Machine learning has become an essential tool in cybersecurity due to its ability to analyze large amounts to find potentials threats.
Machine learning algorithms can detect suspicious activities or abnormal behavior in network traffic.
ML models can analyze the characteristics of known malware and learn to recognise new or unknown malware variants based on patterns
ML enables automated analysis and decision-making, reducing the manual effort required for threat detection and response.
Machine learning algorithms can provide real-time detection and response capabilities.
Machine learning algorithms can understand the user behavior and detect any deviations from normal behavior.
However sometimes ML models can generate false positives, or false negatives, failing to detect actual threats.
So regular model updates and tuning are required to minimize the errors in ML models detection.