Machine Learning
AI technique enabling systems to learn from data and improve performance without explicit programming.
Overview
Machine learning powers modern cybersecurity by identifying patterns humans might miss and adapting to evolving threats. In security applications, ML models analyze vast amounts of data to detect anomalies, classify threats, and predict attacks. Supervised learning trains on labeled examples (known malware, phishing sites), while unsupervised learning discovers unknown patterns. Deep learning, using neural networks, excels at complex tasks like image analysis and natural language processing. However, ML models require quality training data, can be fooled by adversarial techniques, and need ongoing refinement. The most effective security solutions combine ML with human expertise.
Real-World Examples
- ▸Phishing detection based on email content and metadata
- ▸Malware classification using behavioral analysis
- ▸Anomaly detection in network traffic
Related Terms
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