MARK-ELM: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection

Expert Systems with Applications

Volumn 42, Issue 8, 2015, Pages 4062-4080

  Fossaceca, John M ^a   Mazzuchi, Thomas A ^a   Sarkani, Shahram ^a  

^a The George Washington University   (United States)

Author keywords

Adaptive Boosting; Cyber security; Ensemble Learning; Extreme Learning Machine; Fractional Polynomial Kernels; KDD Cup 1999; Kernel Selection; Machine Learning; Multiclass Classification; Multiple Kernel Learning; Network Intrusion Detection

Indexed keywords

ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NETWORK SECURITY; NEURAL NETWORKS; SECURITY OF DATA; SUPPORT VECTOR MACHINES;

CYBER SECURITY; ENSEMBLE LEARNING; EXTREME LEARNING MACHINE; FRACTIONAL POLYNOMIALS; KDD CUP 1999; KERNEL SELECTION; MULTI-CLASS CLASSIFICATION; MULTIPLE KERNEL LEARNING; NETWORK INTRUSION DETECTION;

INTRUSION DETECTION;

EID: 84961308100     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.12.040     Document Type: Article

References (91)

1. M.S. Abadeh, H. Mohamadi, and J. Habibi Design and analysis of genetic fuzzy systems for intrusion detection in computer networks Expert Systems with Applications 38 6 2011 7067 7075
2. A. Ahmim, and N. Ghoualmi-Zine A new fast and high performance intrusion detection system International Journal of Security and Its Applications 7 5 2013
3. Bache, K.; Lichman, M. (2013). UCI machine learning repository. < http://archive. ics. uci. edu/ml > (accessed August 2013).
4. V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset Expert Systems with Applications 38 5 2011 5947 5957
5. C. Cai, H. Pan, and G. Cheng Fusion of BVM and ELM for anomaly detection in computer networks 2012 International conference on computer science & service system (CSSS) 2012, August IEEE 1957 1960
6. J.J. Cao, S. Kwong, R. Wang, and K. Li A weighted voting method using minimum square error based on Extreme Learning Machine 2012 International conference on machine learning and cybernetics (ICMLC) 2012, July IEEE 411 414 Vol. 1
7. J. Cao, Z. Lin, G.B. Huang, and N. Liu Voting based extreme learning machine Information Sciences 185 1 2012 66 77
8. C.C. Chang, and C.J. Lin LIBSVM: A library for support vector machines ACM Transactions on Intelligent Systems and Technology (TIST) 2 3 2011 27 < http://www.csie.ntu.edu.tw/~cjlin/libsvm > (accessed October 2013)
9. Y. Chen, A. Abraham, and B. Yang Hybrid flexible neural tree based intrusion detection systems International Journal of Intelligent Systems 22 4 2007 337 352
10. T. Chen, X. Zhang, S. Jin, and O. Kim Efficient classification using parallel and scalable compressed model and its application on intrusion detection Expert Systems with Applications 41 13 2014 5972 5983
11. C. Cheng, W.P. Tay, and G.B. Huang Extreme learning machines for intrusion detection The 2012 International joint conference on neural networks (IJCNN) 2012, June IEEE 1 8
12. J. Chorowski, J. Wang, and J.M. Zurada Review and performance comparison of SVM-and ELM-based classifiers Neurocomputing 128 2014 507 516
13. T.-S. Chou, J. Fan, S. Fan, and K. Makki Ensemble of machine learning algorithms for intrusion detection IEEE International conference on systems, man and cybernetics, 2009 SMC 2009 2009 IEEE 3976 3980
14. G. Creech, and J. Hu A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns IEEE Transactions on Computers 63 4 2013 513 529
15. G. Creech, and F. Jiang The application of extreme learning machines to the network intrusion detection problem Numerical analysis and applied mathematics ICNAAM 2012: International conference of numerical analysis and applied mathematics Vol. 1479 (1) 2012, September AIP Publishing 1506 1511
16. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press
17. G.P.M. de Farias, A.L. de Oliveira, and G.G. Cabral Extreme learning machines for intrusion detection systems Neural information processing 2012, January Springer Berlin Heidelberg 535 543
18. W. Deng, Q. Zheng, and K. Zhang Reduced kernel extreme learning machine Proceedings of the 8th international conference on computer recognition systems CORES 2013 2013, January Springer International Publishing 63 69
19. D.E. Denning An intrusion-detection model IEEE Transactions on Software Engineering 2 1987 222 232
20. Denning, D. E.; Neumann, P. G. (1985). Requirements and model for IDES - A real-time intrusion detection expert system. Document A005, SRI International, p. 333.
21. O. Depren, M. Topallar, E. Anarim, and M.K. Ciliz An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks Expert Systems with Applications 29 4 2005 713 722
22. S. Ding, X. Xu, and R. Nie Extreme learning machine and its applications Neural Computing and Applications 2013 1 8
23. S. Ding, Y. Zhang, X. Xu, and L. Bao A novel extreme learning machine based on hybrid kernel function Journal of Computers 8 8 2013 2110 2117
24. S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie Extreme learning machine: Algorithm, theory and applications Artificial Intelligence Review 2013 1 13
25. S. Elhag, A. Fernández, A. Bawakid, S. Alshomrani, and F. Herrera On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems Expert Systems with Applications 42 1 2015 193 202
26. V. Engen, J. Vincent, and K. Phalp Exploring discrepancies in findings obtained with the KDD Cup'99 data set Intelligent Data Analysis 15 2 2011 251 276
27. D.M. Farid, N. Harbi, and M.Z. Rahman Combining Naïve Bayes and decision tree for adaptive intrusion detection International Journal of Network Security and Its Applications 2 2 2010
28. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1 1997 119 139
29. H. Fu, C.M. Vong, P.K. Wong, and Z. Yang Fast detection of impact location using kernel extreme learning machine Neural Computing and Applications 2014 1 10
30. Gartner Press Release (2013, December): Gartner says the internet of things installed base will grow to 26 billion units by 2020. < http://www.gartner.com/newsroom/id/2636073 > (accessed June 2014).
31. M. Gönen, and E. AlpaydIn Multiple kernel learning algorithms The Journal of Machine Learning Research 12 2011 2211 2268
32. M.A.M. Hasan, M. Nasser, B. Pal, and S. Ahmad Support vector machine and random forest modeling for intrusion detection system (IDS) Journal of Intelligent Learning Systems and Applications 6 2014 45 52
33. S. Haykin Neural networks: A comprehensive foundation 2nd ed. 1999 Prentice Hall Upper Saddle River
34. Hettich, S.; Bay, S. D. (2013). KDD Cup dataset (1999): UCI KDD archive. < http://kdd.ics.uci.edu//databases/kddcup99/kddcup99.html > (accessed May 2013).
35. A.E. Hoerl, and R.W. Kennard Ridge regression: Biased estimation for nonorthogonal problems Technometrics 12 1 1970 55 67
36. Hoi, Steven C. H. (2013). MKBoost matlab source code. < http://www.cais.ntu.edu.sg/~chhoi/mkboost/code/MKBoost.zip > (accessed February 2013).
37. S.J. Horng, M.Y. Su, Y.H. Chen, T.W. Kao, R.J. Chen, and J.L. Lai A novel intrusion detection system based on hierarchical clustering and support vector machines Expert Systems with Applications 38 1 2011 306 313
38. Hsu, C. W.; Chang, C. C.; Lin, C. J. (2003). A practical guide to support vector classification. < http://ntu.csie.org/~cjlin/papers/guide/guide.pdf > (accessed February 2014).
39. C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13 2 2002 415 425
40. W. Hu, J. Gao, Y. Wang, O. Wu, and S. Maybank Online adaboost-based parameterized methods for dynamic distributed network intrusion detection IEEE Transactions on Systems, Man, and Cybernetics, Part B 44 1 2014 66 82
41. W. Hu, W. Hu, and S. Maybank AdaBoost-based algorithm for network intrusion detection IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38 2 2008 577 583
42. Huang, G. B. (2012, June). Extreme learning machine: learning without iterative tuning. A tutorial in IJCNN2012/WCCI2012, Brisbane, Australia. < http://www.ntu.edu.sg/home/egbhuang/pdf/ELM-WCCI2012.pdf > (accessed on July 15, 2013).
43. G.B. Huang An insight into extreme learning machines: Random neurons, random features and kernels Cognitive Computation 2014 1 15
44. Huang, G.B. (2014b). Extreme Learning Machine Source Code. < http://www.ntu.edu.sg/home/egbhuang/elm-codes.html > (accessed July 2013).
45. G.B. Huang, Y.Q. Chen, and H.A. Babri Classification ability of single hidden layer feedforward neural networks IEEE Transactions on Neural Networks 11 3 2000 799 801
46. G.B. Huang, L. Chen, and C.K. Siew Universal approximation using incremental constructive feedforward networks with random hidden nodes IEEE Transactions on Neural Networks 17 4 2006 879 892
47. W. Huang, N. Li, Z. Lin, G.B. Huang, W. Zong, and J. Zhou Liver tumor detection and segmentation using kernel-based extreme learning machine 35th annual international conference of the IEEE Engineering in medicine and biology society (EMBC) 2013, July IEEE 3662 3665
48. G.B. Huang, D.H. Wang, and Y. Lan Extreme learning machines: A survey International Journal of Machine Learning and Cybernetics 2 2 2011 107 122
49. G.B. Huang, H. Zhou, X. Ding, and R. Zhang Extreme learning machine for regression and multiclass classification IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 2 2012 513 529
50. G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine: A new learning scheme of feedforward neural networks 2004 ieee international joint conference on neural networks, 2004. Proceedings 2 2004, July IEEE 985 990
51. V. Jaiganesh, S. Mangayarkarasi, and P. Sumathi Intrusion detection systems: A survey and analysis of classification techniques International Journal of Advanced Research in Computer and Communication Engineering 2 2013 1629 1635
52. V. Jaiganesh, S. Mangayarkarasi, and P. Sumathi An efficient algorithm for network intrusion detection system International Journal of Computer Applications 90 12 2014 12 16
53. V. Jaiganesh, and P. Sumathi Kernelized extreme learning machine with Levenberg-Marquardt learning approach towards intrusion detection International Journal of Computer Applications 54 14 2012 38 44
54. I. Kang, M.K. Jeong, and D. Kong A differentiated one-class classification method with applications to intrusion detection Expert Systems with Applications 39 4 2012 3899 3905
55. KDD Website (2014). < http://www.kdd.org/kddcup/index.php > (accessed January 2014).
56. Koc, L. (2013, January). Application of a Hidden Bayes Naive multiclass classifier in network intrusion detection [Doctoral dissertation]. The George Washington University.
57. L. Koc, T.A. Mazzuchi, and S. Sarkani A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier Expert Systems with Applications 39 18 2012 13492 13500
58. S. Lee, G. Kim, and S. Kim Self-adaptive and dynamic clustering for online anomaly detection Expert Systems with Applications 38 12 2011 14891 14898
59. W.J. Lee, S. Verzakov, and R.P. Duin Kernel combination versus classifier combination Multiple classifier systems 2007 Springer Berlin Heidelberg 22 31
60. Y. Li, J. Xia, S. Zhang, J. Yan, X. Ai, and K. Dai An efficient intrusion detection system based on support vector machines and gradually feature removal method Expert Systems with Applications 39 1 2012 424 430
61. C. Liu Gabor-based kernel PCA with fractional power polynomial models for face recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 26 5 2004 572 581
62. X. Liu, C. Gao, and P. Li A comparative analysis of support vector machines and extreme learning machines Neural Networks 33 2012 58 66
63. Y. Liu, H.T. Loh, and S.B. Tor Comparison of extreme learning machine with support vector machine for text classification Innovations in Applied Artificial Intelligence 2005 390 399
64. M.M. Mazid, A.S. Ali, and K.S. Tickle A comparison between rule based and association rule mining algorithms Third international conference on network and system security, 2009. NSS'09 2009, October IEEE 452 455
65. J. McHugh Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory ACM Transactions on Information and System Security 3 4 2000 262 294
66. M.S. Mok, S.Y. Sohn, and Y.H. Ju Random effects logistic regression model for anomaly detection Expert Systems with Applications 37 10 2010 7162 7166
67. S. Mukkamala, A.H. Sung, and A. Abraham Intrusion detection using an ensemble of intelligent paradigms Journal of Network and Computer Applications 28 2 2005 167 182
68. H.A. Nguyen, and D. Choi Application of data mining to network intrusion detection: Classifier selection model Challenges for next generation network operations and service management 2008 Springer Berlin Heidelberg 399 408
69. NSL-KDD Data-Set for Network-Based Intrusion Detection Systems. (accessed February, 2014) < http://nsl.cs.unb.ca/NSL-KDD/ >, March 2009.
70. R. Patel, A. Thakkar, and A. Ganatra A survey and comparative analysis of data mining techniques for network intrusion detection systems International Journal of Soft Computing and Engineering (IJSCE) ISSN 2012 2231 2307
71. P. Phoungphol, Y. Zhang, and Y. Zhao Robust multiclass classification for learning from imbalanced biomedical data Tsinghua Science and Technology 17 6 2012 619 628
72. R. Polikar Ensemble based systems in decision making IEEE Circuits and Systems Magazine 6 3 2006 21 45
73. D.M. Powers Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation Journal of Machine Learning Technologies 2 1 2011 37 63
74. R. Rossius, G. Zenker, A. Ittner, and W. Dilger A short note about the application of polynomial kernels with fractional degree in Support Vector Learning Machine Learning: ECML-98 1998 Springer Berlin Heidelberg 143 148
75. Sabhnani, M.; Serpen, G. (2003, June). Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context. In MLMTA (pp. 209-215).
76. R.E. Schapire The strength of weak learnability Machine Learning 5 2 1990 197 227
77. B. Schölkopf, and A.J. Smola Learning with kernels: Support vector machines, regularization, optimization, and beyond 2002 MIT Press
78. S.S.S. Sindhu, S. Geetha, and A. Kannan Decision tree based light weight intrusion detection using a wrapper approach Expert Systems with Applications 39 1 2012 129 141
79. M. Sokolova, and G. Lapalme A systematic analysis of performance measures for classification tasks Information Processing and Management 45 4 2009 427 437
80. R. Sommer, and V. Paxson Outside the closed world: On using machine learning for network intrusion detection 2010 IEEE symposium on security and privacy (SP) 2010, May IEEE 305 316
81. G. Song, X. Jin, G. Chen, and Y. Nie Multiple kernel learning method for network anomaly detection 2010 International conference on intelligent systems and knowledge engineering (ISKE) 2010, November IEEE 296 299
82. L.J. Su, and M. Yao Extreme learning machine with multiple kernels 2013 10th IEEE international conference on control and automation (ICCA) 2013, June IEEE 424 429
83. Y. Sun, M.S. Kamel, A.K. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 40 12 2007 3358 3378
84. S. Tabibian, A. Akbari, and B. Nasersharif Extension of a kernel-based classifier for discriminative spoken keyword spotting Neural Processing Letters 39 2 2014 195 218
85. Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A. A. (2009). A detailed analysis of the KDD Cup 99 data set. In Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009.
86. G. Wang, J. Hao, J. Ma, and L. Huang A new approach to intrusion detection using artificial neural networks and fuzzy clustering Expert Systems with Applications 37 9 2010 6225 6232
87. X.K. Wei, Y.H. Li, and Y. Feng Comparative study of extreme learning machine and support vector machine Advances in neural networks-ISNN 2006 2006 Springer Berlin Heidelberg 1089 1095
88. Weller-Fahy, David J. 2013. Network Intrusion Dataset Assessment. No. AFIT-ENG-13-M-49. Air Force Institute of Technology Wright-Patterson AFB Ohio Graduate School of Engineering and Management.
89. H. Xia, and S.C. Hoi MkBoost: A framework of multiple kernel boosting IEEE Transactions on Knowledge and Data Engineering 25 7 2013 1574 1586
90. X. Xu Adaptive intrusion detection based on machine learning: Feature extraction, classifier construction and sequential pattern prediction International Journal of Web Services Practices 2 1-2 2006 49 58
91. J. Zhu, H. Zou, S. Rosset, and T. Hastie Multi-class adaboost Statistics and Its Interface 2 2009 349 360