Evolving Threat Detection with Meta-Learning Algorithms

Abstract

Meta-learning algorithms have thus changed how systems learn threats, so they are relevant to emerging cyber threats. These algorithms solve crucial issues such as the absence of data, computational expense, and the generality of threats one has never heard of before. The approach of real-time big data processing techniques, optimization, and adaptive learning are the foundational elements of this revolutionary approach. Therefore, this paper pays more attention to how meta-learning can help rectify anomaly detection and malware identification while helping to allocate resources in cybersecurity better.