Name: MACHINE LEARNING IN MALWARE ANALYSIS AND PREVENTION - Periodic Series
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MACHINE LEARNING IN MALWARE ANALYSIS AND PREVENTION

Authors: Dr. Sneha Deshmukh, Dr. Ashwin Menon

Pages: 74-89

Abstract

Strong and proactive internet security measures must be put in place since digital threats are becoming more frequent and sophisticated. The integration of AI techniques for predicting and detecting cyberattacks is examined in this study. We use unaided approaches for inconsistency discovery and administered learning calculations for break expectation on a variety of datasets, such as network logs, client behaviors, and framework exercises. To improve the precision and usefulness of danger-distincting evidence, social analysis and continuous observation frameworks are combined. Traditional detection techniques face several difficulties since malware is become more varied and complicated. This study looks at how well the Machine Learning algorithm, a potent machine learning tool, recognizes and categorizes malware samples. Emerging approaches such as behavior-based detection and semantic malware descriptions have shown promise and are deployed in commercial software. However, new techniques must be developed to keep pace with the development of malware.

References

Faruk, M. J. H., Shahriar, H., Valero, M., Barsha, F. L., Sobhan, S., Khan, M. A., ... & Wu, F. (2021, December). Malware detection and prevention using artificial intelligence techniques. In 2021 IEEE international conference on big data (big data) (pp. 5369-5377). IEEE.
Anderson, H. S., Kharkar, A., Filar, B., & Roth, P. (2017). Evading machine learning malware detection. black Hat, 2017, 1-6.
Nath, H. V., & Mehtre, B. M. (2014, March). Static malware analysis using machine learning methods. In International Conference on Security in Computer Networks and Distributed Systems (pp. 440-450). Berlin, Heidelberg: Springer Berlin Heidelberg.
Saad, S., Briguglio, W., & Elmiligi, H. (2019). The curious case of machine learning in malware detection.
Akhtar, M. S., & Feng, T. (2022). Malware analysis and detection using machine learning algorithms. Symmetry, 14(11), 2304. https://doi.org/10.3390/sym14112304
Dada, E. G., Bassi, J. S., Hurcha, Y. J., & Alkali, A. H. (2019). Performance evaluation of machine learning algorithms for detection and prevention of malware attacks. IOSR Journal of Computer Engineering, 21(3), 18-27.
Urooj, U., Al-Rimy, B. a. S., Zainal, A., Ghaleb, F. A., & Rassam, M. A. (2021). Ransomware Detection Using the Dynamic Analysis and Machine Learning: A survey and Research Directions. Applied Sciences, 12(1), 172. https://doi.org/10.3390/app12010172
Udayakumar, N., Anandaselvi, S., & Subbulakshmi, T. (2017, December). Dynamic malware analysis using machine learning algorithm. In 2017 International Conference on Intelligent Sustainable Systems (ICISS) (pp. 795-800). IEEE
Djenna, A., Bouridane, A., Rubab, S., & Marou, I. M. (2023). Artificial intelligence-based malware detection, analysis, and mitigation. Symmetry, 15(3), 677. https://doi.org/10.3390/sym15030677
Haque, M. A., Ahmad, S., Sonal, D., Abdeljaber, H. A., Mishra, B. K., Eljialy, A. E. M., ... & Nazeer, J. (2023). Achieving organizational effectiveness through machine learning based approaches for malware analysis and detection. Data and Metadata, 2, 139-139.
Faruk, M. J. H., Masum, M., Shahriar, H., Qian, K., & Lo, D. (2022, June). Authentic learning of machine learning to ransomware detection and prevention. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 442-443). IEEE.
Sharma, P., Kapoor, S., & Sharma, R. (2023). Ransomware detection, prevention and protection in IoT devices using ML techniques based on dynamic analysis approach. International Journal of System Assurance Engineering and Management, 14(1), 287-296
Alraizza, A., & Algarni, A. (2023). Ransomware detection using machine learning: A survey. Big Data and Cognitive Computing, 7(3), 143. https://doi.org/10.3390/bdcc7030143
Akhtar, M. S., & Feng, T. (2023). Evaluation of machine learning algorithms for malware detection. Sensors, 23(2), 946. https://doi.org/10.3390/s23020946
Lee, K., Lee, S. Y., & Yim, K. (2019). Machine learning based file entropy analysis for ransomware detection in backup systems. IEEE access, 7, 110205-110215.
Buriro, A., Buriro, A. B., Ahmad, T., Buriro, S., & Ullah, S. (2023). MalwD&C: a quick and accurate machine learning-based approach for malware detection and categorization. Applied Sciences, 13(4), 2508. https://doi.org/10.3390/app13042508
Awais, M., Tariq, M. A., Iqbal, J., & Masood, Y. (2023, February). Anti-ant framework for android malware detection and prevention using supervised learning. In 2023 4th International Conference on Advancements in Computational Sciences (ICACS) (pp. 1-5). IEEE.
Masum, M., Faruk, M. J. H., Shahriar, H., Qian, K., Lo, D., & Adnan, M. I. (2022, January). Ransomware classification and detection with machine learning algorithms. In 2022 IEEE 12th annual computing and communication workshop and conference (CCWC) (pp. 0316-0322). IEEE
Bearden, R., & Lo, D. C. T. (2017, December). Automated microsoft office macro malware detection using machine learning. In 2017 IEEE international conference on big data (Big Data) (pp. 4448-4452). IEEE.
Cuan, B., Damien, A., Delaplace, C., & Valois, M. (2018, July). Malware detection in pdf files using machine learning. In SECRYPT 2018-15th International Conference on Security and Cryptography (p. 8p).