A novel intrusion detection system based on hierarchical clustering and support vector machines

Horng, Shi–Jinn; Su, Ming‐Yang; Chen, Yuan‐Hsin; Kao, Tzong-Wann; Chen, Rong-Jian; Lai, Jui-Lin; Perkasa, Citra Dwi

doi:10.1016/j.eswa.2010.06.066

Cited by 389 publications

(157 citation statements)

References 18 publications

(17 reference statements)

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“…The performance metrics of our proposed IDS are compared with other methods that are presented on other research papers (Table 19 and Table 20). We would like to note that there are some papers that used the KDD Test set with their proposed method [2], [39]- [41], but they included the http-tunnel attack into the U2R class [39], [40]. In fact, the http-tunnel attack is a Remoteto-Local (R2L) attack [26], which intends to gain local access from a remote machine.…”

Section: Hybrid Model and Experimental Resultsmentioning

confidence: 99%

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWith the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99

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Section: Hybrid Model and Experimental Resultsmentioning

confidence: 99%

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

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“…The basic probability distribution function is defined by (5) and (6). Finally, the combination in (2) is used to combine the modified evidences.…”

Section: )mentioning

confidence: 99%

“…Most of them are based on SVM technology which has a solid theoretical basis and can classify data records into multiple classes. Horng et al [6] proposed an SVM-based intrusion detection system based on a hierarchical clustering algorithm to preprocess dataset before training. The simple feature selection procedure was applied to eliminate unimportant features from the training set so that the obtained SVM model could classify the network traffic data more accurately.…”

Section: Introductionmentioning

confidence: 99%

Network Attack Classification and Recognition Using HMM and Improved Evidence Theory

Luo¹,

Wen²,

Xiang³

2016

ijacsa

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Abstract-In this paper, a decision model of fusion classification based on HMM-DS is proposed, and the training and recognition methods of the model are given. As the pure HMM classifier can't have an ideal balance between each model with a strong ability to identify its target and the maximum difference between models. So in this paper, the results of HMM are integrated into the DS framework, and HMM provides state probabilities for DS. The output of each hidden Markov model is used as a body of evidence. The improved evidence theory method is proposed to fuse the results and encounter drawbacks of the pure HMM for improving classification accuracy of the system. We compare our approach with the traditional evidence theory method, other representative improved DS methods, pure HMM method and common classification methods. The experimental results show that our proposed method has a significant practical effect in improving the training process of network attack classification with high accuracy.

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“…Horng et al [2] Proposed novel intrusion detection on hierarchical clustering and support vector machines. The Hierarchical clustering algorithm is used to obtain fewer, abstracted, and higher-qualified training instances from the KDD 99 dataset.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Effect of Clustering the Training Data in Naive Bayes Classifier for Anomaly Network Intrusion Detection

Subramanian¹,

Ong²

2014

JACN

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Abstract-This paper presents the analysis of the effect of clustering the training data and test data in classification efficiency of Naive Bayes classifier. KDD cup 99 benchmark dataset is used in this research. The training set is clustered using k means clustering algorithm into 5 clusters. Then 8800 samples are taken from the clusters to form the training and test set. The results are compared with that of two Naive Bayes classifiers trained on random sampled data containing 8800 and 17600 instances respectively. The main contribution of this paper is that it is empirically proved that the training set derived from clusters generated by k-means clustering algorithm improves the classification efficiency of the Naive Bayes classifier. The results show the accuracy of the Naive Bayes classifier trained with clustered instances is 94.4% while that of normal instances are 85.41% and 89.26%.

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A novel intrusion detection system based on hierarchical clustering and support vector machines

Cited by 389 publications

References 18 publications

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Network Attack Classification and Recognition Using HMM and Improved Evidence Theory

Analysis of the Effect of Clustering the Training Data in Naive Bayes Classifier for Anomaly Network Intrusion Detection

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