Malware Analysis and Detection using ML tools: Current State and Challenges
Malware Analysis and Detection using ML tools: Current State and Challenges |
||
 |
 |
|
| © 2025 by IJETT Journal | ||
| Volume-73 Issue-1 |
||
| Year of Publication : 2025 | ||
| Author : Gulshan, Neetu Sharma |
||
| DOI : 10.14445/22315381/IJETT-V73I1P132 | ||
How to Cite?
Gulshan, Neetu Sharma, "Malware Analysis and Detection using ML tools: Current State and Challenges," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 371-384, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P132
Abstract
In the era of digitalization, a major issue that must be addressed is cyber security. The use of technologies and advancements has endangered the user’s information and data. Here, the main focus is on malware that should be detected in the early stages. Malware detection identifies and mitigates malicious software threats to computer systems and networks. With the increase in cyber-attacks, malware detection has become critical for individuals and organizations to safeguard their digital assets and sensitive information. In this paper, here discussion of the current state of malware detection, including challenges and advancements in the field. It also covers the most commonly used malware detection techniques, such as ‘signature-based detection’, ‘behaviour-based detection’, and ‘machine learning-based detection’. At last, it quantifies the ml-based method for detection in various parameters.
Keywords
Malware Detection, Cyber Security, Machine Learning, Cyber-Attacks.
References
[1] Daniel Gibert, Carles Mateu, and Jordi Planes, “The Rise of Machine Learning for Detection and Classification of Malware: Research Developments, Trends and Challenges,” Journal of Network and Computer Applications, vol. 153, pp. 1-22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Abdelouahab Amira et al., “A Survey of Malware Analysis Using Community Detection Algorithms,” ACM Computing Surveys, vol. 56, no. 2, pp.1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Akshat Gaurav, Brij B. Gupta, and Prabin Kumar Panigrahi, “A Comprehensive Survey on Machine Learning Approaches for Malware Detection in IoT-Based Enterprise Information System,” Enterprise Information Systems, vol. 17, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Tony Quertier et al., “A Lean Transformer Model for Dynamic Malware Analysis and Detection,” arXiv, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Mihai Christodorescu, Somesh Jha, and Christopher Kruegel, “Mining Specifications of Malicious Behavior,” Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp. 5-14, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mike Nkongolo, “Assessing Cyclostationary Malware Detection via Feature Selection and Classification,” arXiv, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Halit Bakır, and Rezan Bakır, “Droidencoder: Malware Detection Using Auto-Encoder Based Feature Extractor and Machine Learning Algorithms,” Computers and Electrical Engineering, vol. 110, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Aastha Sharma, Divya Upadhyay, and Shanu Sharma, “Enhancing Blockchain Security: A Novel Approach to Integrated Malware Defence Mechanisms,” Engineering Research Express, vol. 6, no. 2, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Baskoro Adi Pratomo et al.,, “Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection,” arXiv, pp. 1-31, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Valerian Rey et al., “Federated Learning for Malware Detection in IoT Devices,” Computer Networks, vol. 204, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Rajif Agung Yunmar et al., “Hybrid Android Malware Detection: A Review of Heuristic-Based Approach,” IEEE Access, vol. 12, pp. 41255-41286, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Borja Molina-Coronado et al., “Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection,” arXiv, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Avinash Singh, Richard Adeyemi Ikuesan, and Hein Venter, “MalFe-Malware Feature Engineering Generation Platform,” Computers, vol. 12, no. 10, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Matthew G. Gaber, Mohiuddin Ahmed, and Helge Janicke, “Malware Detection with Artificial Intelligence: A Systematic Literature Review,” ACM Compuing Surveys, vol. 56, no. 6, pp. 1-33, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S.M. Rakib Hasan, and Dhakal Aakar, “Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis,” arXiv, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Pie Yan et al., “Prompt Engineering-Assisted Malware Dynamic Analysis Using GPT-4,” arXiv, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Andrea Ponte et al., “SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines,” Computers and Security, vol. 150, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fehmi Jaafar, Gabriela Nicolescu, and Christian Richard, “A Systematic Approach for Privilege Escalation Prevention,” 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), Vienna, Austria, pp. 101-108, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] L. Nataraj et al., “Malware Images: Visualization and Automatic Classification,” VizSec '11: Proceedings of the 8th International Symposium on Visualization for Cyber Security, pp. 1-4, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Igar Santos et al., “Opcode Sequences as Representation of Executables for Data-Mining-Based Unknown Malware Detection,” Information Sciences, vol. 231, pp. 64-82, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Konrad Rieck et al., “Learning and Classification of Malware Behaviour,” Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA 2008), pp. 108-125, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Clemens Kolbitsch et al., “Effective and Efficient Malware Detection at the End Host,” Proceedings of the 18th USENIX Security Symposium, pp. 351-398, 2009.
[Google Scholar] [Publisher Link]
[23] Michael Bailey et al., “Automated Classification and Analysis of Internet Malware,” Proceedings of the 10th International Conference on Recent Advances in Intrusion Detection (RAID 2007), pp. 178-197, 2007.
[CrossRef] [Google Scholar] [Publisher Link]