AI for Cybersecurity

Overview of modern cybersecurity challenges

Role of AI and machine learning in cyber defense

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

Network traffic data, log files, system events

Structured vs. unstructured security data

Feature engineering from cybersecurity datasets

Classification models for malware and intrusion detection

Supervised vs. unsupervised approaches

Use of algorithms like SVM, Random Forests, and XGBoost

Identifying abnormal behavior in network activity

Autoencoders, Isolation Forests, and clustering for anomaly detection

Reducing false positives with AI

Applying LSTM and CNNs to detect sequential and visual patterns

Behavioral-based malware detection

Threat hunting with deep neural networks

Traditional IDS vs. AI-enhanced IDS

Real-time monitoring and response

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

Analyzing threat reports and dark web data

Text classification for phishing and social engineering

Extracting Indicators of Compromise (IoCs) using NLP

Biometric authentication with machine learning

Behavioral biometrics (keystroke, mouse dynamics)

Detecting credential stuffing and insider threats

AI for cloud workload protection

Threat detection in IoT networks

Edge AI security for connected devices

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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