2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 6 , Issue 1 , PP: 30-40, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Tarek Gaber 1 * , Joseph Bamidele Awotunde 2 , Chin-Shiuh Shieh 3
Doi: https://doi.org/10.54216/JISIoT.060103
Accurate and prompt detection of security attacks in the Industrial Internet of Things (IIoT) is important to reduce security risks. Since a massive number of IoT devices are placed over the globe and the quantity gets increased, an effective security solution is necessary. A botnet is a computer network comprising numerous hosts executing on standalone software. In this view, this article develops a novel Glowworm Swarm Optimization Driven Gated Recurrent Unit Enabled Botnet Detection (GSOGRU-BD) model in IIoT Environment. The presented GSOGRU-BD model intends to effectually identify the presence of botnet attacks in the IIoT environment. To do so, the GSOGRU-BD model initially pre-processed the input data to get rid of missing values. In addition, the GSOGRU-BD model involves the GRU model for the effective recognition and classification of botnets. Besides, the GSO algorithm is used for optimal hyperparameter tuning of the GRU model. Comparative experimental validation of the GSOGRU-BD model is tested using a benchmark dataset and the results reported the better outcomes for the GSOGRU-BD model.
Botnet detection, Industrial IoT, Security, Attack detection, Machine learning, Deep learning
[1] Xenofontos, C., Zografopoulos, I., Konstantinou, C., Jolfaei, A., Khan, M.K. and Choo, K.K.R., 2021. Consumer, commercial and industrial iot (in) security: attack taxonomy and case studies. IEEE Internet of Things Journal.
[2] Khan, W.U., Malik, J., Hasan, T., Bibi, I., Al-Wesabi, F.N., Dev, K. and Huang, G., 2022. Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach.
[3] Scheidt, N. and Adda, M., 2021. Threats in industrial IoT. In Internet of Things, Threats, Landscape, and Countermeasures (pp. 137-166). Boca Raton and London: CRC Press.
[4] Popoola, S.I., Ande, R., Adebisi, B., Gui, G., Hammoudeh, M. and Jogunola, O., 2021. Federated deep learning for zero-day botnet attack detection in IoT edge devices. IEEE Internet of Things Journal.
[5] Nguyen, T.N., Ngo, Q.D., Nguyen, H.T. and Giang, N.L., 2022. An Advanced Computing Approach for IoT-Botnet Detection in Industrial Internet of Things. IEEE Transactions on Industrial Informatics.
[6] Bhatt, S. and Ragiri, P.R., 2021. Security trends in Internet of Things: A survey. SN Applied Sciences, 3(1), pp.1-14.
[7] Franco, J., Aris, A., Canberk, B. and Uluagac, A.S., 2021. A survey of honeypots and honeynets for internet of things, industrial internet of things, and cyber-physical systems. IEEE Communications Surveys & Tutorials, 23(4), pp.2351-2383.
[8] Kumar, P., Singh, A. and Sengupta, A., 2021. Industrial IoT: Challenges and Mitigation Policies. In Computer Networks, Big Data and IoT (pp. 143-159). Springer, Singapore.
[9] Kumar, P., Kumar, R., Gupta, G.P., Tripathi, R. and Srivastava, G., 2022. P2TIF: A Blockchain and Deep Learning Framework for Privacy-preserved Threat Intelligence in Industrial IoT. IEEE Transactions on Industrial Informatics.
[10] Popoola, S.I., Adebisi, B., Ande, R., Hammoudeh, M. and Atayero, A.A., 2021. Memory-efficient deep learning for botnet attack detection in iot networks. Electronics, 10(9), p.1104.
[11] Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H.T. and Damaševičius, R., 2021. Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics, 10(11), p.1341.
[12] Li, J., Lyu, L., Liu, X., Zhang, X. and Lv, X., 2021. FLEAM: A federated learning empowered architecture to mitigate DDoS in industrial IoT. IEEE Transactions on Industrial Informatics.
[13] Yan, Q., Huang, W., Luo, X., Gong, Q. and Yu, F.R., 2018. A multi-level DDoS mitigation framework for the industrial Internet of Things. IEEE Communications Magazine, 56(2), pp.30-36.
[14] Lee, S., Abdullah, A., Jhanjhi, N. and Kok, S., 2021. Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning. PeerJ Computer Science, 7, p.e350.
[15] Zhang, Y.G., Tang, J., He, Z.Y., Tan, J. and Li, C., 2021. A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide. Natural Hazards, 105(1), pp.783-813.
[16] Zhang, Y. and Yang, L., 2021. A novel dynamic predictive method of water inrush from coal floor based on gated recurrent unit model. Natural Hazards, 105(2), pp.2027-2043.
[17] Sampathkumar, A., Mulerikkal, J. and Sivaram, M., 2020. Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wireless Networks, 26(6), pp.4227-4238.
[18] Xiuwu, Y., Qin, L., Yong, L., Mufang, H., Ke, Z. and Renrong, X., 2019. Uneven clustering routing algorithm based on glowworm swarm optimization. Ad Hoc Networks, 93, p.101923.
[19] Meidan, Y.; Bohadana, M.; Mathov, Y.; Mirsky, Y.; Shabtai, A.; Breitenbacher, D.; Elovici, Y. N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders. IEEE Pervasive Comput. 2018, 17, 12–22.
[20] Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H.T. and Damaševičius, R., 2021. Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics, 10(11), p.1341.
[21] Nguyen, T. N., Ngo, Q. D., Nguyen, H. T., & Giang, N. L. 2022. An Advanced Computing Approach for IoT-Botnet Detection in Industrial Internet of Things. IEEE Transactions on Industrial Informatics.
[22] Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G., 2022. Securing industrial internet of things against botnet attacks using hybrid deep learning approach. IEEE Transactions on Network Science and Engineering.
[23] Mudassir, M., Unal, D., Hammoudeh, M., & Azzedin, F., 2022. Detection of Botnet Attacks against Industrial IoT Systems by Multilayer Deep Learning Approaches. Wireless Communications and Mobile Computing, 2022.
