ML Threat Detection with KDD Cup Data

Abstract

Artificial Intelligence (AI) has rapidly emerged as a transformative technology, with applications in numerous fields including security. With the increasing frequency and sophistication of security threats, such as cyber-attacks and malware, the need for effective methods to detect and prevent these threats is critical. Machine learning (ML), a subfield of AI, has been applied in this context, as its ability to analyze large amounts of data and identify patterns can be particularly useful in detecting security threats. In this empirical study, we examine the use of machine learning techniques for security threat detection, with the goal of advancing the understanding of the most effective approaches and techniques.