BotNet Prediction in Social Media based on Feature Extraction with Classification using Machine Learning Algorithms
Keywords:
botnet, Machine Learning, social media users, feature analysis, unusual activitiesAbstract
Botnet threat detection has been a focus of continuing study. Botnet identification using flow-based features has been successfully accomplished using machine learning (ML) approaches.Flow-based features' main drawbacks are their significant processing expense and partial capture of network communication patterns.This research propose novel technique in BotNet prediction among the authorized social media users by machine learning algorithm feature analysis. Information has been gathered here from users of social media platforms also it has been filtered based on unusual activities. Then this filtered data features has been extracted and classified using KNN-Xception architecture where the malicious activity. An assessment of the experimental data has been done with regards of detection accuracy, RMSE, malicious activity rate, recall, mAP. The suggested method accomplished detection accuracy of 96%, RMSE of 61%, malicious activity rate of 39%, recall of 59%, mAP of 61%.
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McDermott, C. D., Majdani, F., &Petrovski, A. V. (2018, July). Botnet detection in the internet of things using deep learning approaches. In 2018 international joint conference on neural networks (IJCNN) (pp. 1-8). IEEE.
Ibrahim, W. N. H., Anuar, S., Selamat, A., Krejcar, O., Crespo, R. G., Herrera-Viedma, E., & Fujita, H. (2021). Multilayer framework for botnet detection using machine learning algorithms. IEEE Access, 9, 48753-48768.
Sengupta, T., De, S., & Banerjee, I. (2021, July). A closeness centrality based p2p botnet detection approach using deep learning. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Lee, S., Abdullah, A., Jhanjhi, N., &Kok, S. (2021). Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning. PeerJ Computer Science, 7, e350.
Kundu, P. P., Truong-Huu, T., Chen, L., Zhou, L., & Teo, S. G. (2022). Detection and Classification of Botnet Traffic using Deep Learning with Model Explanation. IEEE Transactions on Dependable and Secure Computing.
Al-Sarem, M., Saeed, F., Alkhammash, E. H., & Alghamdi, N. S. (2021). An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection. Sensors, 22(1), 185.
Shareena, J., Ramdas, A., & AP, H. (2021). Intrusion detection system for iot botnet attacks using deep learning. SN Computer Science, 2(3), 1-8.
Owen, H., Zarrin, J., & Pour, S. M. (2022). A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention. Journal of Cybersecurity and Privacy, 2(1), 74-88.
Zamani, A. K., &Chapnevis, A. (2022). BotNet Intrusion Detection System in Internet of Things with Developed Deep Learning. arXiv preprint arXiv:2207.04503.
Sarker, I. H. (2021). Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Computer Science, 2(3), 1-16.

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Copyright (c) 2023 Surendra Singh Choudhary, S. K. Ghosh, A. Rajesh, Badria Sulaiman Alfurhood, Suresh Limkar, Jasmeen Gill

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