- Foundations of AI and Machine Learning for Security Engineering
Core AI and ML Concepts for Security AI Use Cases in Cybersecurity Engineering AI Pipelines for Security Challenges in Applying AI to Security
- Machine Learning for Threat Detection and Response
Engineering Feature Extraction for Cybersecurity Datasets Supervised Learning for Threat Classification Unsupervised Learning for Anomaly Detection Engineering Real-Time Threat Detection Systems
- Deep Learning for Security Applications
Convolutional Neural Networks (CNNs) for Threat Detection Recurrent Neural Networks (RNNs) and LSTMs for Security Autoencoders for Anomaly Detection Adversarial Deep Learning in Security
- Adversarial AI in Security
Introduction to Adversarial AI Attacks Defense Mechanisms Against Adversarial Attacks Adversarial Testing and Red Teaming for AI Systems Engineering Robust AI Systems Against Adversarial AI
- AI in Network Security
AI-Powered Intrusion Detection Systems AI for Distributed Denial of Service (DDoS) Detection AI-Based Network Anomaly Detection Engineering Secure Network Architectures with AI
- AI in Endpoint Security
AI for Malware Detection and Classification AI for Endpoint Detection and Response(EDR) AI-Driven Threat Hunting Implementing Lightweight AI Models for Resource-Constrained Devices
- Secure AI System Engineering
Designing Secure AI Architectures Cryptography in AI for Security Ensuring Model Explainability and Transparency in Security Performance Optimization of AI Security Systems
- AI for Cloud and Container Security
AI for Securing Cloud Environments AI-Driven Container Security AI for Securing Serverless Architectures AI and DevSecOps
- AI and Blockchain for Security
Fundamentals of Blockchain and AI Integration AI for Fraud Detection in Blockchain Smart Contracts and AI Security AI-Enhanced Consensus Algorithms
- AI in Identity and Access Management (IAM)
AI for User Behavior Analytics in IAM AI for Multi-Factor Authentication (MFA) AI for Zero-Trust Architecture AI for Role-Based Access Control (RBAC)
- AI for Physical and IoT Security
AI for Securing Smart Cities AI for Industrial IoT Security AI for Autonomous Vehicle Security AI for Securing Smart Homes and Consumer IoT
- Capstone Project - Engineering AI Security Systems
Defining the Capstone Project Problem Engineering the AI Solution Deploying and Monitoring the AI System Final Capstone Presentation and Evaluation
, Foundations of AI and Machine Learning for Security Engineering, Machine Learning for Threat Detection and Response, Deep Learning for Security Applications, Adversarial AI in Security, AI in Network Security, AI in Endpoint Security, Secure AI System Engineering, AI for Cloud and Container Security, AI and Blockchain for Security, AI in Identity and Access Management (IAM), AI for Physical and IoT Security, Capstone Project - Engineering AI Security Systems, Optional Module: AI Agents for Security level 3
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