Advanced Artificial Intelligence Methods in Cybersecurity, Threat and Anomaly Detection Using Unsupervised Learning Techniques

Abstract

Artificial intelligence (AI) is playing an increasingly important role in cybersecurity, enabling faster and more effective detection and response to threats. One of them is the detection of threats and anomalies.

Machine learning algorithms process vast amounts of data in real time, detecting unusual patterns that may indicate potential attacks (e.g., DDoS attacks, intrusions, or network scanning attempts).

AI-based systems learn what behaviours are the norm for a given environment and then flag any deviations, which can help identify new, unknown threats. The first part discusses the use of machine learning algorithms in the environment of real data. The following parts discuss anomalies in network traffic and the possibilities of using ML techniques, as well as the initial process of data collection and preparation.