CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection
Source abstract
Abstract
Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Anomaly Detection Techniques and Applications · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Engineering, Institute of Disaster Prevention, Beijing 101601, China
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Funding
the Earthquake Science and Technology Spark Program Youth Project Research · the Fundamental Research Funds for the Central Universities · the Natural Science Project of Xinjiang University Scientific Research Program
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