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Recenzirano
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Li, Yinhui; Xia, Jingbo; Zhang, Silan; Yan, Jiakai; Ai, Xiaochuan; Dai, Kuobin
Expert systems with applications, 2012, 2012-1-00, 20120101, Letnik: 39, Številka: 1Journal Article
► This paper proposes a desirable IDS model with high efficiency and accuracy. ► It formulates a pipeline of machine learning methods, including k-means algorithm, ant colony optimization (ACO) and SVM. ► The accuracy achieves 98.6249%, and the average Matthews correlation coefficient (MCC) achieves 0.861161. The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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