Advanced Intrusion Detection Systems Leveraging Knowledge Graph-Based Techniques
Mohamed Amine Daoud, Sid Ahmed Mokhtar Mostefaoui, Hadj Madani Meghazi, Ourida LABBADI, Chaima Zenina, Abdelkader Bouguessa · 2024
The integration of intrusion detection systems (IDS) is crucial for strengthening network security. Improving IDS performance requires advanced techniques for handling intrusion detection data, with machine learning playing a key role. However, machine learning methods often face difficulties in detecting complex attack patterns and are prone to generating a high number of false positives, especially when dealing with unknown attacks. To overcome these issues, enhancing IDS capabilities with knowledge graphs is essential. Knowledge graphs have proven to be valuable tools for modeling and analyzing complex interactions within security data. This article proposes an approach that combines knowledge graphs with machine learning and deep learning. By harnessing the semantic modeling, querying, and reasoning capabilities of knowledge graphs, this integration aims to address the challenges of intelligent detection and decision-making in cybersecurity.