities.  Lessons Learned Importance of Patch Management Transparency and Communication Data Protection and Compliance Cybersecurity Awareness and Preparedness Fawry Egyptian Company Data Breach Background: In a significant cybersecurity incident, Fawry, a leading Egyptian electronic payment company, experienced a data breach resulting in the theft of customers' credit card information. Fawry's platform, widely used for various financial transactions, including bill payments and e-commerce, stored users' credit card details for convenience.  Hard organizations to get hacked Organizations and countries invest heavily in cybersecurity measures, have robust defense mechanisms, and employ advanced technologies to mitigate the risk of cyberattacks. Here are a few examples: National Security Agencies Large Tech Companies Financial Institutions Military and Defense Organizations How AI Helps Hacking Automated Vulnerability Detection AI can scan networks and systems for vulnerabilities automatically. Targeted Phishing Attacks Using AI to personalize and tailor phishing e-mails to specific targets. Adversarial Machine Learning ML techniques are used to evade traditional cybersecurity systems like IDS and Anti Viruses. Automated Exploitation Social Engineering and Manipulation: AI Algorithms can generate data like Videos by DeepFake or audio generated messages to profile individuals and manipulate human psychology more effectively. Enhanced Malware Capabilities  New trends Ransomware-as-a-Service (RaaS) Criminal organizations offer ransomware-as-a- service platforms, allowing less technically proficient hackers to launch ransomware attacks in exchange for a share of the profits. Supply Chain Attacks Hackers target software vendors or service providers to compromise their products or services, allowing them to infiltrate the systems of multiple organizations that use the compromised software. Internet of Things (IoT) Vulnerabilities The proliferation of IoT devices introduces new security challenges, as many devices lack robust security features or receive infrequent updates, Failure of hacking Effective Cybersecurity Defenses: Organizations with robust cybersecurity measures, such as firewalls, intrusion detection systems, and incident response protocols, can detect and mitigate hacking attempts before significant damage occurs. User Awareness and Training: Educating users about cybersecurity best practices, such as recognizing phishing emails, using strong passwords, and reporting suspicious activity, can help prevent successful hacking attempts. Rapid Incident Response: Prompt detection and response to security incidents are critical in minimizing the impact of hacking attempts. Organizations with effective incident response plans can contain breaches and restore normal operations quickly. What can be hacked Personal Devices: Networks and Websites and Web Internet of Things Computers, Infrastructure: Applications (IoT) Devices smartphones, tablets, and other personal devices  conclusion hacking represents a pervasive and ever- evolving challenge in the digital age. The critical importance of cybersecurity awareness and preparedness. Individuals and organizations can fortify their defenses against malicious actors. The case studies presented serve as stark reminders of the real-world impact of cyberattacks Learn how to protect yourself as individual or organization.  Reference Classification of Attack Types and Analysis of Attack Methods, for profiling Phishing Mail Attack Groups (IEEE Access Vol. 9 2021) Ethical hacking for IoT: Security issues, challenges, solutions and recommendations (Internet Of Things and Cyper Physical Systems, Vol. 3, 3023) What is Ethical Hacking? - Definition from Techopedia Black Hat Hacker: Definition, Overview, and Examples Grey Hat Hacking: What is it and How Does it Work? Types of Hackers: White Hat vs. Black Hat vs. Grey Hat vs. Hacktivist The 4 Different Types of Hackers and Their Motivations Top 15 Cyber Security Tips for You and Your Family 10 Ways to Protect Against Hackers What is Phishing? How to Protect Yourself from Phishing Attacks How Do Malware Distributors Work? Software Vulnerabilities: What Are They and How Do Hackers Exploit Them? What Motivates Hackers? Understanding the Different Hacker Motivations The Four Types of Hackers and Their Motivations Thank you QUISIONS? TITLE CYBERSECURITY FOR INDUSTRIAL IOT (IIOT): THREATS, COUNTERMEASURES, CHALLENGES AND FUTURE DIRECTIONS PREPARED BY: EL-SAYED RAMADAN SUPERVISED BY: DR/ IBRAHIM GOMAA AGENDA Introduction Background Cloud/Edge/ Fog Computing Industry IOT protocols, paradigms and threats IIoT application domains Security threats and vulnerabilities Countermeasures for the IIoT Open research challenges  Introduction The Industrial Internet of Things (IIoT), also known as Industry 4.0, integrates IoT technology with industrial operations to enhance automation, efficiency, and productivity. This combination involves smart objects, Cyber-Physical Systems (CPSs), information technology, and cloud/edge computing platforms. This paper discusses the latest research results, benefits, challenges, application areas, communication protocols, control systems, cyberattacks, and future research directions related to IIoT BACKGROUND What is CLOUD COMPUTING Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence— over the Internet ("the cloud"). Users can access cloud services without having to manage their own IT infrastructure, which can be expensive and complex. 1. Software-as-a-Service (SaaS) 2. Platform-as-a-Service (PaaS) 3. Infrastructure-as-a-Service (IaaS) 4. Unified Communications-as-a-Service (UCaaS) Types of Deployment Models in Cloud Computing What is EDGE COMPUTING Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. Edge computing is often used in conjunction with cloud computing, with the cloud providing centralized resources and management while edge devices handle local processing and storage. Benefits of EDGE COMPUTING Reduced latency: Edge computing can significantly reduce latency, which is critical for applications that require real-time responses. Improved bandwidth utilization: Edge computing can reduce bandwidth consumption by processing and filtering data locally before sending it to the cloud. Reduced cloud costs: Edge computing can reduce cloud costs by offloading processing tasks from the cloud to edge devices. Enhanced security: Edge computing can improve security by processing sensitive data locally, reducing the risk of data breaches. Improved scalability: Edge computing can improve scalability by distributing processing tasks across multiple edge devices. According to the OpenFog Consortium started by Cisco, the key difference between edge and fog computing is where the intelligence and compute power are placed. INDUSTRY IOT Introduction to IIoT Definition: The Industrial Internet of Things (IIoT) refers to the integration of sensors, devices, and machinery with network connectivity in industrial settings to collect and exchange data. Sample IIoT architecture based on the Purdue Challenges and associated solutions for IIoT Challenges Potential solutions Interconnectedness of smart devices in Tailored protocols IIoT Data segregation across IT/OT devices Larger attack surface Firewalls for traffic filtering Multi-factor authentication Security by design for integrated IT/OT platforms to incorporate security Vulnerabilities of Supervisory Control and throughout the lifecycle of the system Data Acquisition (SCADA) systems from design to development and deployment Lack of IT network firewall for traditional Firewalls for traffic filtering SCADA systems Data integrity Data integrity via Blockchain technology PROTOCOLS, PARADIGMS AND THREATS IIoT communication protocols Threats/Vulnerabilities Lack of authentication Lack of encryption Controller Area Network (CAN) DoS Malicious data injection Eavesdropping Weak authentication and encryption Port obscurity Message Query Telemetry Transport (MQTT) DoS MITM Brute force attacks Lack of authentication and authorization IP spoofing Constrained Application Protocol (CoAP) MITM DoS Poor encryption and authorization Data Distribution Service (DDS) DoS MITM Poor authentication and encryption DoS Advanced Message Query Protocol (AMQP) Malicious data injection MITM Traffic hijacking Remote code execution  IIoT control systems In industrial environments, control systems are vital for managing and operating critical infrastructures such as smart factories, mining, and supply chains. IIoT control systems can be categorized into three types Centralized control system A centralized control system uses a single central controller to monitor and control multiple subsystems. Components: Sensors: Record operating conditions and report data to the central controller. Actuators: Receive control and command signals from the central controller. Decentralized Control System A decentralized control system employs distributed individual controllers within each subsystem. Components: Distributed Control System (DCS): Specific controllers handle production and process-level activities. Programmable Logic Controllers (PLCs): Interpret sensor signals and generate control signal for actuators at fixed intervals. Hierarchical Control System A hierarchical control system features a multi-layer structure for handling complex and large industrial systems. IIOT APPLICATION DOMAINS Industrial IoT application domains Mining: Advantages: Improved safety, enhanced equipment monitoring, optimized resource management. Challenges: Harsh environmental conditions, high costs of deployment. Attacks: Cyber-physical attacks targeting mining operations. Manufacturing (Factories): Advantages: Streamlined production processes, reduced downtime, predictive maintenance. Challenges: Integration with legacy systems, high initial investment. Attacks: Malware, ransomware targeting manufacturing systems. Healthcare: Advantages: Improved patient monitoring, enhanced diagnostic capabilities, streamlined operations. Challenges: Data privacy, regulatory compliance. Attacks: Data breaches, ransomware attacks on healthcare systems. Healthcare: Advantages: Enhanced grid reliability, optimized energy usage, integration of renewable energy sources. Challenges: Ensuring real-time data processing, maintaining grid security. SECURITY THREATS AND VULNERABILITIES Industrial IoT application domaines In the realm of cybersecurity, several types of attacks pose significant threats to Industrial Internet of Things (IIoT) systems. Here are explanations of three major types: 1. Distributed Denial of Service (DDoS) Attacks 2. Phishing Attacks 3. Side Channel Attacks 4. Man-in-the-middle attacks 5. Ransomware attacks 6. Cyber espionage 7. Web application attacks 8. Supply chain attacks 9. Miscellaneous attacks COUNTERMEASURES FOR THE IIOT Countermeasures for the IIoT Cybersecurity challenges in the industrial setting (industrial IoT) are quite different and complex when compared to consumer IoT (normal IoT). Here present commonly known cybersecurity solutions proposed to mitigate cyber threats and associated vulnerabilities for the IIoT. Intrusion detection systems for IIoT Machine learning techniques for IIoT Securing SCADA networks Other countermeasures Intrusion detection systems for IIoT Intrusion Detection Systems (IDS) for IIoT detect malicious network traffic beyond traditional firewalls, with signature-based IDS using predefined patterns and anomaly-based IDS monitoring abnormal behaviour. Hybrid IDS combining both techniques have been proposed, such as lightweight hybrid IDS employing agent-based nodes and ML-based IDS using deep learning for attack detection, but may face challenges in computational efficiency and compatibility with small datasets. One of the limitations of this research is that it primarily focuses only on Modbus/TCP communications. However, in an industrial environment, many other communication protocols such as MQTT, HTTP, andDNP3 are often used. Machine learning techniques for IIoT Machine learning (ML) techniques are crucial for detecting attacks in IIoT due to the large data volumes generated, offering advantages over traditional IDSs by identifying complex anomalies and adapting to evolving network topologies. ML-based frameworks have been proposed for DDoS attack detection, phishing attack detection, network forensics, and anomaly detection, employing algorithms such as K-nearest neighbor, Naïve Bayes, random forest. These models enhance IIoT security by effectively identifying and mitigating threats in industrial networks, but may face challenges in computational efficiency, robustness against outliers, and scalability. Machine learning techniques for IIoT The proposed particle deep framework was validated and tested using the Bot-IoT dataset and UNSW- NB15 datasets. The results obtained showed that the framework identifies all the attack types of the Bot-IoT dataset with a 99.90% accuracy and with a 99.20%for the UNSW-B15 dataset. Securing SCADA networks Securing SCADA networks involves addressing vulnerabilities across components like RTUs, PLCs, and wireless sensor networks, with threats ranging from DoS attacks to jamming and fake location injection. ML-based IDSs are designed to monitor SCADA systems for abnormal behaviour, with models like random forest and ensemble-based learning showing promise. resilient middleware frameworks employing multi-agent systems aim to enhance security through adaptive coordination but may lack specifics on achieving high security levels and the tools required. Other countermeasures The security of IIoT devices poses various challenges and opportunities, with common threats like code injection, data leakage, and DoS attacks, which can be countered through measures such as key management, user device authentication, authorization, and access control. Cryptography-based solutions, including tailored cryptography, blockchain, and distributed ledger technologies, offer secure data exchange and accountability in IIoT environments. Techniques like re-encryption and partial decryption enhance end-to-end data confidentiality. Blockchain-based solutions address security threats across IIoT network layers, from the local layer collecting raw data to the ledger layer providing transaction-specific services. Mitigation strategies include cryptographic signatures, secure access control mechanisms, virtual machine patch management, fully homomorphic encryption, and attribute-based encryption. OPEN RESEARCH CHALLENGES Cybersecurity challenges for IIoT The data generated is more likely to be time-sensitive and it is vital to ensure end-to-end data transmission without time delays to ascertain real-time compliance. it is essential to ensure that the effective process of industrial automation can withstand an impact against the limited storage capacity of sensors and their computational energy reserves. Scalability Decentralized industrial applications Security and privacy Threat landscape Standardization Forensics Scalability Scalability in IIoT involves designing adaptable systems to handle numerous devices and integrating technologies like edge computing, SDN, blockchain, and AI to manage diverse protocols and applications. Decentralized industrial applications Decentralizing industrial IoT applications enhances security, redundancy, and reduces latency, addressing challenges like bandwidth and latency in centralized systems. While edge and fog computing offer decentralized solutions, designing effective decentralized architectures for diverse industrial environments is complex and requires robust coordination mechanisms. Security and privacy Ensuring security and privacy in IIoT involves protecting devices and data against vulnerabilities, especially with the integration of legacy systems, while leveraging technologies like edge, fog, blockchain, and AI for enhanced protection. Threat landscape Mapping the IIoT ecosystem to a dynamic threat landscape is crucial for understanding vulnerabilities and mitigating sophisticated cyber-attacks on critical infrastructures. Standardization Standardization in IIoT is essential for enhancing interoperability, compatibility, reliability, collaboration, and security across diverse devices and protocols, but current standards are insufficient for the unique needs of IIoT applications. Forensics Implementing forensic investigation in IIoT using AI can accurately identify cyber criminals and provide court-admissible evidence, with proactive forensic readiness enhancing detection and analysis of unauthorized activities, though standardized forensic models for IIoT are still needed. REFERENCES REFERENCES 1. Sri Harsha Mekala, Zubair Baig, Adnan Anwar, Sherali Zeadally, Cybersecurity for Industrial IoT (IIoT): Threats, countermeasures, challenges and future directions, Computer Communications, Volume 208, 2023. ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2023.06.020