Summary of Intrusion Detection and Prevention
2025, March 6
1. Introduction to Intrusion Detection and Prevention
- Intrusion Detection (IDS): Identifies and responds to malicious activities or policy violations in a computer system or network.
- Intrusion Prevention (IPS): A proactive approach to stopping threats before they can compromise systems or data.
2. Key Differences Between IDS and IPS
| Feature | IDS (Intrusion Detection System) | IPS (Intrusion Prevention System) |
|---|---|---|
| Functionality | Detects attacks and sends alerts | Blocks malicious traffic |
| Mode | Passive (monitors and logs) | Active (intercepts and blocks) |
| Response | Alerts administrators | Prevents threats in real-time |
3. Importance of IDS and IPS
- Essential for monitoring internal and external security threats.
- Detects unknown threats that bypass firewalls.
- Helps organizations meet security standards (e.g., GDPR, HIPAA).
4. Evolution of IDS
- 1980s: Early IDS for mainframes.
- 1990s: Expansion with networking, rise of Snort (1998).
- 2000s: Integration with firewalls, adoption of machine learning.
- 2010s: Behavioral analysis, hybrid IDS (NIDS + HIDS), cloud and IoT security challenges.
5. Detection Methods
- Signature-Based Detection: Identifies attacks based on predefined patterns (high accuracy but ineffective for new threats).
- Anomaly-Based Detection: Detects deviations from normal behavior (can detect unknown threats but prone to false positives).
6. Role of Machine Learning in IDS
- Uses decision trees, clustering, and neural networks for threat detection.
- Improves over time by identifying emerging attack patterns.
7. Next-Generation IDS (NGIDS)
- Integrates AI, machine learning, and big data analytics.
- Features real-time threat intelligence and predictive capabilities.
8. IDS for Cloud & IoT
- Cloud IDS adapts to dynamic environments and distributed infrastructures.
- IoT IDS manages vast device networks and security challenges.
9. Modern IDS Systems
- Integrated with SIEM (Security Information and Event Management) for real-time security analysis.
- Automated responses enhance threat mitigation.
10. Future Trends in IDS
- AI and Automation: Detects advanced persistent threats (APTs).
- Big Data and Predictive Analytics: Analyzes vast traffic to predict threats.
- Blockchain-based IDS: Decentralized security measures for distributed systems.