Dynamic Malware Analysis Using AI-Powered Binary Classification and Automated Reverse Engineering
Keywords:
Dynamic Analysis, Malware Detection, AI-Powered Classification, Reverse Engineering, Deep Learning, Binary Analysis, Cybersecurity, Threat IntelligenceAbstract
The rise of sophisticated malware has rendered traditional detection mechanisms increasingly ineffective. This research investigates how dynamic malware analysis, augmented by artificial intelligence (AI)-powered binary classification and automated reverse engineering, can bolster cybersecurity frameworks. The paper explores dynamic behavioral inspection integrated with deep learning models to improve malware classification accuracy. By leveraging reverse engineering automation, the proposed methodology enhances malware de-obfuscation and family attribution. Experimental results using real-world malware datasets demonstrate detection accuracies exceeding 95%, significantly reducing manual analysis time. This paper contributes to advancing proactive malware defense through hybrid AI and automation-based solutions.
References
Gaber, M. G., Ahmed, M., and Janicke, H. "Malware Detection with Artificial Intelligence: A Systematic Review." ACM Computing Surveys, vol. 56, no. 1, 2023.
Poudyal, S., and Dasgupta, D. "Analysis of Crypto-Ransomware Using ML-Based Profiling." IEEE Access, vol. 9, 2021, pp. 130769–130781.
Al Balawi, M., and Alnabhan, M. "Generative AI for Advanced Malware Detection." 2024 4th Intelligent Computing and Information Systems Conference (ICICIS), IEEE, 2024.
Farzaan, M. A. M., Ghanem, M. C., and El-Hajjar, A. "AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments." arXiv preprint arXiv:2404.05602, 2024.
Lumpatki, S. S., and Patwardhan, S. "An Overview of Artificial Intelligence Applications in Cybersecurity Domains." International Conference on Smart Technologies and Systems for Next Generation Computing, Springer, 2024.
Jasim, S. S. "Mobile Based Malware Detection Using Artificial Intelligence Techniques: A Review." Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 16, no. 1, 2024, pp. 44–58.
Gebrehans, G., Ilyas, N., and Eledlebi, K. "Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis." IEEE Transactions on Artificial Intelligence, 2025.
Tyagi, A. K., and Addula, S. R. "Artificial Intelligence for Malware Analysis: A Systematic Study." Artificial Intelligence–Enabled Digital Security, Wiley, 2024.
Poudyal, S., and Dasgupta, D. "AI-Powered Ransomware Detection Framework Using Reverse Engineering." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2020.
Wolsey, A. "The State-of-the-Art in AI-Based Malware Detection Techniques: A Review." arXiv preprint arXiv:2210.11239, 2022.
Lüchinger, J. "AI-Powered Ransomware to Optimize Its Impact on IoT Spectrum Sensors." Master’s thesis, University of Zurich, 2023.
Rohatgi, S., and Mazhar, L. "Malware Analysis and Detection: New Approaches and Techniques." Emerging Threats and Countermeasures in Cybersecurity, Wiley, 2025.
Almomani, I., and Maglaras, L. A. "Cyber Malware: Insights into Reverse Engineering and AI-Powered Attacks." Springer, 2023.
Jalaluddin, A. Z. "An Exploration of Countermeasures to Defend Against Weaponized AI Malware Exploiting Facial Recognition." PhD Dissertation, ProQuest Dissertations Publishing, 2020.
