Intrusion Detection Systems (IDS) Full Syllabus

Module 1: Introduction to IDS

• What is Intrusion Detection?
• IDS vs IPS (Intrusion Prevention System)
• Types of IDS:
  • Network-based IDS (NIDS)
  • Host-based IDS (HIDS)
• Signature-based vs Anomaly-based detection
• Limitations of traditional IDS

Module 2: IDS Architecture and Components

• Key Components:
  • Sensors
  • Analyzers
  • User Interface / Management Console
• Placement of IDS in the network
• Data sources: Logs, Packets, NetFlow

Module 3: Machine Learning in IDS

• Why AI/ML for IDS?
• Workflow of AI-based IDS:
  • Data Collection
  • Feature Extraction
  • Model Selection
  • Detection and Alerting
• Common ML Algorithms:
  • Decision Trees
  • Random Forest
  • SVM (Support Vector Machines)
  • KNN
  • Neural Networks

Module 4: Data Preprocessing & Feature Engineering

• Handling raw network traffic
• Labeling malicious vs normal traffic
• Feature selection techniques (e.g., PCA)
• Data balancing (SMOTE, oversampling)

Module 5: Anomaly Detection

• Unsupervised learning for unknown threats
• Statistical methods vs AI methods
• Techniques:
  • Clustering (K-Means, DBSCAN)
  • Autoencoders for anomaly detection
  • Isolation Forest

Module 6: Implementation of AI-IDS Systems

• Open-source tools & platforms:
  • Snort + ML integration
  • Suricata
  • Bro/Zeek + AI
• Real-time data pipeline setup
• Building alert generation rules

Module 7: Evaluation Metrics for IDS Models

• Confusion Matrix
• Accuracy, Precision, Recall, F1-score
• ROC Curve and AUC
• False Positive Rate (FPR), False Negative Rate (FNR)

Module 8: Deep Learning for IDS

• Using CNNs and RNNs on traffic data
• LSTM for time-series anomaly detection
• Real-world dataset usage: NSL-KDD, CICIDS2017

Module 9: Continuous Learning and Model Updating

• Online learning for IDS
• Model retraining techniques
• Handling concept drift in network behavior

Module 10: Hands-on Projects & Labs

• Build an AI-based NIDS using Python
• Real-time anomaly detection system
• Simulating network attacks and detecting them
• Integration with SIEM tools (like ELK, Splunk)

Module 11: Security, Privacy & Ethics

• Data privacy concerns in IDS
• Adversarial attacks on IDS models
• Model explainability (XAI in IDS)
• Ethical concerns in automated threat detection