Abstract
In the current era, Intrusion Detection System maintains the system actions and store the log files in order to find the security issues. There is a necessity of improving the speed and accuracy in the network security in the network systems. We have applied clustering algorithms especially MakeDensityBasedClusterer, Canopy cluster, Kmeans cluster, FarthestFirst, Decision table. In this paper, we analyzed Botnet Attacks using Data Mining Techniques and the clustering algorithms for Intrusion Detection of Internet of Things. The farthestfirst cluster algorithm can be used for any anomaly detection to speed up the process as it took only 0.17 s to build the model.
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References
Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, D. Breitenbacher, A. Shabtai, Y. Elovici, N-BaIoT: network-based detection of IoT botnet attacks using deep autoencoders, in IEEE Pervasive Computing, Special Issue—Securing the IoT (2018)
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Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, D. Breitenbacher, A. Shabtai, Y. Elovici, dataset
Meidan, Bohadana, Mathov, Mirsky, Shabtai, Department of Software and Information Systems Engineering; Ben-Gurion University of the Negev; Beer-Sheva, 8410501, Israel
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Kavitha, S., Hanumanthappa, M., Kalavathi, B.N. (2022). Intrusion Detection of Internet of Things Botnet Attacks Using Data Mining Technique. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_18
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DOI: https://doi.org/10.1007/978-981-19-1122-4_18
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