Binary Mayfly Optimization with Deep Wavelet Network-based Malware Detection for Cybersecurity
Binary Mayfly Optimization with Deep Wavelet Network-based Malware Detection for Cybersecurity |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-10 |
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| Year of Publication : 2023 | ||
| Author : V. S. Pavankumar, S. Arivalagan, M. Murugesan, P. Sudhakar |
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| DOI : 10.14445/22315381/IJETT-V71I10P216 | ||
How to Cite?
V. S. Pavankumar, S. Arivalagan, M. Murugesan, P. Sudhakar, "Binary Mayfly Optimization with Deep Wavelet Network-based Malware Detection for Cybersecurity," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 173-182, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P216
Abstract
A malware attack is the most prominent cyberattack where malware (malicious software) implements unauthorized action on the target's system. The malware (otherwise called a virus) incorporates different attacks like spyware, command and control, ransomware, etc. Cyber attackers create, sell and use malware for various reasons; however, it is more commonly used to steal business, personal or financial data. Machine Learning (ML) approaches, and Deep Learning (DL) approaches are currently utilized to give an effective solution to overcome these cyberattacks. With the advancement of the ML and DL approaches, a classification model has been commonly exploited in this study to categorize whether the file is malicious or not. This article introduces a new Binary Mayfly Optimization with Deep Wavelet Network-based Malware Detection (BMFO-DWNMD) for cybersecurity. The presented BMFO-DWNMD technique focuses on the recognition and classification of malware using the classification and Feature Selection (FS) process. In the proposed BMFO-DWNMD approach, the BMFO approach is exploited for the optimum Feature Subset (FSB) selection. Next, the BMFO-DWNMD model uses a DWN classifier to recognize malware attacks. Lastly, the African Vulture Optimization Algorithm (AVOA) is exploited for the process of hyperparameter tuning. A comprehensive set of simulations has been performed to depict the investigational validation of the BMFO-DWNMD model. The experimental outcomes illustrate an enhanced achievement of the BMFO-DWNMD model over other models.
Keywords
Cybersecurity, Malware attacks, Mayfly optimization, Feature selection, Deep Learning.
References
[1] Suryakant Badde et al., “Cyber Attack Detection Framework for Cloud Computing,” Intelligent Data Engineering and Analytics, vol. 1177, pp. 243-254, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tran Viet Khoa et al., “Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0,” 2020 IEEE Wireless Communications and Networking Conference, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Truong Thu Huong et al., “Lockedge: Low-Complexity Cyberattack Detection in IoT Edge Computing,” IEEE Access, vol. 9, pp. 29696-29710, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Fargana J. Abdullayeva, “Detection of Cyberattacks in Cloud Computing Service Delivery Models using Correlation Based Feature Selection,” 2021 IEEE 15th International Conference on Application of Information and Communication Technologies, pp. 1-4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Rahul Chourasiya, Vaibhav Patel, and Anurag Shrivastava, “Classification of Cyber Attack using Machine Learning Technique at Microsoft Azure Cloud,” International Research of Engineering and Applied Sciences, vol. 6, no. 1, pp. 4-8, 2018.
[Google Scholar] [Publisher Link]
[6] Prabhat Kumar, Govind P. Gupta, and Rakesh Tripathi, “An Ensemble Learning and Fog-Cloud Architecture-Driven Cyber-Attack Detection Framework for IoMT Networks,” Computer Communications, vol. 166, pp. 110-124, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Carmelo Ardito et al., “An Artificial Intelligence Cyberattack Detection System to Improve Threat Reaction in e-Health,” ITASEC, vol. 2940, pp. 270-283, 2021.
[Google Scholar] [Publisher Link]
[8] MD. Mamunur Rashid et al., “Cyberattacks Detection in IOT-Based Smart City Applications using Machine Learning Techniques,” International Journal of Environmental Research and Public Health, vol. 17, no. 24, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Khoi Khac Nguyen et al., “Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach,” 2018 IEEE Wireless Communications and Networking Conference, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Xiayang Chen et al., “Ensemble Learning Methods for Power System Cyber-Attack Detection,” 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis, pp. 613-616, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Abdulrahman Al-Abassi et al., “An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System,” IEEE Access, vol. 8, pp. 83965-83973, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Iqbal H. Sarker et al., “IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model,” Symmetry, vol. 12, no. 5, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yihao Wan, and Tomislav Dragičević, “Data-Driven Cyber-Attack Detection of Intelligent Attacks in Islanded DC Microgrids,” IEEE Transactions on Industrial Electronics, vol. 70, no. 4, pp. 4293-4299, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Qasem Abu Al-Haija, and Saleh Zein-Sab, “An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks,” Electronics, vol. 9, no. 12, pp. 1-26, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mahmoud Elsisi et al., “Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning,” IEEE Access, vol. 9, pp. 78415-78427, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahmad Ali AlZubi, Mohammed Al-Maitah, and Abdulaziz Alarifi, “Cyber-Attack Detection in Healthcare using Cyber-Physical System and Machine Learning Techniques,” Soft Computing, vol. 25, no. 18, pp. 12319-12332, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] S. Kannan, and T. Pushparaj, “Creation of Testbed Security using Cyber-Attacks,” SSRG International Journal of Computer Science and Engineering, vol. 4, no. 11, pp. 4-14, 2017.
[CrossRef] [Publisher Link]
[18] Ruba Abu Khurma, Ibrahim Aljarah, and Ahmad Sharieh, “A Simultaneous Moth Flame Optimizer Feature Selection Approach Based on Levy Flight and Selection Operators for Medical Diagnosis,” Arabian Journal for Science and Engineering, vol. 46, no. 9, pp. 8415-8440, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Salwa Said et al., “Deep Wavelet Network for Image Classification,” 2016 IEEE International Conference on Systems, Man, and Cybernetics, pp. 922-927, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ahmed Hossam-Eldin et al., “Improving the Frequency Response of Hybrid Microgrid under Renewable Sources’ Uncertainties using a Robust LFC-Based African Vulture Optimization Algorithm,” Processes, vol. 10, no. 11, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mansour Ahmadi et al., “Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification,” Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp. 183-194, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Muhammad Furqan Rafique et al., “Malware Classification using Deep Learning Based Feature Extraction and Wrapper Based Feature Selection Technique,” arXiv, pp. 1-21, 2019.
[CrossRef] [Google Scholar] [Publisher Link]