Machine learning in cybersecurity threat detection and response.

Abstract

Machine Learning has become vital in the cybersecurity field and widely used to enhance identifying threats, preventing attacks, and reacting to them. Its uses include: intrusion detection, malware, fraud, and real-time response counter measure programs. However, the integration of ML into cybersecurity has multiple issues. Due to the dynamic nature of cyber threats, the requirement for obtaining high quality data, high false positive/negative rates, vulnerability to adversarial attacks, and resource constraints, the application of ML-based solutions is challenging. Moreover, issues of ethics and privacy where the collection and monitoring of data is concerned makes it even worse. However, there are certain challenges that have to be overcome Here too, ML techniques are evolving constantly, data sharing is strong and privacy regulation are important and must be followed to be relevant. If these problems are solved by ML, then the future of cybersecurity is bright because ML can give the organization solutions that are better, more flexible and scalable than the current systems for protecting from advanced cyber threats.