Basics of Cybersecurity: Threats & Vulnerabilities

Basics of Cybersecurity: Threats and Vulnerabilities

• Cyber threats originate from individuals aiming to steal data or damage systems.
• Malware attacks involve malicious software (viruses, worms, trojans, spyware, ransomware).
• Effects of malware include data collection, network manipulation, and data destruction.
• Social engineering manipulates individuals to provide access for malware.
• Phishing, baiting, pretexting, vishing, smishing, piggybacking, and tailgating are social engineering techniques.
• Supply chain attacks target legitimate applications, potentially infecting software from vendors.
• Man-in-the-Middle attacks intercept communications, eavesdropping or impersonating parties.
• Denial-of-Service attacks overwhelm a system with traffic, hindering functionality.
• HTTP flood DDoS and SYN flood DDoS are denial-of-service techniques.
• Injection attacks insert malicious input into web applications, potentially exposing sensitive information.
• Cross-Site Scripting (XSS) is an example of an injection attack.

Types of Cybersecurity Vulnerabilities

• System misconfigurations can cause security mistakes in network assets.
• Cybercriminals exploit vulnerabilities in security settings to gain access.
• Outdated or unpatched software is easily exploited by attackers.
• Patch management strategies are crucial to ensure systems are updated regularly.
• Missing or weak authorization credentials allow attackers to guess passwords and access systems.
• Malicious insider threats are posed by employees with access to critical systems.
• Missing or poor data encryption makes sensitive data vulnerable to interception.

Data Privacy and Protection

• Data privacy focuses on who can access data, often controlled by the user.
• Data protection is the company's responsibility to ensure data privacy.
• Data privacy guidelines dictate how data should be collected and handled.
• Data protection involves procedural steps to safeguard sensitive data.
• Principles of data protection include data availability, lifecycle management, and information protection.

Legal and Ethical Considerations in Cyber Security

• Compliance with laws like GDPR and CCPA is crucial to protect personal data.
• Protecting intellectual property prevents unauthorized access and ensures rights are respected.
• Legal liability exists for organizations negligent in security.
• Ethical hackers must obtain permission to perform penetration testing and security audits.
• Cybersecurity professionals must comply with codes of conduct to ensure integrity and responsibility.
• Surveillance and monitoring should balance security with privacy rights to inform individuals.

Zero-Day Vulnerabilities

• These are software flaws known to attackers but not yet identified by users.
• Attackers can exploit these vulnerabilities to infiltrate systems undetected.

Incident Reporting Obligations

• Timely reporting of data breaches is mandatory by regulations.
• Laws exist to combat hacking, identity theft, and other cybercrimes.

Cybersecurity Legislation in the Philippines

• Various acts including RA No. 11967, RA No. 8792, RA No. 11930, RA No. 8484, RA No. 9995, RA No. 11934, RA No. 11202, and RA No. 9775 address cybercrimes, and RA No. 10173 addresses data privacy.
• Specific penalties exist for violations depending on the act violated.

IT and Society: Digital Divide

• The digital divide refers to inequalities in access, use, and skills related to information and communication technologies.
• Geographical disparities, economic barriers, education and literacy limitations, and cultural and language barriers contribute to the digital divide.

IT in Education and Learning

• Technology extends beyond online classes to include gamified learning, AI-driven personalization, VR, and data analytics.

Innovations in E-learning

• Massive Open Online Courses (MOOCs) democratize access to education from top institutions.
• Virtual classrooms and collaboration tools enhance accessibility and real-time interaction.
• AI in education personalizes lessons based on individual progress (DreamBox, Khan Academy)

Opportunities

• Digital tools promote inclusivity, allowing students from diverse backgrounds to engage.
• These tools support teacher-student interaction and provide access to global experiences.

Challenges

• Concerns regarding data privacy remain important.
• Digital fatigue and inequality in access are ongoing issues, particularly for lower-income countries.

Impact of IT on Culture and Social Interactions

• Digital communication platforms have reshaped social interactions, leading to increased awareness of global issues, greater connectivity, and more opportunities for self-expression.
• Cyberbullying, misinformation, and polarization are also negative consequences of these platforms.

Emerging Technologies

• Emerging technology is a term generally used to describe a new technology, continuing development of an existing technology, or the varying interpretations of a technology based on its use case.

Artificial Intelligence (AI)

• AI involves developing computers and robots that behave in ways that mimic or surpass human capabilities.
• Narrow or Weak AI carries out specific tasks (e.g., personal assistants).
• General AI is complex and human-like in capabilities (e.g., self-driving cars).
• Reactive machines perceive the world but don't store memory or rely on past experience.
• Limited memory AI has limited memory but gathers data to make decisions.
• Theory of mind and self-aware AI are hypothetical AI types that have not been realized.

AI Benefits and Disadvantages

• AI benefits include automating tasks, solving complex problems, improving customer experiences, accelerating healthcare, and reducing human error.
• Disadvantages include job displacement, bias and discrimination, hallucinations, and privacy and ethical concerns.

AI Applications and Examples

• Applications include healthcare, retail, customer service, manufacturing, finance, and marketing.
• Examples include generative AI tools, smart assistants, self-driving cars, wearables, and visual filters.

Machine Learning (ML)

• Machine learning teaches computers to learn from data without explicit programming.
• Deep learning uses sophisticated neural networks for advanced predictive analytics.
• Categories of ML include supervised, unsupervised, reinforcement, and semi-supervised learning.

ML Benefits and Risks

• Benefits include lower operational costs, improved efficiency, and better insights.
• Risks include job displacement, bias/discrimination, and ethical concerns.

ML Applications

• Applications include speech recognition, customer service, computer vision, recommendation systems, and more.

Big Data

• Big data refers to massive and complex data sets that can't be easily managed using conventional tools.
• Characteristics are often described by the "5 Vs": volume, velocity, variety, veracity, and value.
• Big data benefits include better insights, decision-making, personalization, and efficiency improvements.
• Uses are present in retail, healthcare, financial services, manufacturing, and more.

Data Analytics

• Data analytics involves collecting, organizing, and transforming data to inform decisions.
• Types of data analytics include descriptive, diagnostic, predictive, and prescriptive analytics.

Blockchain Technology

• Blockchain is a decentralized ledger of data shared securely among participants.
• Using cloud services enables easy data integration and sharing of transactional data from multiple sources.
• Prevents alteration and data tampering because data can't be modified without consensus from participants.

Cryptocurrency

• Cryptocurrencies are digital payment systems independent of banks, enabling peer-to-peer transactions without intermediaries.
• Transactions are recorded in a public ledger called a blockchain.
• Cryptocurrency is stored in digital wallets.

Fintech

• Fintech refers to using technology to improve and automate financial services.
• Examples are robo-advisors, investment apps, payment apps, personal finance apps, P2P lending platforms, and crypto apps.
• Insurtech is an example of leveraging technology in the insurance industry.