Evolutionary Optimization Algorithm with Deep Echo State Network for Anomaly Detection on Secure Cloud Computing Environment

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 4

Year of Publication : 2023

Authors : V. Sujatha Bai, M. Punithavalli

10.14445/23488379/IJEEE-V10I4P105

How to Cite?

V. Sujatha Bai, M. Punithavalli, "Evolutionary Optimization Algorithm with Deep Echo State Network for Anomaly Detection on Secure Cloud Computing Environment," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 4, pp. 46-56, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I4P105

Abstract:

Cloud Computing (CC) is undoubtedly an indispensable technology across the world. It indicates a revolution in collaborative services and data storage. Yet, security problems have increased with the move to CC, which includes intrusion detection systems (IDS). Anomaly detection (AD) is a vital method that ensures the security of CC environments. It identifies unusual behaviour that specifies a security threat. AD is important in a secure CC environment for identifying breaches or attacks and monitoring system performance. The design of potential AD in a secure CC environment needs a combination of machine learning (ML) algorithms, continuous monitoring, and data analysis. Therefore, this article introduces a new Falcon Optimization Algorithm with Deep Echo State Network for Anomaly Detection (FOADESN-AD) technique for a secure CC environment. The presented FOADESN-AD technique exploits the DL model with a metaheuristic optimizer for anomaly or intrusion detection in the cloud platform. To accomplish this, the FOADESN-AD technique initially performs a Z-score normalization process. For anomaly detection, the FOADESN-AD technique uses the DESN classifier, which accurately detects the presence of anomalies in the cloud environment. Moreover, the FOA is utilized to finetune the hyperparameter values of the DESN model, achieving superior classification results. The performance analysis of the FOADESN-AD method is implemented on the CSE-CICIDS-2018 dataset. The experimental values stated the betterment of the FOADESN-AD method over other existing approaches.

Keywords:

Cloud computing, Falcon optimization algorithm, Anomaly detection, Security, Deep learning.

References:

[1] V. Kanimozhi, and T. Prem Jacob, “Artificial Intelligence Outflanks All Other Machine Learning Classifiers in Network Intrusion Detection System on the Realistic Cyber Dataset CSE-CIC-IDS2018 Using Cloud Computing,” ICT Express, vol. 7, no. 3, pp. 366- 370, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Theyazn H. H. Aldhyani, and Hasan Alkahtani, “Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments,” Sensors, vol. 22, no. 13, p. 4685, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Haifeng Lin et al., “Internet of Things Intrusion Detection Model and Algorithm Based on Cloud Computing and Multi-Feature Extraction Extreme Learning Machine,” Digital Communications and Networks, vol. 9, no. 1, pp. 111-124, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] K.P. Sanal Kumar et al., “Security and Privacy-Aware Artificial Intrusion Detection System Using Federated Machine Learning,” Computers & Electrical Engineering, vol. 96, p. 107440, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Dr. S.Veerapandi, Dr. R.Surendiran, and Dr. K.Alagarsamy, "Enhanced Fault Tolerant Cloud Architecture to Cloud-based Computing using Both Proactive and Reactive Mechanisms," DS Journal of Digital Science and Technology, vol. 1, no. 1, pp. 32-40, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Noah Oghenefego Ogwara, Krassie Petrova, and Mee Loong Yang, “Towards the Development Of A Cloud Computing Intrusion Detection Framework Using An Ensemble Hybrid Feature Selection Approach,” Journal of Computer Networks and Communications, 2022, pp.1-16.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hasan Torabi, Seyedeh Leili Mirtaheri, and Sergio Greco, “Practical Autoencoder-Based Anomaly Detection By Using Vector Reconstruction Error,” Cybersecurity, vol. 6, no. 1, p. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Muhammad Asif et al., “MapReduce-based Intelligent Model for Intrusion Detection Using Machine Learning Technique,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 10, pp. 9723-9731, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Dr. S.Veerapandi, Dr.R.Surendiran, and Dr. K.Alagarsamy, "Live Virtual Machine Pre-copy Migration Algorithm for Fault Isolation in Cloud Based Computing Systems," DS Journal of Digital Science and Technology, vol. 1, no. 1, pp. 23-31, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Maamar Ali Saud AL Tobi et al., "Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes Classifiers," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 26-34, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] S. Shashikala, and G. K. Ravikumar, "Deep Studying Signature for Obstruction Obscure in Copy Move Image Forgeries," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 262-270, 2022.
[CrossRef] [Publisher Link]
[12] Stanislav Yamashkin et al., "Metageosystem Analysis Based on a System of Machine Learning and Simulation Algorithms," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 1-12, 2022.
[CrossRef] [Publisher Link]
[13] Dr. R.Surendiran, and Prof. K. Raja, " A Fog Computing Approach for Securing IoT Devices Data using DNA-ECC Cryptography," DS Journal of Digital Science and Technology, vol. 1, no. 1, pp. 10-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ammar Aldallal, and Faisal Alisa, “Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning,” Symmetry, vol. 13, no. 12, p. 2306, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Guosheng Zhao, Yang Wang, and Jian Wang, “Lightweight Intrusion Detection Model of the Internet of Things with Hybrid Cloud-Fog Computing,” Security and Communication Networks, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Azidine Guezzaz et al., “A Lightweight Hybrid Intrusion Detection Framework Using Machine Learning for Edge-Based Iiot Security,” The International Arab Journal of Information Technology, vol. 19, no. 5, pp. 1-9, 2022.
[Google Scholar] [Publisher Link]
[17] Ahmad Shokuh Saljoughi, Mehrdad Mehrvarz, and Hamid Mirvaziri, “Attacks and Intrusion Detection In Cloud Computing Using Neural Networks And Particle Swarm Optimization Algorithms,” Emerging Science Journal, vol. 1, no. 4, pp. 179-191, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Bahram Hajimirzaei, and Nima Jafari Navimipour, “Intrusion Detection for Cloud Computing Using Neural Networks and Artificial Bee Colony Optimization Algorithm,” Ict Express, vol. 5, no. 1, pp. 56-59, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] K. Samunnisa, G. Sunil Vijaya Kumar, and K. Madhavi, “Intrusion Detection System in Distributed Cloud Computing: Hybrid Clustering and Classification Methods,” Measurement: Sensors, vol. 25, p. 100612, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mahmoud M. Sakr, Medhat A. Tawfeeq, and Ashraf B. El-Sisi, “Network Intrusion Detection System Based PSO-SVM for Cloud Computing,” International Journal of Computer Network and Information Security, vol. 11, no. 3, p. 22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Pawan Jaybhaye, and Dr. Bandu B. Meshram, "Malware Detection and Prevention on Cloud," International Journal of Computer and Organization Trends, vol. 9, no. 4, pp. 5-10, 2019.
[CrossRef] [Publisher Link]
[22] Ahmet Sardar Ahmed Issa, and Zafer Albayrak, “DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM,” Acta Polytechnica Hungarica, vol. 20, no. 2, 2023.
[Google Scholar]
[23] Yu-Ting Bai et al., “Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization,” Mathematics, vol. 11, no. 6, p. 1503, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Kareem, S.W. and Okur, M.C., 2021. Falcon optimization algorithm for bayesian network structure learning. Computer Science, 22.
[25] [Online]. Available: https://registry.opendata.aws/cse-cic-ids2018/
[26] Phyu Thi Htun, Kyaw Thet Khaing "Anomaly Intrusion Detection System using Random Forests and k-Nearest Neighbor," International Journal of P2P Network Trends and Technology, vol. 3, no. 1, pp. 39-43, 2013.
[Google Scholar] [Publisher Link]
[27] Hsiao-Chung Lin et al., “Ensemble Learning for Threat Classification in Network Intrusion Detection on a Security Monitoring System for Renewable Energy,” Applied Science, vol. 11, no. 23, p. 11283, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ogobuchi Daniel Okey et al., “BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning, Sensors, vol. 22, p. 7409, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Saud Alzughaib, and Salim El Khediri, “Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset,” Applied Science, vol. 13, p. 2276, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Balajee R M, and Jayanthi Kannan M K, “Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm,” Electronics, vol. 12, p. 1423, 2023.
[CrossRef] [Google Scholar] [Publisher Link]