Malware Detection System Using Machine Learning

M Roopa Sarika

doi:10.71097/IJSAT.v16.i2.6747

Malware Detection System Using Machine Learning

Author(s)	Ms. M Roopa Sarika
Country	India
Abstract	The increasing prevalence of sophisticated and elusive malware presents a persistent and significant challenge to contemporary cybersecurity. This project addresses this critical issue by developing an intelligent malware detection system that employs machine learning to enhance the efficacy of malware identification. The system focuses on the static analysis of key structural information within executable files (PE files), specifically the PE header, enabling rapid initial assessment and mitigating the risks associated with executing potentially harmful code.Our system has a layered architecture, comprising a user-friendly web interface (React), a processing engine (Flask API), and a data storage component (MongoDB). The React interface streamlines file uploads and provides a clear presentation of scan results. The Flask API manages file processing, orchestrates the extraction of relevant data, and utilizes a pre- trained Random Forest model to classify files as either benign or malicious. MongoDB provides robust storage for scan results and historical data, facilitating efficient data management and analysis. At the core of this system is the Random Forest algorithm, a powerful ensemble learning technique that excels at discerning complex patterns in data. By training this model on a diverse dataset of benign and malicious PE files, the system learns to recognize subtle structural features indicative of malicious intent. This enables the system to potentially identify novel malware variants exhibiting similar characteristics to known threats, offering a proactive defense that complements traditional signature-based methods. By focusing on PE header analysis, the system achieves accelerated initial scans compared to more in-depth dynamic analysis methods, which is crucial for minimizing potential damage. The system also provides a confidence score, offering users a quantitative measure of the model's certainty in its prediction and aiding in risk assessment. The system's modular design allows for future expansion and the integration of more advanced analysis techniques.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-30
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6747
Short DOI	https://doi.org/g9r8dm

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 16 Isu 3 Cover Page Vol 16 Isu 2 Cover Page Vol 16 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Malware Detection System Using Machine Learning

Share this