Enhanced Intrusion Detection Systems Using a Knowledge Graph-Based Approach

The integration of intrusion detection systems as critical components of network security is required. To enhance the effectiveness of intrusion detection systems, the advent of machine learning highlights the necessity for enhanced methods to manage intrusion detection data. These machine learning approaches have difficulties for recognizing complicated assault patterns. However, these systems often produce a significant number of false positives., particularly when dealing with unknown assaults. Given this situation, it is critical to improve the capabilities of intrusion detection systems by leveraging the power of knowledge graphs. Knowledge graphs have developed as an effective technique for modeling and analyzing complicated interactions in security data. The goal of this article is to investigate an approach for integrating Knowledge Graph and Machine Learning/Deep Learning by leveraging Knowledge Graph's semantic modeling, querying, and reasoning capabilities while addressing the challenges of intelligent detection and decision-making in cybersecurity.