A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Information Sciences

Volumn 294, Issue , 2015, Pages 95-108

  Costa, Kelton A P ^a   Pereira, Luis A M ^b   Nakamura, Rodrigo Y M ^a   Pereira, Clayton R ^c   Papa, João P ^a   Xavier Falcão, Alexandre ^b  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

^c FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

Author keywords

Clustering; Intrusion detection; Meta heuristic; Optimum path forest

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTER NETWORKS; FORESTRY; GRAPH THEORY; MOTION COMPENSATION; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; SECURITY OF DATA;

CLUSTERING; ITS APPLICATIONS; K-NEAREST NEIGHBORS; META-HEURISTIC TECHNIQUES; METAHEURISTIC; OPTIMUM-PATH FORESTS; PROBABILITY DENSITY FUNCTION (PDF); SELF-ORGANIZATION MAPS;

INTRUSION DETECTION;

ALGORITHMS; FORESTS; PROBABILITY;

EID: 84961288567     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.09.025     Document Type: Article

References (54)

1. Leman Akoglu, and Christos Faloutsos Anomaly, event, and fraud detection in large network datasets Proceedings of the Sixth ACM International Conference on Web Search and Data Mining WSDM'13 2013 ACM 773 774
2. Fernando Bação, Victor Lobo, Marco Painho, Self-Organizing Maps as Substitutes for k-Means Clustering, 2005, pp. 476-483.
3. J. Cannady, Artificial neural networks for misuse detection, in: Proceedings of the National Information Systems Security Conference, 1998, pp. 443-456.
4. F.A.M. Cappabianco, A.X. Falcão, C.L. Yasuda, and J.K. Udupa Brain tissue mr-image segmentation via optimum-path forest clustering Comput. Vis. Image Und. 116 10 2012 1047 1059
5. S. Chabaa, A. Zeroual, and J. Antari Identification and prediction of internet traffic using artificial neural networks J. Intell. Learn. Syst. Appl. 2 3 2010 147 155
6. R. Chaki, N. Chaki, IDSX: a cluster based collaborative intrusion detection algorithm for mobile ad-hoc network, in: Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007, pp. 179-184.
7. W.-H. Chen, S.-H. Hsu, and H.-P. Shen Application of SVM and ANN for intrusion detection Comput. Oper. Res. 32 10 2005 2617 2634
8. D. Comaniciu An algorithm for data-driven bandwidth selection IEEE Trans. Pattern Anal. Mach. Intell. 25 2 2003 281 288
9. Igino Corona, Giorgio Giacinto, and Fabio Roli Adversarial attacks against intrusion detection systems: taxonomy, solutions and open issues Inf. Sci. 239 0 2013 201 225
10. Eleazar Eskin Anomaly detection over noisy data using learned probability distributions Proceedings of the International Conference on Machine Learning 2000 Morgan Kaufmann 255 262
11. A.X. Falcão, J. Stolfi, and R.A. Lotufo The image foresting transform theory, algorithms, and applications IEEE Trans. Pattern Anal. Mach. Intell. 26 1 2004 19 29
12. Iztok Fister, Iztok Fister Jr., Xin-She Yang, and Janez Brest A comprehensive review of firefly algorithms Swarm Evol. Comput. 13 0 2013 34 46
13. Z.W. Geem Music-Inspired Harmony Search Algorithm: Theory and Applications first ed. 2009 Springer Publishing Company, Incorporated
14. A.K. Ghosh, J. Wanken, F. Charron, Detecting anomalous and unknown intrusions against programs, in: Proceedings of the Annual Computer Security Application Conference, 1998, pp. 259-267.
15. Yu Guan, Ali A. Ghorbani, Y-means: a clustering method for intrusion detection, in: Proceedings of Canadian Conference on Electrical and Computer Engineering, 2003, pp. 1083-1086.
16. X. Haijun, P. Fang, W. Ling, L. Hongwei, Ad hoc-based feature selection and support vector machine classifier for intrusion detection, in: Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007, pp. 18-20.
17. David Halliday, Robert Resnick, and Jearl Walker Extended, Fundamentals of Physics 6th ed. 2000 Wiley
18. S. Haykin Neural Networks: A Comprehensive Foundation 1998 Prentice-Hall
19. K.R.L. Jucá, A. Boukerche, and J.B.M. Sobral Intrusion detection based on the immune human system Proceedings of the 16th International Parallel and Distributed Processing Symposium 2002 IEEE Computer Society Washington, DC, USA 187
20. D.-K. Kang, D. Fuller, and V. Honavar Learning classifiers for misuse detection using a bag of system calls representation Proceedings of IEEE International Conference on Intelligence and Security Informatics vol. 3495 2005 Springer-Verlag Berlin Heidelberg 511 516
21. H.G. Kayacik, A.N.Z. Heywood, and I.M. Heywood A hierarchical SOM-based intrusion detection system Eng. Appl. Artif. Intell. 20 4 2007 439 451
22. J. Kennedy, and R.C. Eberhart Swarm Intelligence 2001 M. Kaufman
23. Gisung Kim, Seungmin Lee, and Sehun Kim A novel hybrid intrusion detection method integrating anomaly detection with misuse detection Expert Syst. Appl. 41 4, Part 2 2014 1690 1700
24. J.Z. Lei, and A.A. Ghorbani Network intrusion detection using an improved competitive learning neural network Proceedings of the 2nd Annual Conference on Communication Networks and Services Research 2004 IEEE Computer Society 190 197
25. H. Li, and D. Gu A novel intrusion detection scheme using support vector machine fuzzy network for mobile ad hoc networks Proceedings of the Pacific-Asia Conference on Web Mining and Web-based Application 0 2009 47 50
26. Hung-Jen Liao, Chun-Hung Richard Lin, Ying-Chih Lin, and Kuang-Yuan Tung Intrusion detection system: a comprehensive review J. Network Comput. Appl. 36 1 2013 16 24
27. Reza Mansouri, Forough Nasseri, and Mohammad Khorrami Effective time variation of g in a model universe with variable space dimension Phys. Lett. A 259 34 1999 194 200
28. Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Hiren Patel, Avi Patel, and Muttukrishnan Rajarajan A survey of intrusion detection techniques in cloud J. Network Comput. Appl. 36 1 2013 42 57
29. J.P. Papa, A.X. Falcão, V.H.C. Albuquerque, and J.M.R.S. Tavares Efficient supervised optimum-path forest classification for large datasets Pattern Recogn. 45 1 2012 512 520
30. J.P. Papa, A.X. Falcão, and C.T.N. Suzuki Supervised pattern classification based on optimum-path forest Int. J. Imaging Syst. Technol. 19 2 2009 120 131
31. J.P. Papa, C.T.N. Suzuki, A.X. Falcão, LibOPF: A Library for the Design of Optimum-Path Forest Classifiers, 2014. Software version 2.1. < http://www.ic.unicamp.br/∼afalcao/LibOPF >.
32. Ahmed Patel, Mona Taghavi, Kaveh Bakhtiyari, and Joaquim Celestino Júnior An intrusion detection and prevention system in cloud computing: a systematic review J. Network Comput. Appl. 36 1 2013 25 41
33. C.R. Pereira, R.Y.M. Nakamura, K.A.P. Costa, and João P. Papa An optimum-path forest framework for intrusion detection in computer networks Eng. Appl. Artif. Intell. 25 6 2012 1226 1234
34. Leonid Portnoy, Eleazar Eskin, Sal Stolfo, Intrusion detection with unlabeled data using clustering, in: Proceedings of ACM CSS Workshop on Data Mining Applied to Security, 2001, pp. 5-8.
35. S. Rana, S. Jasola, and R. Kumar A boundary restricted adaptive particle swarm optimization for data clustering Int. J. Mach. Learn. Cybern. 2012 1 10
36. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi GSA: a gravitational search algorithm Inf. Sci. 179 13 2009 2232 2248
37. L.M. Rocha, F.A.M. Cappabianco, and A.X. Falcão Data clustering as an optimum-path forest problem with applications in image analysis Int. J. Imaging Syst. Technol. 19 2 2009 50 68
38. P.T.M. Saito, P.J. de Rezende, A.X. Falcão, C.T.N. Suzuki, and J.F. Gomes A data reduction and organization approach for efficient image annotation Proceedings of the 28th Symposium on Applied Computing 2013 ACM Coimbra, Portugal 53 57
39. Khushboo Satpute, Shikha Agrawal, Jitendra Agrawal, and Sanjeev Sharma A survey on anomaly detection in network intrusion detection system using particle swarm optimization based machine learning techniques Suresh Chandra Satapathy, Siba K. Udgata, Bhabendra Narayan Biswal, Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) Advances in Intelligent Systems and Computing vol. 199 2013 Springer Berlin Heidelberg 441 452
40. J. Sen, An intrusion detection architecture for clustered wireless ad hoc networks, in: Proceedings of the Second International Conference on Computational Intelligence, Communication Systems and Networks, July 2010, pp. 202-207.
41. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 22 8 2000 888 905
42. Ali Shiravi, Hadi Shiravi, Mahbod Tavallaee, and Ali A. Ghorbani Toward developing a systematic approach to generate benchmark datasets for intrusion detection Comput. Secur. 31 3 2012 357 374
43. A. Sperotto, R. Sadre, D.F. van Vliet, and A. Pras A labeled data set for flow-based intrusion detection Proceedings of the 9th IEEE International Workshop on IP Operations and Management Lect. Notes Comput. Sci. vol. 5843 2009 Springer Verlag 39 50
44. M. Tavallaee, E. Bagheri, W. Lu, and A.A. Ghorbani A detailed analysis of the kdd cup 99 data set Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications 2009 IEEE Press 53 58
45. W. Wang, and W. Wu Online detection of network traffic anomalies using degree distributions Int. J. Commun. Network Syst. Sci. 3 2 2010 177 182
46. S.X. Wu, and W. Banzhaf The use of computational intelligence in intrusion detection systems: a review Appl. Soft Comput. 10 1 2010 1 35
47. H. Yang, Q. Du, Particle swarm optimization-based dimensionality reduction for hyperspectral image classification, in: IEEE International Geoscience and Remote Sensing Symposium, 2011, pp. 2357-2360.
48. Xin-She Yang Firefly algorithm, stochastic test functions and design optimisation Int. J. Bio-Inspired Comput. 2 2 2010 78 84
49. Xin She Yang A new metaheuristic bat-inspired algorithm Proceedings of the Nature Inspired Cooperative Strategies for Optimization Studies in Computational Intelligence volume 284 2010 Springer 65 74
50. Xin-She Yang, and Amir Hossein Gandomi Bat algorithm: a novel approach for global engineering optimization Eng. Comput. 29 5 2012 464 483
51. Xin-She Yang, and Xingshi He Bat algorithm: literature review and applications Int. J. Bio-Inspired Comput. 5 3 2013 141 149
52. Nong Ye, Xiangyang Li, A scalable clustering technique for intrusion signature recognition, in: Proceedings of 2nd IEEE SMC Information Assurance Workshop, 2001, pp. 1-4.
53. S. Zanero, and S.M. Savaresi Unsupervised learning techniques for an intrusion detection system Proceedings of the 2004 ACM Symposium on Applied Computing 2004 ACM New York, NY, USA 412 419
54. Shi Zhong, Taghi M. Khoshgoftaar, and Naeem Seliya Clustering-based network intrusion detection Int. J. Reliab. Qual. Saf. Eng. 14 2 2007 169 187