Threat Detection – DZone Refcardz
Threat detection requires a multifaceted approach that constantly adapts to the ever-changing cyber landscape. It is also important to note that recognizing the “what” through indicators of compromise (IOCs) and known malicious patterns is only half the equation. The ability to identify the “how” via predictive analytics, anomaly detection, and threat intelligence feeds — all aligned with strategic incident response planning — forms the other essential half.
The progression from foundational techniques, like signature- and behavior-based analysis, to advanced machine learning (ML) and heuristic methods highlights the industry’s gradual innovation over the years. The tables below summarize these methodologies and the purposes they serve:
Approach
Attributes
Purpose
Use Cases
Signature-based detection
Relies on known patterns and definitions to identify threats
Identify and block known malicious codes and viruses
Antivirus software, intrusion detection systems
Behavior-based detection
Monitors and analyzes unusual system behavior to detect anomalies
Detect new or unknown threats through behavior analysis
Anomaly detection in network traffic, user monitoring
Approach
Attributes
Purpose
Use Cases
Heuristics
Utilizes rules and patterns to identify suspicious activities
Quick identification of potential threats with less data
Email scanning for spam and phishing, fraud detection
Anomaly detection
Detects deviations from established baselines, revealing potential threats
Detecting unknown threats by identifying behavioral anomalies
Network security, fraud detection, system monitoring
ML algorithms
Employs algorithms that learn and adapt to detect new and evolving threats
Adapting to evolving threat landscape with continuous learning
Adaptive threat detection, predictive threat analysis