An improved NSGA-III algorithm for feature selection used in intrusion detection

Knowledge-Based Systems

Volumn 116, Issue , 2017, Pages 74-85

  Zhu, Yingying ^a   Liang, Junwei ^a   Chen, Jianyong ^a   Ming, Zhong ^a  

^a SHENZHEN UNIVERSITY   (China)

Author keywords

Feature selection; IDS; Many objective optimization; Network anomaly detection

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; INTRUSION DETECTION; MERCURY (METAL); OPTIMIZATION;

CLASSIFICATION ACCURACY; CLASSIFICATION EFFICIENCY; IMBALANCE PROBLEM; IMBALANCED CLASSIFICATION; INTRUSION DETECTION SYSTEMS; MANY-OBJECTIVE OPTIMIZATIONS; NETWORK ANOMALY DETECTION; POPULATION EVOLUTION;

FEATURE EXTRACTION;

EID: 85006241215     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2016.10.030     Document Type: Article

References (39)

1. [1] Patcha, J.-M.P., An overview of anomaly detection techniques: existing solution and latest technological trends. Comput. Netw. 51:12 (2007), 3448–3470.
2. [2] Kim, G., Lee, S., Kim, S., A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst. Appl. 41 (2014), 1690–1700.
3. [3] Theodoridis, S., Koutroumbas, K., Pattern Recognition. 2009, Academic Press.
4. [4] Handl, J., Knowles, J., Feature subset selection in unsupervised learning via multi-objective optimization. Int. J. Comput. Intel. Res. 2:3 (2006), 217–238.
5. [5] de la Hoz, E., de la Hoz, E., Feature selection by multi-objective optimization: application to network anomaly detection by hierarchical self-organizing maps. Knowl.-Based Syst. 71 (2014), 322–338.
6. [6] Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K., Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutorials 16:1 (2014), 303–336.
7. [7] Navidi, W., Statistics for Engineers and Scientists. third ed., 2010, McGraw-Hill.
8. [8] Quinlan, J., Induction of decision trees. Mach. Learn. 1:1 (1986), 81–106.
9. [9] Hall, M.A., Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of the Seventeenth International Conference on Machine Learning, ICML'00, 2000, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 359–366.
10. [10] Yu, L., Liu, H., Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5 (2004), 1205–1224.
11. [11] Eesa, A.S., Orman, Z., Brifcani, A.M.A., A novel feature-selection approach base on cuttlefish optimization algorithm for intrusion detection systems. Expert Syst. Appl. 42:5 (2015), 2670–2679.
12. [12] Ramezani, F., Lu, J., Taheri, J., Hussain, F.K., Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web 18:6 (2015), 1737–1757.
13. [13] Hamdani, T., Won, J.-M., Alimi, A.M., Karray, F., Multi-objective feature selection with NSGA II. Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICCANGA), LNCS, 4431, 2007, Springer, 240–247.
14. [14] Emmanouilidis, A.H., MacIntyre, J., A multi-objective evolutionary setting for feature selection and a commonality-based crossover operator. Proceedings of the 2000 Congress on Evolutionary Computation, 2000, IEEE Press, New York, 209–316.
15. [15] Horn, J., Nafpliotis, N., Goldberg, D.E., A niched pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, 1994, IEEE World Congress on Computational Intelligence, 1, 1994, 82–87.
16. [16] Oliveira, L., Sabourin, R., Bortolozzi, F., Suen, C., A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition. Int. J. Pattern Recognit Artif Intell. 17:6 (2003), 903–929.
17. [17] Srinivas, N., Deb, K., Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2:3 (1994), 221–248.
18. [18] Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms. 2001, Wiley, Chichester, U.K.
19. [19] Deb, K., Saxena, D., Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Proc. WCCI-2006, 2006, 3352–3360.
20. [20] Deb, K., Agrawal, S., Pratap, A., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6:2 (2002), 182–197.
21. [21] Zitzler, E., Laumanns, M., Thiele, L., SPEA2: improving the strength Pareto evolutionary algorithm for multi-objective optimization. International Center for Numerical Methods in Engineering (CIMNE), 2001, 95–100.
22. [22] Deb, K., Jain, H., An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach. Part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18:4 (2014), 577–601.
23. [23] Yin, L., Ge, Y., Feature selection for high-dimensional imbalanced data. Neurocomputing 105 (2013), 3–11.
24. [24] Garza-Fabre, M., Pulido, G.T., Coello Coello, C.A., Ranking methods for many-objective optimization. Proc. MICAI-2009, 2009, 633–645.
25. [25] The Jaccard Index, https://en.wikipedia.org/wiki/Jaccard_index.
26. [26] Laumanns, M., Thiele, L., Deb, K., Zitzler, E., Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10:3 (2002), 263–282.
27. [27] Hadka, Reed, P., Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol. Comput. 20:3 (2012), 423–452.
28. [28] Das, Dennis, J., Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multi-criteria optimization problems. SIAM J. Optim. 8:3 (1998), 631–657.
29. [29] KDD Cup, 1999. dataset http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.
30. [30] McHugh, J., Testing intrusion detection systems: a 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.
31. [31] Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A., A detailed analysis of the KDDCup 1999 dataset. Proceedings of the IEEE International Symposium on Computational Intelligence in Security and Defense Applications (CISDA-2009), 2009, 1–6.
32. [32] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6:2 (2002), 182–197.
33. [33] Maxion, R.A., Roberts, R.R., Proper Use of ROC Curves in Intrusion/Anomaly Detection., 2004, School of Computing Science, University of Newcastle upon Tyne Tech. Rep. CS-TR-871.
34. [34] Panda, M., Abraham, A., Patra, M., Discriminative multinomial Naïve Bayes for network intrusion detection. Sixth International Conference on Information Assurance and Security (IAS), 2010, 5–10.
35. [35] Yu, Z., Tsai, J.J.P., Weigert, T., An adaptive automatically tuning intrusion detection system. ACM Trans. Auton. Adaptive Syst. 3:3 (2008), 1–25 10.
36. [36] Ippoliti, D., Zhou, X., A-GHSOM: an adaptive growing hierarchical self organizing map for network anomaly detection. J. Parallel Distrib. Comput. 72:12 (2012), 1576–1590.
37. [37] Gunes Kayacik, H., Nur Zincir-Heywood, A., Heywood, M.I., A hierarchical SOM- based intrusion detection system. Eng. Appl. Artif. Intell. 20:4 (2007), 439–451.
38. [38] Hoz, E., Ortiz, A., Ortega, J., Hoz, E., Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques. Proceedings of the International Conference on Hybrid Artificial Intelligence Systems (HAIS), Lecture Notes in Computer Science, 8073, 2013, 103–111.
39. [39] Sivatha Sindhu, S.S., Geetha, S., Kannan, A., Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst. Appl. 39:1 (2012), 129–141.