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Botnet Attack Detection Using Machine Learning Algorithm Integrated With Ensemble Algorithm

Publication Type : Book Chapter

Publisher : Springer, Singapore

Source : In: Ranganathan G., Fernando X., Shi F., El Allioui Y. (eds) Soft Computing for Security Applications. Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_13

Url : https://link.springer.com/chapter/10.1007/978-981-16-5301-8_13

Campus : Kochi

School : School of Computing

Department : Computer Science

Year : 2021

Abstract : Botnet investigation helps in understanding the idea of attack and the usual way of doing things utilized by the aggressors. Botnet assaults are hard to follow given their quick speed, pestilence nature, and more modest size. AI functions as a only source to botnet assault issues. It encourages location as well as helps in avoidance from bot assault. The efficient learning framework can peruse the client's activities and conduct in the digital world. It can undoubtedly distinguish the conducting nature and part of each action via online media. Nonetheless, the dark cap local area works just in the personal responsibility and spotlights on proliferating vindictive exercises. The botnet is perhaps the most arising danger for the computerized society. The proposed probe model undertakings improved the nature of results by far-reaching botnet location and criminological examination. In this paper, the botnet attack has been detected by using machine learning techniques by ensemble classifier model to improve the accuracy rate by hybriding the model of classifier. The botnet dataset has been used in this approach of detecting the botnet attack. The performance has been calculated and compared with other related work.

Cite this Research Publication : Lincy N.L., Devi U., Vimina E.R. (October 2021) "Botnet Attack Detection Using Machine Learning Algorithm Integrated With Ensemble Algorithm". In: Ranganathan G., Fernando X., Shi F., El Allioui Y. (eds) Soft Computing for Security Applications. Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_13

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