Anomaly detection is the process of identifying unusual patterns or behaviors in data that do not conform to expected norms. In cybersecurity, it's used to detect potential threats such as intrusions, data breaches, malware, or insider threats by identifying deviations from normal behavior.

Why It's Important in Cybersecurity

• Early threat detection : Identifies unknown or zero-day attacks that signature-based systems may miss.
• Behavior-based monitoring : Focuses on patterns rather than known malware signatures.
• Insider threat detection : Can uncover malicious activity from users within the organization.
• Compliance and auditing : Helps ensure logs and behaviors remain within acceptable bounds.

Techniques Used

1. Statistical Methods
   • Define a baseline of "normal" activity and detect deviations.
   • Examples: z-score, moving averages, histogram-based thresholds.
2. Machine Learning
   • Supervised Learning : Requires labeled data (e.g., random forest, SVM).
   • Unsupervised Learning : Detects outliers without labeled data (e.g., k-means, DBSCAN, Isolation Forest).
   • Semi-supervised : Trains on normal data only (e.g., autoencoders, One-Class SVM).
3. Deep Learning
   • Neural networks (LSTM, CNNs) for complex pattern recognition in large-scale data.
   • Used especially in network traffic or time-series analysis.
4. Rule-Based Systems
   • Custom rules for known risky behaviors (e.g., multiple failed login attempts).