A Comprehensive Survey on Cognitive Cyber Security Analysis Using Machine Learning Approaches

Abstract

Cybersecurity threats have evolved in sophistication, necessitating the development of intelligent and adaptive defense mechanisms. Cognitive cybersecurity systems present the opportunity to provide answers to threat detection, vulnerability analysis, and anomaly detection since they combine the power of artificial intelligence (AI) with human knowledge. Ensemble machine learning is especially effective among AI methods because it will be able to enable prediction stability and accuracy through the aggregation of multiple classifiers. However, dynamic vulnerability assessments are difficult to perform on heterogeneous data volumes created in security operations centers, relying only on real-time data. The vulnerability scan is done manually, and it demands highly skilled analysts who need to go through a time-consuming process with cumulative results, and therefore the security repositories available still carry too much inconsistent information, and this leads to wastage of time in detecting the flaws. To address these issues, this paper proposes a cognitive cybersecurity framework powered by ensemble machine learning for real-time vulnerability detection and dynamic threat assessment. The framework aims to enhance accuracy, reduce redundancy in vulnerability reporting, and improve the efficiency of security analysis.