A Binary-Classification Method Based on Dictionary Learning and ADMM for Network Intrusion Detection

Yin, Xiu; Zhang, Yingzhou; Chen, Xinghao

doi:10.1109/cyberc.2017.88

Cited by 4 publications

(1 citation statement)

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“…Authors in [1] first explored the use of data mining techniques such as association rules and sequence analysis to establish an attack behavior mining model. From then on, embedding data mining technology in the traditional IDS has attracted more and more attention from researchers who focus on intrusion detection [2]- [13]. Data mining mainly includes regression, correlation analysis, classification, clustering and so on, among which clustering and classification receive more attention.…”

Section: Introductionmentioning

confidence: 99%

A Classification Algorithm Based on Data Clustering and Data Reduction for Intrusion Detection System over Big Data

2019

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With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

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Section: Introductionmentioning

confidence: 99%