Journal
KNOWLEDGE-BASED SYSTEMS
Volume 203, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2020.106167
Keywords
Intrusion detection; K-means; Quantum-inspired ant lion optimized; Cluster analysis
Categories
Funding
- National Natural Science Foundation of China [61972438]
- Key Research and Development Projects in Anhui Province [202004a05020002]
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Intrusion detection maintains network security by detecting intrusion behaviors. There are many clustering algorithms that can be used directly for intrusion detection. K-means is a simple and efficient method used in data clustering. However, k-means has a tendency to converge to local optima and depends on the initial value of cluster centers. Therefore, we present an efficient hybrid clustering algorithm referred to as QALO-K, whereby, we combine k-means with quantum-inspired ant lion optimized. This algorithm combines the advantages of quantum computing and swarm intelligence algorithms to improve the k-means algorithm and make the k-means algorithm converge towards the global optimal direction. Our proposed algorithm is tested on several standard datasets from UCI Machine Learning Repository for cluster analysis and its performance is compared with other well-known algorithms. The proposed method was applied on KDD Cup 99 large datasets for intrusion detection. The simulation results infer that the proposed algorithms can be efficiently used for data clustering and intrusion detection. (C) 2020 Elsevier B.V. All rights reserved.
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