AI-Powered Cyber Threat Detection: Leveraging Machine Learning for Real-Time Anomaly Identification and Threat Mitigation

Abstract

As human dependence on the use of information technology grows exponentially, so do the security challenges it poses. A wide variety of cybersecurity systems have been set in place to try to shield such technologies from unauthorized access and prevent sensitive data leakage, both in business contexts and within the public domain. Moreover, progress is being made to provide formal education and training in the field of cybersecurity, as this is a key element in mitigating the threat posed by an ever-evolving cyber risk landscape.

Despite these efforts, incidents of cyberattacks continue to frequently appear. These often take the form of zero-day attacks, with new threats emulating secure network communication patterns and using legitimate processes to remain undetected from traditional prevention systems. Moreover, an intricate attack perpetrated by a well-prepared malefactor can take weeks to be identified, thus causing severe damages well before any countermeasure is taken. Regarding this issue, both companies and nation-states are currently looking into innovative and more active ways to bolster the security of their systems, exploring AI-powered systems that have been long since exploited by malevolent actors.

“So far, cyber security solutions have been more like locks than keys. They are a sometimes-effective deterrent to the unskilled or the careless but not much more. Given time – or sufficiently advanced technology – any such device can be broken. The difficulty is in devising an ‘unbreakable’ one, and herein lies the problem.”