Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

Journal of Computational Science

Volumn 25, Issue , 2018, Pages 152-160

  Aljawarneh, Shadi ^a   Aldwairi, Monther ^a,b   Yassein, Muneer Bani ^a  

^a JORDAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Jordan)

^b ZAYED UNIVERSITY   (United Arab Emirates)

Author keywords

Association impact scale; Correlation analysis; Feature reduction; Intrusion detection

Indexed keywords

BINS; FEATURE EXTRACTION; INFORMATION FILTERING; MERCURY (METAL); PROBABILITY DISTRIBUTIONS; STATISTICAL TESTS;

ANOMALY BASED INTRUSION DETECTION SYSTEMS; CORRELATION ANALYSIS; EXPLORATORY ANALYSIS; FALSE NEGATIVE RATE; FEATURE REDUCTION; MULTI-CLASS PROBLEMS; NETWORK TRANSACTIONS; SENSITIVE INFORMATIONS;

INTRUSION DETECTION;

EID: 85016025969     PISSN: 18777503     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jocs.2017.03.006     Document Type: Article

References (43)

1. De la Hoz, Eduardo, De La Hoz, Emiro, Ortiz, Andrés, Ortega, Julio, Prieto, Beatriz, PCA Filtering and Probabilistic SOM for Network Intrusion Detection, vol. 164, 2015, Special Issue: Advances in Computational Intelligence in Elsevier—Neurocomputing, 71–81.
2. Ravale, Ujwala, Marathe, Nilesh, Padiya, Puja, Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function. Proceeding of International Conference on Advanced Computing Technologies and Applications, ICACTA-2015, Procedia Computer Science, vol. 45, Elsevier, 2015, 428–435.
3. D.P. Gaikward, Ravindra c Thool, Intrusion detection system using bagging with partial decision tree base classifier, in: Proceeding of International Conference on Advanced in Computing, Communication and Control, ICAC3(15, in: Procedia Computer Science, vol. 49, Elsevier, 2015, pp. 92–98.
4. Pawar, Sunil Nilkanth, Sadashivrao Bichkar, Rajankumar, Genetic algorithm with variable length chromosomes for network intrusion detection. Int. J. Autom. Comput. 12:3 (2015), 337–342.
5. Denning, D.E., An intruison-detection model. IEEE Symposium on Security and Privacy, 1986, 118–131.
6. D. Anderson, T. Frivold, A. Valdes, Next- generation Intrusion Detection Expert System (NIDES): A summary, SRI Int., no. May 1995, p. 47, 1995.
7. M. Lincoln Laboratory, DARPA Intrusion Detection Data Sets. [Online]. Available: https://www.ll.mit.edu/ideval/data/. [accessed: 7 April 2016].
8. McHugh, J., Testing Intrusion detection systems: a [33] critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Trans. Inf. Syst. Secur. 3:4 (2000), 262–294.
9. Kuang, Fangjun, Zhang, Siyang, Jin, Zhong, Xu, Weihong, A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection. Soft Comput. 19 (2015), 1187–1199.
10. Aldwairi, M., Khamayseh, Y., Al-Masri, M., Application of artificial bee colony for intrusion detection systems. Sec. Commun. Netw. 8:16 (2015), 2730–2740.
11. Ahmad, I., Hussain, M., Alghamdi, A., Alelaiwi, A., Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components. Neural Comput. 24 (2014), 1671–1682.
12. Guo, Chun, Zhou, Yajian, Ping, Yuan, Zhang, Zhongkun, Liu, Guole, Yang, Yixian, A distance sum-based hybrid method for intrusion detection. Appl. Intell. 40 (2014), 178–188.
13. Mukherjee, Saurabh, Sharma, Neelam, Intrusion detection using naive bayes classifier with feature reduction, in: proceedings in 2nd International Conference on Computer, Communication, Control and Information Technology, C3IT-2012. Procedia Technol. 4 (2012), 119–128 (Elsevier).
14. Snapp, S., Brentano, J., Dias, G., et al. DIDS (distributed intrusion detection system)—motivation, architecture, and an early prototype. Proceedings of the 14th National Computer Security Conference, October, 1991.
15. Crosbie, M., Spafford, G., Defending a Computer System Using Autonomous Agents, Technical Report 95-022., 1994, COAST Laboratory—Purdue University.
16. Hosseinpour, F., Meulenberg, A., Ramadass, S., Vahdani, P., Moghaddasi, Z., Distributed agent based model for intrusion detection system based on artificial immune system. Int. J. Digit. Content Technol. Appl. 7 (2013), 206–214.
17. Afzali, N., Azmi, R., MAIS-IDS: a distributed intrusion detection system using multi-agent AIS approach. Eng. Appl. Artif. Intell. 35 (2014), 286–298.
18. Singh, J., Nene, M.J., A survey on machine learning techniques for intrusion detection systems. Int. J. Adv. Res. Comput. Commun. Eng. 2:11 (2013), 4349–4355.
19. Wagh, S.K., Survey on intrusion detection system using machine learning techniques. Int. J. Comput. Appl. 78:16 (2013), 30–37.
20. C. Qiu, J. Shan, B. Polytechnic, B. Shandong, Research on Intrusion Detection Algorithm Based on BP Neural Network, vol. 9, no. 4, pp. 247–258, 2015.
21. Vokorokos, L., Baláž A., Chovanec, M., Intrusion detection system using self organizing map. Informatica 6:1 (2006), 1–6.
22. J.-P. Planquart, Application of Neural Networks to Intrusion Detection, 2001.
23. Sahu, S.K., Sarangi, S., Jena, S.K., A detail analysis on intrusion detection datasets. Souvenir 2014 IEEE Int. Adv. Comput. Conf. IACC, 2014, 1348–1353.
24. J. Allen, C., Alan, F., William, M., John, P. Jed, and S. Ed, State of the Practice of Intrusion Detection Technologies, Technical Report no. CMU/SEI-99-TR- 028, Tech. Rep., no. January, p. 221, 2000.
25. Paxson, V., Bro: a system for detecting network intruders in real-time. Comput. Networks 31:23 (1999), 2435–2463.
26. Sekar, R., Guang, Y., Verma, S., Shanbhag, T., A high-performance network intrusion detection system. Proc. 6th ACM Conf. Comput. Commun. Secur. − CCS ’99, 1999, 8–17.
27. Lu, H., Zheng, K., Liu, B., Zhang, X., Liu, Y., A memory-efficient parallel string matching architecture for high-speed intrusion detection. IEEE J. Sel. Areas Commun. 24:10 (2006), 1793–1803.
28. Das, A., Nguyen, D., Zambreno, J., Memik, G., Choudhary, A., An FPGA-based network intrusion detection architecture. IEEE Trans. Inf. Forensics Secur. 3:1 (2008), 118–132.
29. Artan, N.S., Ghosh, R., Guo, Y., Chao, H.J., A 10- Gbps high-speed single-chip network intrusion detection and prevention system. IEEE GLOBECOM 2007-2007 IEEE Glob. Telecommun. Conf., 2007, 343–348.
30. Vasiliadis, G., Antonatos, S., Polychronakis, M., Markatos, E., Ioannidis, S., Gnort: high performance network intrusion detection using graphics processors. Recent Adv. Intrusion Detect., 2008, 116–134.
31. S. Bastke, Combining statistical network data, probabilistic neural networks and the computational power of GPUs for anomaly detection in computer networks, Work. Intell. Secur. (SecArt 2009), no. iii, pp. 1–6, 2009.
32. OuYang, Q., Theoretical and mathematical foundations of computer science. Commun. Comput. Inf. Sci. 164:January (2011), 154–160.
33. Sasanka Potluri and Christian Diedrich, High Performance Intrusion Detection and Prevention Systems: A Survey, unpublished.
34. Dhanabal, L., Shantharajah, S.P., A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering 4 (2015), 446–452.
35. Altwaijry, H., Algarny, S., Bayesian based intrusion detection system. J. King Saud Univ. Comp. Inf. Sci. 24 (2012), 1–6.
36. Shrivas, A.K., Dewangan, A.K., An ensemble model for classification of attacks with feature selection based on KDD99 and NSL-KDD data set. Int. J. Comp. Appl. 99 (2014), 8–13.
37. Bhavsar, Y.B., Waghmare, K.C., Intrusion detection system using data mining technique: support vector machine. Int. J. Emerg. Technol. Adv. Eng. 3 (2013), 581–586.
38. Revathi, D.a.M.S., A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. 2:12 (2013), 1848–1853.
39. Cireşan, D., Meier, U., Schmidhuber, J., Multi-column deep neural networks for image classification. Int. Conf. Pattern Recognit.(February), 2012, 3642–3649.
40. Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., Culurciello, E., Hardware accelerated convolutional neural networks for synthetic vision systems. Proc. Int. Symp. Circuits Syst., 2010.
41. K. Ovtcharov, O., Ruwase, J., Kim, J., Fowers, K. Strauss, E.S. Chung, Accelerating Deep Convolutional Neural Networks Using Specialized Hardware, pp. 3–6, 2015.
42. Potluri, Sasanka, and Christian Diedrich Accelerated deep neural networks for enhanced Intrusion Detection System. In Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st International Conference on, pp. 1–8. IEEE, 2016.
43. Aljawarneh, S.A., Moftah, R.A., Maatuk, A.M., Investigations of automatic methods for detecting the polymorphic worms signatures. Futur. Gener. Comput. Syst. 60 (2016), 67–77.