ENIDrift: A Fast and Adaptive Ensemble System for Network Intrusion Detection under Real-world Drift
Xian Wang
Abstract
Machine Learning (ML) techniques have been widely applied for network intrusion detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer from fundamental limitations that hinder them from being deployed in the real world. They consider a narrow scope rather than real-world drift that involves dynamically distributed network packets and well-crafted ML attacks. Besides, they pose high runtime overhead and have low processing speed.
Topics & Concepts
Intrusion detection systemComputer scienceNetwork packetOverhead (engineering)Concept driftIntrusionReal-time computingDistributed computingArtificial intelligenceComputer networkMachine learningOperating systemData stream miningGeochemistryGeologyNetwork Security and Intrusion DetectionData Stream Mining TechniquesAnomaly Detection Techniques and Applications