Summary of IDS Analysis Models and Techniques
2025, March 7
1. What is IDS Analysis?
- Definition: The process of evaluating network traffic, logs, and system behavior to detect intrusions.
- Objective: Accurate threat detection, minimize false positives, and generate meaningful alerts.
2. IDS Analysis Models
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Signature-Based Models
- Matches data against known attack signatures.
- Accurate for known attacks, cannot detect new threats.
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Anomaly-Based Models
- Detects deviations from normal behavior.
- Can detect unknown threats, high false positives.
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Hybrid Models
- Combines signature + anomaly detection.
- More accurate, complex and resource-heavy.
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Statistical & Machine Learning Models
- Uses algorithms and statistics to model behavior and detect anomalies.
3. Signature-Based Detection
- How it works: Compares traffic to a database of known attack patterns.
- Tools: Snort, Suricata.
- Use Case: Detects known malware via payload matching.
4. Anomaly-Based Detection
- How it works: Flags behavior that deviates from a learned baseline.
- Tools: Bro (Zeek), OSSEC.
- Example: Unusual CPU spike triggers alert.
5. Hybrid IDS Models
- Combines both techniques for balanced detection.
- Tools: Cisco Firepower, McAfee Network Security Platform.
- Detects both known malware and stealthy APTs.
6. Statistical Analysis Models
- Techniques:
- Thresholding (mean + standard deviation)
- Probabilistic models
- Scalable, needs lots of training data.
7. Machine Learning in IDS
Supervised Learning
- Learns from labeled data.
- Algorithms: Decision Trees, SVM, Random Forest.
- Accurate, needs labeled dataset.
Unsupervised Learning
- Learns patterns from unlabeled data.
- Algorithms: K-Means, Autoencoders.
- Detects novel attacks, higher false positives.
Reinforcement Learning
- Learns dynamically through feedback.
- Adapts over time, resource-heavy and slow to start.
8. Evaluating IDS Models
| Metric | Meaning |
|---|---|
| True Positives | Correctly flagged attacks |
| False Positives | False alerts on normal activity |
| True Negatives | Correctly identified normal activity |
| False Negatives | Missed attacks |
| Precision | TP / (TP + FP) |
| Recall | TP / (TP + FN) |
| F1-Score | Balance between precision and recall |
9. IDS Model Selection Criteria
- Attack Type: Known or unknown threats?
- Data: Availability of labeled data?
- Network Scale: Size and complexity?
- False Positive Tolerance: How much noise is acceptable?