Machine learning (ML) is a core component of AI-driven malware detection. It enables cybersecurity systems to analyze vast amounts of data, learn from patterns, and make accurate predictions about potential threats. There are three primary types of machine learning used in malware detection:
| Machine Learning Type | How It Works | Use Case |
|---|---|---|
| Supervised Learning | Trains on labeled datasets containing both malware and benign software samples. | Effective for recognizing known malware based on historical data. |
| Unsupervised Learning | Analyzes data without predefined labels to identify abnormal behavior. | Useful for detecting zero-day attacks and emerging threats. |
| Deep Learning | Uses neural networks to detect complex patterns in large datasets. | Enhances accuracy in detecting advanced malware techniques. |
By leveraging these approaches, AI can rapidly adapt to new cyber threats and provide continuous protection against malware attacks.
