Intrusion Detection

Intrusion detection encompasses the tools, techniques, and processes used to identify unauthorized access, active attacks, and suspicious behavior within a system or network. Intrusion Detection Systems (IDS) monitor network traffic and system activity, comparing observations against known attack signatures and behavioral baselines to generate alerts when potential intrusions are detected. Unlike firewalls that prevent unauthorized access, IDS focuses on detecting threats that have already bypassed preventive controls.

There are two primary approaches to intrusion detection: signature-based detection, which matches activity against a database of known attack patterns (effective for known threats but blind to novel attacks), and anomaly-based detection, which establishes baselines of normal behavior and flags deviations (catches novel attacks but produces more false positives). Modern systems increasingly use machine learning to improve anomaly detection accuracy and reduce alert fatigue.

In cloud-native and application-level contexts, intrusion detection extends beyond traditional network IDS. It includes runtime application self-protection (RASP) that instruments the application to detect attacks in real time, cloud-native threat detection services (AWS GuardDuty, Google Cloud Security Command Center), container runtime security (Falco, Aqua Security), and application-level anomaly detection such as unusual API call patterns, impossible travel scenarios, or abnormal data access volumes. Effective intrusion detection requires careful tuning to balance sensitivity (catching real threats) with specificity (minimizing false positives).