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首頁> 外文期刊>Expert Systems with Application >Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
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Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

機譯：基于改進的K均值的入侵檢測系統(tǒng)多級混合支持向量機和極限學(xué)習(xí)機

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Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small training datasets representing the entire original training dataset, significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far. (C) 2016 Elsevier Ltd. All rights reserved.
機譯：由于計算機之間的連接不斷增加，入侵檢測已成為網(wǎng)絡(luò)安全所必需的。已經(jīng)開發(fā)了幾種入侵檢測系統(tǒng)，以使用不同的統(tǒng)計方法和機器學(xué)習(xí)技術(shù)來保護網(wǎng)絡(luò)。本研究旨在設(shè)計一個模型，處理數(shù)據(jù)分析中的實際入侵檢測問題，并將網(wǎng)絡(luò)數(shù)據(jù)分類為正常和異常行為。這項研究提出了一種多層次的混合入侵檢測模型，該模型使用支持向量機和極限學(xué)習(xí)機來提高檢測已知和未知攻擊的效率。還提出了一種改進的K-means算法來構(gòu)建高質(zhì)量的訓(xùn)練數(shù)據(jù)集，該數(shù)據(jù)集對改善分類器的性能做出了重要貢獻。改進后的K-means用于構(gòu)建代表整個原始訓(xùn)練數(shù)據(jù)集的新的小型訓(xùn)練數(shù)據(jù)集，顯著減少分類器的訓(xùn)練時間，并提高入侵檢測系統(tǒng)的性能。流行的KDD Cup 1999數(shù)據(jù)集用于評估提出的模型。與基于相同數(shù)據(jù)集的其他方法相比，該模型具有較高的攻擊檢測效率，其準確性（95.75％）是迄今為止的最佳性能。 （C）2016 Elsevier Ltd.保留所有權(quán)利。

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