A Novel Hybrid LE and SVM with CV in Intrusion Detection

Abstract:

A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 2572-2576
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.2572
• Online since: September 2014
• Authors: Qing Liu*, Yun Kai Zhang, Qing Ru Li
• Keywords: Cross Validation, Intrusion Detection, Laplacian Eigenmaps, Maximum Likelihood-Estimation (MLE), Support Vector Machine (SVM)

References

[1] Om H, Kundu A. A hybrid system for reducing the false alarm rate of anomaly intrusion detection system[C]/Recent Advances in Information Technology (RAIT), 2012 1st International Conference on. IEEE, 2012: 131-136.

DOI: 10.1109/rait.2012.6194493

[2] Yanxue Z, Dongmei Z, Jinxing L. Approach to Forecasting Multi-step Attack Based on Fuzzy Hidden Markov Model[J]. Journal of Applied Sciences, 2013, 13(22).

DOI: 10.3923/jas.2013.4955.4960

[3] De Castro L N, Timmis J. Artificial immune systems: a new computational intelligence approach[M]. Springer, (2002).

[4] Hashem S H. Efficiency of Svm and Pca to Enhance Intrusion Detection System[J]. Journal of Asian Scientific Research, 2013, 3(4): 381-395.

[5] Eskin E. Anomaly detection over noisy data using learned probability distributions[J]. (2000).

[6] Shon T, Kim Y, Lee C, et al. A machine learning framework for network anomaly detection using SVM and GA[C]/Information Assurance Workshop, 2005. IAW'05. Proceedings from the Sixth Annual IEEE SMC. IEEE, 2005: 176-183.

DOI: 10.1109/iaw.2005.1495950

[7] Srinoy S. Intrusion detection model based on particle swarm optimization and support vector machine[C]/Computational Intelligence in Security and Defense Applications, 2007. CISDA 2007. IEEE Symposium on. IEEE, 2007: 186-192.

DOI: 10.1109/cisda.2007.368152

[8] Horng S J, Su M Y, Chen Y H, et al. A novel intrusion detection system based on hierarchical clustering and support vector machines[J]. Expert systems with Applications, 2011, 38(1): 306-313.

DOI: 10.1016/j.eswa.2010.06.066

[9] Kavitha B, Karthikeyan D S, Sheeba Maybell P. An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier[J]. Knowledge-Based Systems, 2012, 28: 88-96.

DOI: 10.1016/j.knosys.2011.12.004

[10] Wu S X, Banzhaf W. The use of computational intelligence in intrusion detection systems: A review[J]. Applied Soft Computing, 2010, 10(1): 1-35.

DOI: 10.1016/j.asoc.2009.06.019

[11] Kolias C, Kambourakis G, Maragoudakis M. Swarm intelligence in intrusion detection: A survey[J]. computers & security, 2011, 30(8): 625-642.

DOI: 10.1016/j.cose.2011.08.009

[12] Kuang F, Xu W, ZHANG S, et al. A novel approach of KPCA and SVM for intrusion detection[J]. Journal of Computational Information Systems, 2012, 8(8): 3237-3244.