Authors : Yash Gajjar; Vaishnavi Sharma; Sanskruti Bhatt; Dr. Maitri Jhaveri

Volume/Issue : Volume 9 - 2024, Issue 3 - March

Google Scholar : https://tinyurl.com/bdzeeszp

Scribd : https://tinyurl.com/mr29sda8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAR2188

Abstract : Executable files coming from the internet bring along with them many potential hazards and vul- nerabilities in the form of malware to computer systems. The executables can be of form raw binaries, mnemonics, libraries, and function calls/APIs. They can misguide many of the conventional malware detection techniques. This paper explores the potential of Machine Learning- based methods for malware detection problems. The scope of the work here is currently limited to Static Anal- ysis of Executable files. Various feature selection tech- niques are implemented to reduce the size of the training data. Machine learning algorithms like K-Nearest Neigh- bors and Random Forest Classifier were trained on the curated feature sets. The outperforming experiment re- sult was shown by the Random Forest Classifier having an accuracy of 99.5%. We have developed a framework as a two-step module; in the first step, a list of features are extracted from a given executable file, and then for the next step, trained algorithm is integrated into the framework which will classify whether the given executa- ble file is malicious or not. This framework is demon- strated in the form of a Webapp developed in Python. Furthermore, this framework is evaluated based on its performance on a small dataset containing 35 portable executables (.exe) files and it is observed to be retaining the accuracy of the trained algorithm.

Keywords : Portable Executables (PE), Malicious Code, Machine Learning (ML).