Malware Detection Using Artificial Intelligence: Techniques, Research Issues and Future Directions
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Abstract
Artificial intelligence (AI) is an effective technology used for upgrading the security posture against a variety of security challenges and cyber-attacks that cyber security teams may use. Malware is a software which aims to access a device without the explicit permission of its owner. Forensics investigations report that many organizations have encountered unusual records, collected by their antiviral security monitoring systems. Most of their arrangements skeptically pass a large amount of diplomatic data through various unethical strategies that make malware identification tougher. However, these procedures have varied limitations that call for an unused inquiry about the track. This study explores the complex relationship between malware detection and AI [1]. This paper provides insights into performance evaluation metrics and discusses several research issues that impede the effectiveness of existing techniques. The study also provides recommendations for future research directions and is a valuable resource for researchers and practitioners working in the field of malware detection.
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Daniel Gibert, Carles Mateu, Jordi Planes., The rise of machine learning for detection and classification of malware: Research developments, trends and challenges, Journal of Network and Computer Applications Volume 153 , 1 March 2020, 102526 https://doi.org/10.1016/j.jnca.2019.102526
Kaspersky: A Brief History of Computer Viruses & What the Future Holds
Gary Smith, April 10, 2024 : +95 Cyber Security Breach Statistics 2024, station
Kurt Baker, Malware Analysis, April 17, 2023 : crowdstrike
Perception Point : Malware Detection: 7 Methods and Security Solutions that Use Them
Mohamed, Cogent Engineering (2023), 10: 2272358https://doi.org/10.1080/23311916.2023.2272358 https://doi.org/10.1080/23311916.2023.2272358
Matthew G. Gaber, Mohiuddin Ahmed, and Helge Janicke. 2024. Malware Detection with Artificial Intelligence: A Systematic Literature Review. ACM Comput. Surv. 56, 6, Article 148 (January 2024), 33 pages. https://doi.org/10.1145/3638552
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022). Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review. Electronics, 11(2), 198. https://doi.org/10.3390/electronics11020198
Gupta, S., Sabitha, A. S., & Punhani, R. (2019). Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 6133–6140). https://doi.org/10.35940/ijrte.c5675.098319
R .Sri Devi, M. Mohan Kumar, Cyber Security Affairs in Empowering Technologies. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10S, pp. 1–7). https://doi.org/10.35940/ijitee.j1001.08810s19
Saudi, M. M., Sukardi, S., Abd Aziz, N. A. A., Ahmad, A., & Husainiamer, M. ‘Afif. (2019). Malware Classification for Cyber Physical System (CPS) based on Phylogenetics. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 3666–3670). https://doi.org/10.35940/ijeat.a2711.109119
Joshma K J, & Sankar P, V. (2024). Phishing Website Detection. In Indian Journal of Data Mining (Vol. 4, Issue 1, pp. 38–41). https://doi.org/10.54105/ijdm.a1642.04010524
Rathore, R., & Shrivastava, Dr. N. (2023). Network Anomaly Detection System using Deep Learning with Feature Selection Through PSO. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 5, pp. 1–6). https://doi.org/10.35940/ijese.f2531.0411523