Recommended Prerequisites:
- Completion of AI+ Security Level 1 and 2
- Intermediate / Advanced Python Programming: Proficiency or expert in Python, including deep learning frameworks (TensorFlow, PyTorch).
- Intermediate Machine Learning Knowledge: Proficiency in understanding of deep learning, adversarial AI, and model training.
- Advanced Cybersecurity Knowledge: Proficiency in threat detection, incident response, and network/endpoint security.
- AI in Security Engineering: Knowledge of AI’s role in identity and access management (IAM), IoT security, and physical security.
- Cloud and Container Expertise: Understanding of cloud security, containerization, and blockchain technologies.
- Linux/CLI Mastery: Advanced command-line skills and experience with security tools in Linux environments.
Course Outline:
Lesson 1: Foundations of AI and Machine Learning for Security Engineering
- 1.1 Core AI and ML Concepts for Security
- 1.2 AI Use Cases in Cybersecurity
- 1.3 Engineering AI Pipelines for Security
- 1.4 Challenges in Applying AI to Security
Lesson 2: Machine Learning for Threat Detection and Response
- 2.1 Engineering Feature Extraction for Cybersecurity Datasets
- 2.2 Supervised Learning for Threat Classification
- 2.3 Unsupervised Learning for Anomaly Detection
- 2.4 Engineering Real-Time Threat Detection Systems
Lesson 3: Deep Learning for Security Applications
- 3.1 Convolutional Neural Networks (CNNs) for Threat Detection
- 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security
- 3.3 Autoencoders for Anomaly Detection
- 3.4 Adversarial Deep Learning in Security
Lesson 4: Adversarial AI in Security
- 4.1 Introduction to Adversarial AI Attacks
- 4.2 Defense Mechanisms Against Adversarial Attacks
- 4.3 Adversarial Testing and Red Teaming for AI Systems
- 4.4 Engineering Robust AI Systems Against Adversarial AI
Lesson 5: AI in Network Security
- 5.1 AI-Powered Intrusion Detection Systems (IDS)
- 5.2 AI for Distributed Denial of Service (DDoS) Detection
- 5.3 AI-Based Network Anomaly Detection
- 5.4 Engineering Secure Network Architectures with AI
Lesson 6: AI in Endpoint Security
- 6.1 AI for Malware Detection and Classification
- 6.2 AI for Endpoint Detection and Response (EDR)
- 6.3 AI-Driven Threat Hunting
- 6.4 AI for Securing Mobile and IoT Devices
Lesson 7: Secure AI System Engineering
- 7.1 Designing Secure AI Architectures
- 7.2 Cryptography in AI for Security
- 7.3 Ensuring Model Explainability and Transparency in Security
- 7.4 Performance Optimization of AI Security Systems
Lesson 8: AI for Cloud and Container Security
- 8.1 AI for Securing Cloud Environments
- 8.2 AI-Driven Container Security
- 8.3 AI for Securing Serverless Architectures
- 8.4 AI and DevSecOps
Lesson 9: AI and Blockchain for Security
- 9.1 Fundamentals of Blockchain and AI Integration
- 9.2 AI for Fraud Detection in Blockchain
- 9.3 Smart Contracts and AI Security
- 9.4 AI-Enhanced Consensus Algorithms
Lesson 10: AI in Identity and Access Management (IAM)
- 10.1 AI for User Behavior Analytics in IAM
- 10.2 AI for Multi-Factor Authentication (MFA)
- 10.3 AI for Zero-Trust Architecture
- 10.4 AI for Role-Based Access Control (RBAC)
Lesson 11: AI for Physical and IoT Security
- 11.1 AI for Securing Smart Cities
- 11.2 AI for Industrial IoT Security
- 11.3 AI for Autonomous Vehicle Security
- 11.4 AI for Securing Smart Homes and Consumer IoT
Lesson 12: Capstone Project – Engineering AI Security Systems
- 12.1 Defining the Capstone Project Problem
- 12.2 Engineering the AI Solution
- 12.3 Deploying and Monitoring the AI System
- 12.4 Final Capstone Presentation and Evaluation
