Efficient and Simple Machine Learning-based Malware and Trojan Identification Tool

J. Dhiviya Rose¹ , Isha Mittal² , Ramya Mihir³

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.2 , pp.64-68, Apr-2022

Online published on Apr 30, 2022

Copyright © J. Dhiviya Rose, Isha Mittal, Ramya Mihir . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
When COVID-19 hit the world, it altered the working pattern of all the people around the world. Along with this, it is seen that there has been an exponential growth in the cases of malware, trojans and cyber-crime rates. New and recent malwares uses advanced techniques like polymorphism and metamorphism to help in assisting the malware detection and analysis procedure. Identifying malware in view of its features and conduct is analytic and serious for the computer security. Most of the anti-viruses that are present rely upon the signature-based noticing which is moderately easy to dodge and evade and is insufficient and also ineffective for zero-day exploit-based malware. With the ascent of the Internet, there has been enormous development in the quantity of malware on the planet. With this project, we provide a new approach to identify malware using static analysis, i.e. without executing. With the help of different machine learning models, we will identify malware if present in any file, to prevent any further attacks. The target audience and the people who will majorly get benefitted from this project are the students as well as the working professionals who are these days working in online mode due to the pandemic. This application will promote an easy use to identify the files that they receive over emails, SMS, or any other e-mode, to scan before opening any malware file and getting trapped. The target audience for this proposed system is mainly all the students, and professionals, who are more likely to be active on the internet.

Key-Words / Index Term :
Malware, Internet Security, Machine Learning

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