Abstract
Intrusion detection concept plays a vital role in personal computer (PC) security design. Intrusion detection system (IDS) essentially fits in as a unit which monitors the user activities, incoming traffic, and then distinguishes or classifies which one is intrusion and which one is normal or legitimate. Fundamentally IDS recognizes any misuse or unauthorized access which is essentially an attack to the crucial assets of the system or network. It detects the malicious traffic data on a network or a host. The task of desired classification often gets affected by the presence of noisy, redundant, and irrelevant data input. Occurrence of noise in dataset leads to poor classification as it increases the possibility of wrong detection of a class. High misclassification rate and low detection rate by the classifier in IDS enlarges feature space. To overcome this limitation, hybrid intrusion system (H-IDS) is proposed in this paper. H-IDS uses a hybrid strategy with support vector machine (SVM) and intelligent water drops (IWD) to execute the feature selection and classification techniques for IDS. Experimentations reveal that proposed H-IDS helps to accomplish the goal by attaining high classification, detection, and precision as compared to current state of the art.
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References
Aslahi-Shahri, B.M., Rahmani, R., Chizari, M., Maralani, A., Eslami, M., Golkar, M.J., Ebrahim, A.: A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput. Appl. 27(6), 1669–1676 (2016)
Isaiah, O.A., Johnson, O.V., Mutiu, G.: Denial of service (DoS) attacks using PART Rule and decision table rule. J. Electr. Electron. Syst. 6(2), 1–4 (2017)
Pozi, M.S.M., Sulaiman, N.M., Mustapha, N., Perumal, T.: Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming. Neural Process. Lett. 44(2), 279–290 (2015)
Mabu, S., Chen, C., Lu, N., Shimada, K., Hirasawa, K.: An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. IEEE Trans. Syst. Man Cybern. Part-C: Appl. Rev. 41(1), 130–139 (2011)
Aghdam, H.M., Kabiri, P.: Feature selection for intrusion detection system using ant colony optimization. Int. J. Netw. Secur. 18(3), 420–432 (2016)
Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert. Syst. Appl. Elseveir, 11994–12000 (2009)
Ravale, U., Marathe, N., Padiya, P.: Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function. In: International Conference on Advanced Computing Technologies and Applications, Elsevier, pp. 428–435 (2015)
Farhat, V., McCarthy, B., Raysman, R., Holland & Knight L.L.P.: Cyber Attacks: Prevention and Proactive Responses, Practical Law Company on Its Intellectual Property & Technology web services at http://us.practicallaw.com/3-511-5848 (2016)
Heba, F.E., Darwish, A., Hassanien, E.A., Abraham, A.: Principle components analysis and support vector machine based intrusion detection system. In: IEEE International Conference on Intelligent Systems Design and Applications, pp. 363–367 (2010)
Ambusaidi, A.M., He, X., Nanda, P., Tan, Z.: Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 2986–2998 (2016)
Chen, R., Cheng, F., Hsieh, C.: Using rough set and support vector machine for network intrusion detection. Int. J. Netw. Secur. Appl. (IJNSA) 1(1), 1–12 (2009)
Saurabh, P., Verma, B., Sharma, S.: Biologically inspired computer security system: the way ahead, recent trends in computer networks and distributed systems security, CCIS, vol. 335, pp. 474-484. Springer (2011)
Saurabh, P., Verma, B.: An efficient proactive artificial immune system based anomaly detection and prevention system. Expert. Syst. Appl., Elsevier 60, 311–320 (2016)
Saurabh, P., Verma, B.: Immunity inspired cooperative agent based security system. Int. Arab. J. Inf. Technol. 15(2), 289–295 (2018)
Saurabh, P., Verma, B., Sharma, S.: An Immunity inspired anomaly detection system: a general framework a general framework. In: 7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), vol. 202, AISC, Springer, pp. 417–428 (2012)
Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspired Comput. 1(1), 71–79 (2009)
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Hariyale, N., Rathore, M.S., Prasad, R., Saurabh, P. (2020). A Hybrid Approach for Intrusion Detection System. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_31
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DOI: https://doi.org/10.1007/978-981-15-0035-0_31
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