Malware Analysis and Mitigation

Author(s)	Shubhankar Vidhyadhar Barve, Ms. Vaishali Kumar
Country	India
Abstract	As terrorist software becomes more sophisticated, viruses are increasingly hard to detect with traditional methods. With regard to this, a multi-stage approach to malware detection has been proposed – using static and dynamic analysis and algorithmic evaluation to observe file activity, with the aim of detecting nefarious behavior within files. The system reports on specified sifting criteria within the files, presenting the user with an option of pre-emptive deletion. To further enhance accuracy, a Random Forest model is incorporated to enable evolving pattern updating – continuously adjusting detection parameters to improve accuracy. This model is made available in GitHub repositories, making it simple to access and adjust as desired. Through the marriage of machine learning and multi-layered analysis, this method fosters advanced detection capabilities and total user autonomy over information systems security, adapting to the diverse needs and challenges of users
Keywords	Malware detection, machine learning, static analysis, dynamic analysis, heuristic analysis, Random Forest, cybersecurity.
Field	Computer > Network / Security
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46827
Short DOI	https://doi.org/g9qp48

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