Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis

Year : 2023 | Volume :01 | Issue : 02 | Page : 43-53

Manas Kumar Yogi

Yamuna Mundru

  1. Assistant Professor Department of computer science and engineering, Pragati Engineering College (Autonomous) Andhra Pradesh India
  2. Assistant Professor CSE-AI& ML Department, Pragati Engineering College (Autonomous) Andhra Pradesh India


The ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This study explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio-inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The study begins by introducing the GWO algorithm, elucidating its fundamental principles and mechanisms. It subsequently details how GWO can be effectively adapted to the realm of malware analysis, emphasizing its role in improving the selection of discriminative features and optimizing the parameters of machine learning models. In pursuit of empirical validation, a comprehensive experimental setup is presented, featuring diverse malware datasets, well-defined evaluation metrics, and baseline models for performance comparison. The experimental results unveil compelling findings: the deployment of GWO consistently yields substantial enhancements in the accuracy and resilience of malware detection systems. Notably, GWO exhibits remarkable effectiveness in addressing the dynamic and polymorphic nature of contemporary malware, making it a valuable asset for real-time threat identification. The significance of these discoveries has broad implications for both research and practical use in the realm of cybersecurity. The deployment of GWO emerges as a potent strategy for fortifying the capabilities of malware detection systems, rendering them more adaptive and proficient in discerning emerging threats. Furthermore, this study underscores the importance of exploring innovative, nature-inspired approaches, such as GWO, to keep pace with the ever-shifting landscape of cyber threats. In conclusion, this study illuminates the promising potential of the Grey Wolf Optimizer (GWO) mechanism as a transformative tool for malware analysis, ushering in a new era of precision and efficiency in the ongoing battle against malware-induced vulnerabilities.

Keywords: Grey Wolf Optimizer, Malware, Virus, Trojan, Threat, Security

[This article belongs to International Journal of Wireless Security and Networks(ijwsn)]

How to cite this article: Manas Kumar Yogi, Yamuna Mundru. Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis. International Journal of Wireless Security and Networks. 2023; 01(02):43-53.
How to cite this URL: Manas Kumar Yogi, Yamuna Mundru. Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis. International Journal of Wireless Security and Networks. 2023; 01(02):43-53. Available from:


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Regular Issue Subscription Review Article
Volume 01
Issue 02
Received September 11, 2023
Accepted September 22, 2023
Published September 25, 2023