Master Study AI

AI for Cybersecurity

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Course Modules:

Module 1: Introduction to AI in Cybersecurity

Overview of modern cybersecurity challenges

Role of AI and machine learning in cyber defense

Types of cyber threats: malware, phishing, APTs, zero-days

Module 2: Data Sources in Cybersecurity

Network traffic data, log files, system events

Structured vs. unstructured security data

Feature engineering from cybersecurity datasets

Module 3: Machine Learning for Threat Detection

Classification models for malware and intrusion detection

Supervised vs. unsupervised approaches

Use of algorithms like SVM, Random Forests, and XGBoost

Module 4: Anomaly Detection Systems

Identifying abnormal behavior in network activity

Autoencoders, Isolation Forests, and clustering for anomaly detection

Reducing false positives with AI

Module 5: Deep Learning for Cybersecurity

Applying LSTM and CNNs to detect sequential and visual patterns

Behavioral-based malware detection

Threat hunting with deep neural networks

Module 6: Intrusion Detection and Prevention Systems (IDPS)

Traditional IDS vs. AI-enhanced IDS

Real-time monitoring and response

Open-source tools and frameworks (Snort, Zeek + ML)

Module 7: NLP in Cyber Threat Intelligence

Analyzing threat reports and dark web data

Text classification for phishing and social engineering

Extracting Indicators of Compromise (IoCs) using NLP

Module 8: AI for Access Control & Identity Protection

Biometric authentication with machine learning

Behavioral biometrics (keystroke, mouse dynamics)

Detecting credential stuffing and insider threats

Module 9: Securing Cloud and IoT with AI

AI for cloud workload protection

Threat detection in IoT networks

Edge AI security for connected devices

Module 10: Capstone Project

Choose from:

Building an ML-powered intrusion detection system

Designing an AI bot to monitor phishing emails

Creating a real-time anomaly dashboard for a simulated network

Deliver demo, report, and risk analysis

 

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