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http://dx.doi.org/10.3837/tiis.2018.10.024

Feature Selection Algorithms in Intrusion Detection System: A Survey  

MAZA, Sofiane (Department of Computer Science, University of Ferhat Abbas Setif-1)
TOUAHRIA, Mohamed (Department of Computer Science, University of Ferhat Abbas Setif-1)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.10, 2018 , pp. 5079-5099 More about this Journal
Abstract
Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.
Keywords
Intrusion Detection System; Feauture Selection; Artificial Intelligence Algorithms; Deterministic algorithms; Swarm Intelligence;
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1 Thaseen, I.S., Kumar, C.A, "An integrated intrusion detection model using consistency based feature selection and LPBoost," in Proc. of Green Engineering and Technologies (IC-GET), 2016 Online International Conference on 2016, pp. 1-6. IEEE, 2016.
2 Raman, M.G., Somu, N., Kirthivasan, K., Liscano, R., Sriram, V.S, "An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine," Knowledge-Based Systems, Vol. 134, PP. 1-12, 2017.   DOI
3 Aljawarneh, S., Aldwairi, M., Yassein, M.B, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model," Journal of Computational Science, 2017.
4 Khammassi, C., Krichen. S, "A GA-LR wrapper approach for feature selection in network intrusion detection," Computers & Security, Vol. 70, PP. 255-277, 2017.   DOI
5 Sun, N.-Q., Li. Y, "Intrusion detection based on back-propagation neural network and feature selection mechanism," in Proc. of International Conference on Future Generation Information Technology 2009, pp. 151-159. Springer, 2009.
6 Chen, Y., Abraham, A., Yang. J, "Feature selection and intrusion detection using hybrid flexible neural tree," in Proc. of International Symposium on Neural Networks 2005, pp. 439-444. Springer, 2009.
7 Subbulakshmi, T., Ramamoorthi, A., Shalinie, S.M, "Feature Selection and Classification of Intrusions Using Genetic Algorithm and Neural Networks," Recent Trends in Networks and Communications. pp. 223-234. Springer, 2010.
8 Biswas, N.A., Shah, F.M., Tammi, W.M., Chakraborty. S, "FP-ANK: An improvised intrusion detection system with hybridization of neural network and K-means clustering over feature selection by PCA," in Proc. of Computer and Information Technology (ICCIT), 2015 18th International Conference on 2015, pp. 317-322. IEEE, 2015.
9 Reardon, B.J, "Fuzzy logic versus niched Pareto multiobjective genetic algorithm optimization," Modelling and Simulation in Materials Science and Engineering, Vol. 6, No. (6), PP. 717, 1998.   DOI
10 Manzoor, I., Kumar. N, "A feature reduced intrusion detection system using ANN classifier," Expert Systems with Applications, Vol. 88, PP. 249-257, 2017.
11 El Ougli. A, "Integration des techniques floues a la synthèse de controleurs adaptatifs," 2009.
12 Muthurajkumar, S., Kulothungan, K., Vijayalakshmi, M., Jaisankar, N., Kannan. A, "A Rough Set based feature Selection Algorithm for Effective Intrusion Detection in Cloud Mode," in Proc. of Proceedings of the international conference on advances in communication, network, and computing 2013, pp. 8-13, 2013.
13 Raman, M.G., Kirthivasan, K., Sriram, V.S, "Development of Rough Set-Hypergraph Technique for Key Feature Identification in Intrusion Detection Systems," Computers & Electrical Engineering, Vol. 59, PP. 189-200, 2017.   DOI
14 Beer, F., Buhler. U, "Feature selection for flow-based intrusion detection using Rough Set Theory," in Proc. of Networking, Sensing and Control (ICNSC), 2017 IEEE 14th International Conference on 2017, pp. 617-624. IEEE, 2017.
15 El-Alfy, E.-S.M., Al-Obeidat, F.N, "A multicriterion fuzzy classification method with greedy attribute selection for anomaly-based intrusion detection," Procedia Computer Science, Vol. 34, PP. 55-62, 2014.   DOI
16 Ramakrishnan, S., Devaraju. S, "Attack's feature selection-based network intrusion detection systzm using fuzzy control language," International Journal of Fuzzy Systems, Vol. 19, No. (2), PP. 316-328, 2017.   DOI
17 Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., Dai. K, "An efficient intrusion detection system based on support vector machines and gradually feature removal method," Expert Systems with Applications, Vol. 39, No. (1), PP. 424-430, 2012.   DOI
18 Liao. H.-J., Lin. C.-H.R., Lin, Y.-C., Tung, K.-Y. "Intrusion detection system: A comprehensive review," Journal of Network and Computer Applications, Vol. 36, No. (1), PP. 16-24, 2013.   DOI
19 Wu. S.X., Banzhaf. W, "The use of computational intelligence in intrusion detection systems: A review," Applied soft computing, Vol. 10, No. (1), PP. 1-35, 2010.   DOI
20 Tsang. C.-H, Kwong. S, Wang. H, "Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection," Pattern Recognition, Vol. 40, No. (9), PP. 2373-2391, 2007.   DOI
21 Qin, Z., Feng, C., Wang, Y., Li. F, "Conditional Mutual Information‐Based Feature Selection Analyzing for Synergy and Redundancy," Etri Journal, Vol. 33, No. (2), PP. 210-218, 2011.   DOI
22 Luo, B., Xia. J, "A novel intrusion detection system based on feature generation with visualization strategy," Expert Systems with Applications, Vol. 41, No. (9), PP. 4139-4147, 2014.   DOI
23 Amiri, F., Yousefi, M.R., Lucas, C., Shakery, A., Yazdani. N,"Mutual information-based feature selection for intrusion detection systems," Journal of Network and Computer Applications, Vol. 34, No. (4), PP. 1184-1199, 2011.   DOI
24 Kang, S.-H., Kim, K.J, "A feature selection approach to find optimal feature subsets for the network intrusion detection system," Cluster Computing, Vol. 19, No. (1), PP. 325-333, 2016.   DOI
25 Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang. M, "A multi-objective particle swarm optimisation for filter-based feature selection in classification problems," Connection Science, Vol. 24, No. (2-3), PP. 91-116, 2012.   DOI
26 Qu, G., Hariri, S., Yousif. M, "A new dependency and correlation analysis for features," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. (9), PP. 1199-1207, 2005.   DOI
27 Ganapathy, S., Kulothungan, K., Muthurajkumar, S., Vijayalakshmi, M., Yogesh, P., Kannan. A, "Intelligent feature selection and classification techniques for intrusion detection in networks: a survey," EURASIP Journal on Wireless Communications and Networking, Vol. 2013, No. (1), PP. 271, 2013.   DOI
28 Gao, H.-H., Yang, H.-H., Wang, X.-Y, "Ant colony optimization based network intrusion feature selection and detection," in Proc. of Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on 2005, pp. 3871-3875. IEEE, 2005.
29 Varma, P.R.K., Kumari, V.V., Kumar, S.S, "Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System," Procedia Computer Science, Vol. 85, PP. 503-510, 2016.   DOI
30 Zainal, A., Maarof, M.A., Shamsuddin, S.M, "Feature selection using Rough-DPSO in anomaly intrusion detection," in Proc. of International Conference on Computational Science and Its Applications 2007, pp. 512-524. Springer.
31 Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A, "A detailed analysis of the KDD CUP 99 data set," In: Computational Intelligence for Security and Defense Applications, CISDA 2009. IEEE Symposium on 2009, pp. 1-6. IEEE, 2009.
32 Sujitha, B., Kavitha. V, "Layered Approach For Intrusion Detection Using Multiobjective Particle Swarm Optimization," International Journal of Applied Engineering Research, Vol. 10, No. (12), PP. 31999-32014, 2015.
33 Xue .B, "Particle swarm optimisation for feature selection in classification," A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington, 2014.
34 Chen, Y., Li, Y., Cheng, X.-Q., Guo. L, "Survey and taxonomy of feature selection algorithms in intrusion detection system," in Proc. of International Conference on Information Security and Cryptology 2006, pp. 153-167. Springer, 2006.
35 Salappa, A., Doumpos, M., Zopounidis. C, "Feature selection algorithms in classification problems: An experimental evaluation," Optimisation Methods and Software, Vol. 22, No. (1), PP. 199-212, 2007.   DOI
36 Zhou, L.-H., Liu, Y.-H., Chen, G.-L, "A feature selection algorithm to intrusion detection based on cloud model and multi-objective particle swarm optimization," in Proc. of Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on 2011, pp. 182-185. IEEE, 2011.
37 Malik, A.J., Khan, F.A, "A Hybrid Technique Using Multi-objective Particle Swarm Optimization and Random Forests for PROBE Attacks Detection in a Network," in Proc. of Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on 2013, pp. 2473-2478. IEEE, 2013.
38 Tama, B.A., Rhee, K.H, "A combination of PSO-based feature selection and tree-based classifiers ensemble for intrusion detection systems," in Proc. of Advances in Computer Science and Ubiquitous Computing. pp. 489-495. Springer, 2015.
39 Bolon-Canedo, V., Sanchez-Marono, N., Alonso-Betanzos. A, "Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset," Expert Systems with Applications, Vol. 38, No. (5), PP. 5947-5957, 2011.   DOI
40 Xue, B., Qin, A.K., Zhang. M, "An archive based particle swarm optimisation for feature selection in classification," in Proc. of Evolutionary Computation (CEC), 2014 IEEE Congress on 2014, pp. 3119-3126. IEEE, 2014.
41 Parsazad, S., Saboori, E., Allahyar. A, "Fast feature reduction in intrusion detection datasets," in Proc. of MIPRO, 2012 Proceedings of the 35th International Convention 2012, pp. 1023-1029. IEEE, 2012.
42 Eid, H.F., Hassanien, A.E., Kim, T.-h., Banerjee. S, "Linear correlation-based feature selection for network intrusion detection model," in Proc. of Advances in Security of Information and Communication Networks. pp. 240-248. Springer, 2013.
43 Le Thi, H.A., Le, A.V., Vo, X.T., Zidna. A, "A filter based feature selection approach in msvm using dca and its application in network intrusion detection," in Proc. of Asian Conference on Intelligent Information and Database Systems 2014, pp. 403-413. Springer, 2014.
44 Balakrishnan, S., Venkatalakshmi, K., Kannan. A, "Intrusion detection system using Feature selection and Classification technique," International Journal of Computer Science and Application (IJCSA) Vol. 3, No. (4), November 2014, 2014.
45 Aburomman, A.A., Reaz, M.B.I, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, Vol. 38, PP. 360-372, 2016.   DOI
46 Lin, S.-W., Ying, K.-C., Lee, C.-Y., Lee, Z.-J, "An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection," Applied Soft Computing, Vol. 12, No. (10), PP. 3285-3290, 2012.   DOI
47 Kim, G., Lee, S., Kim. S, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection," Expert Systems with Applications, Vol. 41, No. (4), PP. 1690-1700, 2014.   DOI
48 Goeschel, K, "Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis," in Proc. of SoutheastCon, 2016, pp. 1-6. IEEE, 2016.
49 Jaiswal, S., Saxena, K., Mishra, A., Sahu, S.K, "A KNN-ACO approach for intrusion detection using KDDCUP'99 dataset," in Proc. of Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on 2016, pp. 628-633. IEEE
50 Thaseen, I.S., Kumar, C.A, " Intrusion detection model using fusion of chi-square feature selection and multi class SVM," Journal of King Saud University-Computer and Information Sciences, Vol. 29, No. (4), PP. 462-472, 2017.   DOI
51 Thaseen, I.S., Kumar, C.A, "Intrusion Detection Model Using Chi Square Feature Selection and Modified Naïve Bayes Classifier," in Proc. of Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-16') 2016, pp. 81-91. Springer.
52 Raman, M.G., Somu, N., Kirthivasan, K., Sriram, V.S, "A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems," Neural Networks, Vol. 92, PP. 89-97, 2017.   DOI
53 Enache, A.-C., Sgarciu, V., Togan. M, "Comparative Study on Feature Selection Methods Rooted in Swarm Intelligence for Intrusion Detection," in Proc. of Control Systems and Computer Science (CSCS), 2017 21st International Conference on 2017, pp. 239-244. IEEE, 2017.
54 Wazid, M., Das, A.K, "A Secure Group-Based Blackhole Node Detection Scheme for Hierarchical Wireless Sensor Networks," Wireless Personal Communications, Vol. 94, No. (3), PP. 1165-1191, 2017.   DOI
55 Wazid, M., Das, A.K, "An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks," Wireless Personal Communications, Vol. 90, No. (4), PP. 1971-2000, 2016.   DOI
56 Dhopte, S., Chaudhari, M, "Genetic Algorithm for Intrusion Detection System," IJRIT International Journal of Research in Information Technology, Vol. 2, No. (3), PP. 503-509, 2014.
57 Sengupta, N., Sen, J., Sil, J., Saha.M, "Designing of on line intrusion detection system using rough set theory and Q-learning algorithm," Neurocomputing, Vol. 111, PP. 161-168, 2013.   DOI
58 Subba, B., Biswas, S., Karmakar. S, "A Neural Network based system for Intrusion Detection and attack classification," in Proc. of Communication (NCC), 2016 Twenty Second National Conference on 2016, pp. 1-6. IEEE.
59 De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., Martinez-Alvarez. A, "Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps," Knowledge-Based Systems, Vol. 71, PP. 322-338, 2014.   DOI
60 Wazid, M., Sharma, R., Katal, A., Goudar, R., Bhakuni, P., Tyagi. A, "Implementation and Embellishment of Prevention of Keylogger Spyware Attacks," in Proc. of International Symposium on Security in Computing and Communication 2013, pp. 262-271. Springer, 2013
61 Ahmad. I, "Feature selection using particle swarm optimization in intrusion detection," International Journal of Distributed Sensor Networks, Vol. 11, No. (10), PP. 806954, 2015.
62 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 computing and applications, Vol. 24, No. (7-8), PP. 1671-1682, 2014.   DOI
63 Bahl, S., Sharma, S.K, "A minimal subset of features using correlation feature selection model for intrusion detection system," in Proc. of Proceedings of the Second International Conference on Computer and Communication Technologies 2016, pp. 337-346. Springer, 2016.
64 Panigrahi, A., Patra, M.R, "Performance Evaluation of Rule Learning Classifiers in Anomaly Based Intrusion Detection," in Proc. of Computational Intelligence in Data Mining. Vol 2. pp. 97-108. Springer, 2016.
65 Nguyen, H.T., Petrovic, S., Franke. K, "A comparison of feature-selection methods for intrusion detection," in Proc. of International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security 2010, pp. 242-255. Springer, 2010.
66 Laamari, M.A., Kamel. N, "A hybrid bat based feature selection approach for intrusion detection," in Proc. of Bio-Inspired Computing-Theories and Applications. pp. 230-238. Springer, 2014.
67 Zhou, C.V., Leckie, C., Karunasekera. S, "A survey of coordinated attacks and collaborative intrusion detection," Computers & Security, Vol. 29, No. (1), PP. 124-140, 2010.   DOI
68 Liu, H., Yu. L,"Toward integrating feature selection algorithms for classification and clustering," IEEE Transactions on knowledge and data engineering, Vol. 17, No. (4), PP. 491-502, 2005.   DOI
69 Bostani, H., Sheikhan.M, "Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems," Soft computing, Vol. 21, No. (9), PP. 2307-2324, 2017.   DOI
70 Kolias, C., Kambourakis, G., Maragoudakis. M, "Swarm intelligence in intrusion detection: A survey," computers & security, Vol. 30, No. (8), PP. 625-642, 2011.   DOI
71 Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez, G., Vazquez. E, "Anomaly-based network intrusion detection: Techniques, systems and challenges," Computers & Security, Vol. 28, No. (1-2), PP. 18-28, 2009.   DOI
72 The drapa dataset. 1998.
73 The kdd cup 1999 dataset. 1999.
74 The nsl-kdd dataset. 2009.
75 McHugh. J, "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 (TISSEC), Vol. 3, No. (4), PP. 262-294, 2000.   DOI
76 Ravale, U., Marathe, N., Padiya. P, "Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function," Procedia Computer Science, Vol. 45, PP. 428-435, 2015.   DOI
77 Song, J., Zhu, Z., Price. C, "Feature grouping for intrusion detection system based on hierarchical clustering," in Proc. of International Conference on Availability, Reliability, and Security 2014, pp. 270-280. Springer, 2014.
78 Yin, C., Ma, L., Feng. L, "Towards accurate intrusion detection based on improved clonal selection algorithm," Multimedia Tools and Applications, Vol. 76, No. (19), PP. 19397-19410, 2017.   DOI