Machine Learning Algorithms in Action: Identifying and Mitigating Zero-Day Attacks

Abstract

Machine learning algorithms play a crucial role in the identification and mitigation of zero-day attacks, which are cyberattacks that exploit vulnerabilities in software or hardware that are unknown to the vendor. These attacks are particularly dangerous because they occur before a patch or security fix is developed, leaving systems vulnerable. Machine learning models can detect anomalous patterns in network traffic, system behavior, and file interactions that might signal the presence of a zero-day exploit. By using supervised learning to analyze historical attack data or unsupervised learning to identify new, previously unseen threats, these algorithms can quickly flag potential zero-day incidents. Additionally, reinforcement learning techniques can be employed to adaptively update detection mechanisms as new attack methods emerge. Machine learning-powered security systems offer proactive defense by providing real-time threat intelligence, reducing the time between vulnerability discovery and mitigation, and helping organizations safeguard their critical assets from sophisticated cyberattacks.