Hybrid Data Augmentation Based Machine Learning Approach for Botnet Attack Detection in IOT Networks
DOI:
https://doi.org/10.24113/ijoscience.v10i3.513Abstract
This paper presents a comprehensive approach to botnet detection in Internet of Things (IoT) networks through the development and evaluation of a Generative Adversarial Network (GAN) augmented machine learning model. The methodology encompasses a multi-step process, starting with data collection and pre-processing, including feature extraction, normalization, and handling missing values. To address the challenge of data imbalance, a novel application of GANs is proposed. For classification of network traffic into botnet and legitimate traffic is performed using xgboost. The performance of the proposed model is rigorously evaluated using the N-BaIoT dataset, demonstrating its effectiveness through high accuracy, precision, recall, and F1-score metrics. The results indicate significant improvements over existing models, showcasing the potential of the proposed methodology in enhancing IoT network security against botnet threats.
Downloads
References
Alothman, Zainab, Mouhammd Alkasassbeh, and Sherenaz Al-Haj Baddar. "An efficient approach to detect IoT botnet attacks using machine learning." Journal of High Speed Networks 26.3 (2020): 241-254.
Hussain, Faisal, et al. "A two-fold machine learning approach to prevent and detect IoT botnet attacks." Ieee Access 9 (2021): 163412-163430.
Abraham, Brendan, et al. "A comparison of machine learning approaches to detect botnet traffic." 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018.
McDermott, Christopher D., Farzan Majdani, and Andrei V. Petrovski. "Botnet detection in the internet of things using deep learning approaches." 2018 international joint conference on neural networks (IJCNN). IEEE, 2018.
Injadat, Mohammad Noor, Abdallah Moubayed, and Abdallah Shami. "Detecting botnet attacks in IoT environments: An optimized machine learning approach." 2020 32nd International Conference on Microelectronics (ICM). IEEE, 2020.
Tuan, Tong Anh, et al. "Performance evaluation of Botnet DDoS attack detection using machine learning." Evolutionary Intelligence 13 (2020): 283-294.
Hasan, Tooba, et al. "Securing industrial internet of things against botnet attacks using hybrid deep learning approach." IEEE Transactions on Network Science and Engineering (2022).
Hezam, Abdulkareem A., et al. "Deep learning approach for detecting botnet attacks in IoT environment of multiple and heterogeneous sensors." International Conference on Advances in Cyber Security. Singapore: Springer Singapore, 2021.
Celil, O. K. U. R., and Murat DENER. "Detecting IoT Botnet attacks using machine learning methods." 2020 International Conference on Information Security and Cryptology (ISCTURKEY). IEEE, 2020.
Desai, Madhuri Gurunathrao, Yong Shi, and Kun Suo. "A hybrid approach for IoT botnet attack detection." 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021.
Lee, Seungjin, et al. "Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning." PeerJ Computer Science 7 (2021): e350.
Taher, Fatma, et al. "Reliable Machine Learning Model for IIoT Botnet Detection." IEEE Access (2023).
Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici, ‘‘N—BaIoT-network-based detection of IoT botnet attacks using deep autoencoders,’’ IEEE Pervasive Comput., vol. 17, no. 3, pp. 12–22, Sep. 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Fatma Zafar, Shivank Soni
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
