Cybersecurity and AI Overview

Course Learning Objectives

• Understand cybersecurity challenges and the role of AI in addressing them.
• Identify and evaluate vulnerabilities in AI systems, including trapdoors and adversarial attacks.
• Explore AI's implications in offensive and defensive cybersecurity measures.
• Gain hands-on experience in AI-driven security applications and threat simulations.

Class Rules

• Students are encouraged to ask questions.
• Attendance is mandatory for final exam eligibility.

Course Assessment

• Final exam: 50%
• Midterm: 20%
• Quizzes: 10%
• Project: 20% (2-3 members per group, report and presentation required).
• Cheating and plagiarism will result in zero marks.

Introduction to AI and Security

• AI has applications that require human intelligence like decision-making and pattern recognition.

What is AI?

• Systems that perform tasks requiring human intelligence.
• Examples include decision-making and pattern recognition.

Categories of AI:

• Narrow AI: Performs specific tasks, such as spam filtering.
• General AI: Hypothetical AI with human-level intelligence.

What is Machine Learning (ML)?

• A subset of AI that trains systems to learn from data without explicit programming.

Machine Learning

• Supervised Learning: Models are trained on labeled data, e.g., malware detection.
• Unsupervised Learning: Models identify patterns without labels, e.g., anomaly detection.
• Reinforcement Learning: Systems learn by interacting with their environment and receiving feedback, e.g., AI in autonomous cybersecurity systems.

AI Enabling Security

• AI systems support security.
• Can detect anomalies, predict potential attacks, and secure systems against known and unknown threats.
• Examples include AI in intrusion detection systems and AI-driven malware analysis.

Security Enabling Better AI

• Homomorphic Encryption allows computations on encrypted data without decrypting it, ensuring privacy in AI models handling sensitive data.
• Federated Learning trains AI models across multiple devices or servers while keeping data decentralized, enhancing privacy by avoiding direct access to the data itself.

Why AI Security is Important

• AI is embedded in various sectors, from smart cities to healthcare.
• As AI takes over decision-making, attackers are more incentivized to target these systems due to the potential damage.
• AI is crucial for understanding and preparing for the evolving security landscape.

Role of AI in Cybersecurity

• Defense: Detects and prevents cyber threats through pattern recognition and real-time analysis.
• Offense: Used by attackers to automate attacks and create sophisticated malware.

Why AI is Critical in Modern Cybersecurity

• Automation provides efficiency and speed.
• Adaptive defense allows the system to learn and evolve.

AI Applications in Cybersecurity

• Intrusion Detection Systems (IDS) use anomaly detection.
• Malware detection utilizes machine learning models.
• Behavioral Analysis detects insider threats.

Challenges of AI in Security

• AI is a vulnerability.
• Examples include Adversarial Machine Learning (AML), where attackers can craft inputs to cause AI systems to make mistakes.

AI Vulnerabilities

• Data Poisoning involves manipulating training data to degrade performance.
• Adversarial Attacks introduce perturbations to fool AI models.
• Model Inversion Attacks extract sensitive data from AI models.

Trust and Bias Issues

• AI Bias can influence results.
• Lack of Transparency hinders trust in AI systems.

The Dual Use of AI

• Offensive AI: Attackers leverage AI for smart malware, spear-phishing and creating deepfakes for fraud.
• Defensive AI: AI predicts, detects and responds to security threats before harm occurs.

The importance of AI in Modern Cybersecurity

• Scalability and efficiency are crucial for managing large datasets and rapidly detecting threats.
• The evolution of cyber threats necessitates AI to adapt and evolve alongside them.

Examples of AI in Cybersecurity Tools

• Darktrace
• IBM Watson for Cybersecurity
• Vectra AI

This quiz covers the introduction to AI and its crucial role in cybersecurity. It addresses the challenges posed by vulnerabilities in AI systems and the application of AI in both offensive and defensive measures. Students will assess their understanding of AI-driven security solutions and threat simulations.