Abstract
Ad hoc network is a temporary self-organizing and infrastructure less network. So, it is mostly applied in the military field and disaster relief. Due to wireless communication and self-organizing property ad hoc network is more vulnerable to several intrusions or attacks than the traditional system. Blackhole attack is an important routing disruption attack that malicious node advertises itself as part of a path to the destination. In this paper, we have simulated blackhole attack in ad hoc network environment and collected data of essential features for attack behaviors classification. Then, many machine learning techniques have applied for classification of benign and malicious packet information. It suggests a new approach for select features, essential information collection, and intrusion detection in ad hoc network using machine learning techniques. We have shown comparative results of different machine learning techniques. Our results indicate that this approach can use with different classifiers and can extend it with other intrusions.
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References
Khanna, N., Sachdeva, M.: A comprehensive taxonomy of schemes to detect and mitigate blackhole attack and its variants in MANETs. Comput. Sci. Rev. 32, 24–44 (2019)
Khamayseh, Y.M., Aljawarneh, S.A., Asaad, A.E.: Ensuring survivability against black hole attacks in MANETs for preserving energy efficiency. Sustain. Comput. Inform. Syst. 18, 90–100 (2018)
Al-Shurman, M., Yoo, S.-M., Park, S.: Black hole attack in mobile ad hoc networks. In: Proceedings of the 42nd Annual Southeast Regional Conference, pp. 96–97. ACM (2004)
Sen, S., Clark, J.A.: Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Comput. Netw. 55(15), 3441–3457 (2011)
Ucci, D., Aniello, L., Baldoni, R.: Survey of machine learning techniques for malware analysis. Comput. Secur. 81, 123–147 (2018)
Kalkha, H., Satori, H., Satori, K.: Preventing black hole attack in wireless sensor network using HMM. Procedia Comput. Sci. 148, 552–561 (2019)
Hammamouche, A., Omar, M., Djebari, N., Tari, A.: Lightweight reputation-based approach against simple and cooperative black-hole attacks for MANET. J. Inf. Secur. Appl. 43, 12–20 (2018)
Chatterjee, N., Mandal, J.K.: Detection of blackhole behaviour using triangular encryption in NS2. Procedia Technol. 10, 524–529 (2013)
Panos, C., Ntantogian, C., Malliaros, S., Xenakis, C.: Analyzing, quantifying, and detecting the blackhole attack in infrastructure-less networks. Comput. Netw. 113, 94–110 (2017)
Mitrokotsa, A., Dimitrakakis, C.: Intrusion detection in MANET using classification algorithms: the effects of cost and model selection. Ad Hoc Netw. 11(1), 226–237 (2013)
Feng, F., Liu, X., Yong, B., Zhou, R., Zhou, Q.: Anomaly detection in ad-hoc networks based on deep learning model: a plug and play device. Ad Hoc Netw. 84, 82–89 (2019)
Subba, B., Biswas, S., Karmakar, S.: Intrusion detection in mobile ad-hoc networks: Bayesian game formulation. Eng. Sci. Technol. Int. J. 19(2), 782–799 (2016)
Liu, G., Yan, Z., Pedrycz, W.: Data collection for attack detection and security measurement in mobile ad hoc networks: a survey. J. Netw. Comput. Appl. 105, 105–122 (2018)
Woungang, I., Dhurandher, S.K., Obaidat, M.S., Peddi, R.D.: A DSR-based routing protocol for mitigating blackhole attacks on mobile ad hoc networks. Secur. Commun. Netw. 9(5), 420–428 (2016)
Muralidharan, V., Sugumaran, V.: A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl. Soft Comput. 12(8), 2023–2029 (2012)
Yi-Chung, H.: Pattern classification by multi-layer perceptron using fuzzy integral-based activation function. Appl. Soft Comput. 10(3), 813–819 (2010)
Sharma, A.: Guided stochastic gradient descent algorithm for inconsistent datasets. Appl. Soft Comput. 73, 1068–1080 (2018)
Riley, G.F., Henderson, T.R.: The ns-3 network simulator. In: Wehrle, K., Güneş, M., Gross, J. (eds.) Modeling and Tools for Network Simulation, pp. 15–34. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12331-3_2
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
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Prasad, M., Tripathi, S., Dahal, K. (2020). Intrusion Detection in Ad Hoc Network Using Machine Learning Technique. In: Patgiri, R., Bandyopadhyay, S., Borah, M.D., Thounaojam, D.M. (eds) Big Data, Machine Learning, and Applications. BigDML 2019. Communications in Computer and Information Science, vol 1317. Springer, Cham. https://doi.org/10.1007/978-3-030-62625-9_6
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DOI: https://doi.org/10.1007/978-3-030-62625-9_6
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