Network intrusion detection using hybrid binary PSO and random forests algorithm

Security and Communication Networks

Volumn 8, Issue 16, 2015, Pages 2646-2660

  Malik, Arif Jamal ^a   Shahzad, Waseem ^a   Khan, Farrukh Aslam ^a  

^a NATIONAL UNIVERSITY OF COMPUTER AND EMERGING SCIENCES   (Pakistan)

Author keywords

Data mining; Intrusion detection system; Machine learning; Network intrusion detection; Particle swarm optimization; Random forests

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; DATA MINING; DECISION TREES; LEARNING SYSTEMS; NETWORK SECURITY; PARTICLE SWARM OPTIMIZATION (PSO);

BINARY PARTICLE SWARM OPTIMIZATION; DIMENSION REDUCTION TECHNIQUES; GLOBAL SEARCH CAPABILITY; INTRUSION DETECTION SYSTEMS; MACHINE LEARNING TECHNIQUES; NETWORK INTRUSION DETECTION; PERFORMANCE METRICS; RANDOM FORESTS;

INTRUSION DETECTION;

EID: 84944050923     PISSN: 19390114     EISSN: 19390122     Source Type: Journal    
DOI: 10.1002/sec.508     Document Type: Article

References (45)

1. Wu Y. High-dimensional pattern analysis in multimedia information retrieval and bioinformatics. State University, New York, Doctoral Thesis, 2004.
2. Lee W, Stolfo SJ. A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security (TISSEC) 2000; 3: 227-261.
3. Mahoney M, Chan P. An analysis of the 1999 DARPA/Lincoln laboratory evaluation data for network anomaly detection. In Proceeding of Recent Advances in Intrusion Detection (RAID), Pittsburgh, PA, USA, Springer Verlag, 3-8 September 2003; 220-237.
4. Malik AJ, Shahzad W, Khan FA. Binary PSO and Random Forests Algorithm for PROBE Attacks Detection in a Network, New Orleans, USAIEEE Congress on Evolutionary Computation, 5-8 June 2011; 662-668.
5. Lee W, Stolfo S. Data mining approaches for intrusion detection. In Proceedings of the Seventh USENIX Security Symposium, San Antonio, Texas, 1998.
6. Lunt TF, Tamaru A, Gilham F, Jagannathan R, Jalali C, Neuman PG. A real-time intrusion detection expert system (IDES). Technical Report Project 6784, Computer Science Laboratory, SRI International, 1992.
7. Cho S-B, Park H-J. Efficient anomaly detection by modeling privilege flows with hidden Markov model. Computers and Security 2003; 22(1): 45-55.
8. Lippmann R, Cunningham S. Improving intrusion detection performance using keyword selection and neural networks. Computer Networks 2000; 34(4):594-603.
9. Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of Third SIAM Conference on Data Mining, San Francisco, CA May 1-3, 2003.
10. Anderson D, Lunt TF, Tamaru HS, Javits A, Valdes A. Detecting unusual program behavior using the statistical components of NIDES. NIDES Technical Report, SRI International, 1995.
11. Debar H, Becker M, Siboni D. A neural network component for an intrusion detection system. In Proceedings of 1992 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, 1992; 240-250.
12. Mukkamala S, Sung AH, Abrham A. Intrusion detection using ensemble of soft computing paradigms. In Third International Conference on Intelligent Systems Design and Applications, Intelligent Systems Design and Applications, Advances in Soft Computing. Springer-Verlag: Germany, 2003; 239-248.
13. Han S-J, Cho S-B. Detecting intrusion with rule based integration of multiple models. Computers and Security 2003; 22(7): 613-623.
14. Banzhaf W, Nordin P, Keller ER, Francone FD. Genetic Programming: An Introduction on the Automatic Evolution of Compute Programs and its Applications. Morgan Kaufmann Publishers, Inc, 1998.
15. Abraham A. Neuro-fuzzy systems: state-of-the-art modeling techniques, connectionist models of neurons, learning processes, and artificial intelligence. In Lecture Notes in Computer Science, Vol. 2084, Mira J, Prieto A (eds). Springer-Verlag: Germany, 2001; 269-276. Granada, Spain.
16. Zhang J, Zulkernine M, Haque A. Random-forests-based network intrusion detection systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 2008; 38(5): 649-659.
17. Tian WJ, Liu JC. Intrusion detection quantitative analysis with support vector regression and particle swarm optimization algorithm. In WNIS '09. International Conference on Wireless Networks and Information Systems. 2009; 133-136.
18. Zhong SH, Huang HJ, Chen AB. An effective intrusion detection model based on random forest and neural networks. Advanced Materials Research 2011; 267(308): 308-313.
19. Guan J, Da-xin Liu, Wang T. Applications of fuzzy data mining methods for intrusion detection systems. In ICOIN 2004, Lagana A et al. (ed.). Assisi Italy, LNCS 3045, 2004; 706-714.
20. Rawat S, Sastry CS. Network intrusion detection using wavelet analysis. In CIT, LNCS 3356, New york, Das G, Gulati VP (eds). Springer Verlag: Berlin Heidelberg, 2004; 224-232.
21. Twycross J. Immune systems, danger theory and intrusion detection. In Proceedings of the AISB 2004 Symposium on Immune System and Cognition, ICSRIM University of Leeds, The Society for the Study of Artificial Intelligence and the simulation of behaviour, U.K., 29 March- 1 April 2004; 40-42.
22. Bosin A, Dessi N, Pes B. Intelligent Bayesian classifiers in network intrusion detection. In Lecture Notecs in Computer Science, IEA/AIE 2005, Bari, LANI 3533. Springer: Italy, June 22-24, 2005; 445-447.
23. Alves RT, Delgado MRBS, Lopes HS, Freitas AA. An Artificial Immune System for Fuzzy-rule Induction in Data Mining. Lecture Notes in Computer Science, Vol. 3242, Springer-Verlag: Berlin, 2004; 1011-1020.
24. Srinoy S, Kurutach W. Combination artificial ant clustering and K-PSO clustering approach to network security model. In IEEE International Conference on Hybrid Information Technology, Jeju Island, Korea, Springer Verlag, 9-11 November 2006; 128-134.
25. Chuang L, Li J-C, Yang C-H. Chaotic binary particle swarm optimization for feature selection using logistic map. In Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, IAENG, 19-21 March 2008; 131-136.
26. Zhang J, Zulkernine M. A hybrid network intrusion detection technique using random forests. In Proceedings of the First International Conference on Availability, Reliability and Security, Vienna University of Technology, Austria, IEEE Computer Society, 20-22 April 2006; 262-269.
27. Kim DS, Park JS. Modeling network intrusion detection system using feature selection and parameters optimization. IEICE Transactions on Information and Systems 2008; E91-D: 1050-1057.
28. Khor K-C, Ting C-Y, Amnuaisuk S-P. A feature selection approach for network intrusion detection. In ICIME International Conference on Information Management and Engineering, Kuala Lumpur Malaysia, IEEE Computer Society, 3-5 April 2009; 133-137.
29. Khor K-C, Ting C-Y, Amnuaisuk S-P. From feature selection to building of Bayesian classifiers: a network intrusion detection perspective. American Journal of Applied Sciences 2009; 6: 1949-1960.
30. Ghali NI. Feature selection for effective anomaly-based intrusion detection. IJCSNS International Journal of Computer Science and Network Security 2009; 9: 285-289.
31. Chebrolu S, Abraham A, Thomas JP. Feature deduction and ensemble design of intrusion detection systems. International Journal of Computers and Security 2005; 24: 295-307.
32. Abraham A, Jain R. Soft computing models for network intrusion detection systems. In Soft Computing in Knowledge Discovery: Methods and Applications. Springer: Germany, 2004.
33. Chen Y, Abraham A, Yang J. Feature selection and intrusion detection using hybrid flexible neural tree. In Lecture Notes in Computer Science 3498. Springer-Verlag: Berlin Heidelberg, 2005; 439-444.
34. Chou TS, Yen KK, Luo J. Network intrusion detection design using feature selection of soft computing paradigms. World Academy of Science, Engineering and Technology 2008; 47: 529-541.
35. Chen G, Chen Q, Guo W. A PSO-based approach to rule learning in network intrusion detection. In Fuzzy Information and Engineering, Advances in Soft Computing, Vol. 40. Springer: Berlin/Heidelberg, 2007; 666-673.
36. Xiaonan S, Banzhaf W. The use of computational intelligence in intrusion detection systems: a review. Applied Soft Computing 2010; 10:1-35.
37. The UCI KDD Archive KDD'99 datasets. Irvine, CA, USA, 1999. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
38. PSO overview. http://www.swarmintelligence.org/
39. Chandrasekar A, Vasudevan V, Yogesh P. Evolutionary approach for network anomaly detection using effective classification. IJCSNS International Journal of Computer Science and Network Security 2009; 9: 296-302.
40. Breiman L. Random Forests. In Machine Learning, Vol. 45. Kluwer Academic Publishers: Netherlands, 2001; 5-32.
41. Random Forests. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
42. Sarasama ST, Huff J, Zhu QA. Hierarchical Kohonen net for anomaly detection in network security. IEEE Transactions on Systems, Man and Cybernatics-Part B 2005; 35: 302-312.
43. Lee J, Park M, Song S. An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis 2005; 48: 869-885.
44. Uriarte RD, Andres SA. Gene selection and classification of microarray data using random forests. BMC Bioinformatics 2006; 7:3.
45. Izmirlian G. Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial. Annals of the New York Academy of Sciences 2004; 1020: 154-174.