Botnet Attack Detection in the Network with SBLSTM Classification
Keywords:
Information technology (IT), cybercrimes, Botnet attacks, SBLSTM, deep learningAbstract
With the emerging growth of Information Technology, criminals are utilizing cyberspace to perform various software-oriented Cybercrimes that are very difficult to analyze. Cyber infrastructure is highly malicious and directly impacts the current performance of the system. Cybercrime occurs in the form of intuitions and other trips. Various devices are connected over the internet of things (IoT) platform to make the evaluation process more effective. The presented system is focused on creating an efficient framework adaptable to various kinds of environments, the creator of Cyber-attacks. In the presented study, staked boosted long short-term memory (LSTM) encoder algorithm is utilized to detect BOTENET Attacks. The proposed work aims to provide a solution with optimized detection Framework that discovers The Attack coming over the network. In order to attend high accuracy, the proposed approach needs to be an effective validate in terms of performance accuracy, and proactive security techniques are combined using machine learning and deep learning algorithm.
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Guerra-Manzanares, H. Bahsi and S. Nõmm, “Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks,” International Conference on Cyberworlds (CW), Kyoto, Japan, pp. 324-327, 2019.
U. Rehman, R. A. Naqvi, A. Rehman, A. Paul, M. T. Sadiq, and D. Hussain, “A trustworthy SIoT aware mechanism as an enabler for citizen services in smart cities,” Electronics, vol. 9, no. 6, p. 918, 2020.
S. M. Istiaque, M. T. Tahmid, A. I. Khan, Z. A. Hassan and S. Waheed, "Artificial Intelligence Based Cybersecurity: Two-Step Suitability Test," 2021 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2021, pp. 1-6, doi: 10.1109/SOLI54607.2021.9672437.
K. N. Karaca and A. Çetin, "Botnet Attack Detection Using Convolutional Neural Networks in the IoT Environment," 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2021, pp. 1-6, doi: 10.1109/INISTA52262. 2021.9548445.
N. Agarwal, A. Q. Md, V. T, P. K and A. K. Sivaraman, "A Robust Pipeline Approach for DDoS Classification using Machine Learning," 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), 2022, pp. 1621-1627, doi: 10.1109/ICICICT54557.2022.9917596.
Z. A. El Houda, B. Brik and S. -M. Senouci, "A Novel IoT-Based Explainable Deep Learning Framework for Intrusion Detection Systems," in IEEE Internet of Things Magazine, vol. 5, no. 2, pp. 20-23, June 2022, doi: 10.1109/IOTM.005.2200028.
A. Ahmed and C. Tjortjis, "Machine Learning based IoT-BotNet Attack Detection Using Real-time Heterogeneous Data," 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, pp. 1-6, doi: 10.1109/ICECET55527. 2022.9872817.
Hekmati, E. Grippo and B. Krishnamachari, "Neural Networks for DDoS Attack Detection using an Enhanced Urban IoT Dataset," 2022 International Conference on Computer Communications and Networks (ICCCN), 2022, pp. 1-8, doi: 10.1109/ICCCN54977. 2022.9868942.
H. Somaya and M. Tomader, "Tuning the hyperparameters for supervised machine learning classification, to optimize detection of IoT Botnet," 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC), 2022, pp. 1-6, doi: 10.1109/ ISIVC54825.2022.9800742.
R. F. Hayat, S. Aurangzeb, M. Aleem, G. Srivastava and J. C. -W. Lin, "ML-DDoS: A Blockchain-Based Multilevel DDoS Mitigation Mechanism for IoT Environments," in IEEE Transactions on Engineering Management, doi: 10.1109/TEM.2022.3170519.
Abhijit D. Jadhav, VidyullathaPellakuri,” Intrusion Detection System Using Machine Learning Techniques for Increasing Accuracy and Distributed & Parallel Approach For Increasing Efficiency”, 978-1-7281-4042-1/19/$31.00 ©2019 IEEE.
Xiaokang Zhou , Member, IEEE, Yiyong Hu , Member, IEEE, Wei Liang , Member, IEEE, Jianhua Ma, Member, IEEE, and Qun Jin , Senior Member, IEEE,” Variational LSTM Enhanced Anomaly Detection for Industrial Big Data”, IEEE transactions on industrial informatics, vol. 17, no. 5, may 2021.
Hai, T. H., &Khiem, N. T. (2020). Architecture for IDS Log Processing using Spark Streaming.2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). doi:10.1109/icecce49384.2020.9179188.
J. Tillett, R. Rao, and F. Sahin, “Cluster-head identification inad hoc sensor networks using particle swarm optimization,” in Proceedings of the ICPWC 2018 - IEEE International Conferenceon Personal Wireless Communications, pp. 201-205, New Delhi,India.
Y. Dong, W. Hu, J. Zhang, M. Chen, W. Liao, and Z. Chen, ‘‘Quantum beetle swarm algorithm optimized extreme learning machine for intrusion detection,’’ Quantum Inf. Process., vol. 21, no. 1, pp. 1–26, Jan. 2022.
R. Vinayakumar, M. Alazab, K. Soman, P. Poornachandran, A. AlNemrat, and S. Venkatraman, “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7, 2019.
P. Dahiya and D. K. Srivastava, “Network intrusion detection in big dataset using spark,” Procedia computer science, vol. 132, pp. 253–262, 2018.
AUTHORS:
Aruna J. she received her bachelor degree and master degree in computer science and engineering from sathyabama university, Chennai. Currently she is pursuing her Ph.D in sathyabama university, Chennai. Her specializations include network security, cryptography, wireless sensor network.
S.PraylaShyry is currently working as Professor in the Department of Computer Science and Engineering. She acquired her M.E in the field of CSE from Annamalai University and PhD from Sathyabama University in the year 2014 She has actively participated as chairperson in many workshops, conferences. She has also been a reviewer in reputed journals. She has also published more than 50 national, International journals and conferences. Her area of specialization includes cyber security, network security and overlay networks, Artificial Intelligence, Machine Learning. She has also published patents and modelled many products. She has also have granted Projects by the Govt of India.
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