Main Techniques in Network Intrusion Detection Systems (NIDS)

1. Signature-Based Detection: Matches network traffic against a database of known threat signatures (patterns of known attacks).
2. Anomaly-Based Detection: Establishes a baseline of normal network behavior and detects deviations from this norm.
3. Heuristic-Based Detection: Uses algorithms and rules to identify suspicious activity based on behavior patterns.
4. Hybrid Detection: Combines signature-based and anomaly-based methods to enhance detection capabilities.

Signature-Based Detection

How It Works:

• Compares incoming network traffic to a predefined database of known attack signatures.
• If a match is found, it triggers an alert.

Advantages:

• High accuracy for known threats.
• Low false-positive rate when signatures are up to date.

Limitations:

• Ineffective against unknown or zero-day attacks.
• Requires regular updates to the signature database.

Anomaly-Based Detection

How It Works:

• Establishes a baseline of normal network behavior through statistical analysis or machine learning.
• Monitors network traffic for deviations from this baseline, flagging anomalies as potential threats.

Advantages:

• Can detect novel or previously unknown attacks.
• Adaptive to changes in network behavior over time.

Limitations:

• Higher false-positive rate due to normal network changes being flagged as anomalies.
• Requires significant time and resources to establish and maintain an accurate baseline.

Summary

Signature-based detection is precise for known threats but lacks effectiveness against new attacks. Anomaly-based detection excels in identifying unknown threats but can generate more false positives. Combining both methods in a hybrid approach can leverage the strengths of each, providing a more robust NIDS solution.