Malware Prediction Classifier Using Random Forest Algorithm
Author(s):
Dr. M. Chinna Rao, Hasmitha Dasari, Balarka Pradhyumna Danduboina
Keywords:
Malware attacks, Windows, Machine Learning, Random Forest Algorithm, vulnerable.
Abstract
Windows devices are also becoming more popular and are more defenseless to malware attacks. Malware is computer code that is designed to harm the operating system and has various names, including adware, spyware, viruses, worms, trojans, backdoors, ransomware and command and control (C&C) bots, depending on its function. Malware attacks on systems are increasing as a result of increased internet use. The detection of unknown malware has been attempted using several strategies, but none of them have been successful. To deal with these threats, proposed research utilized dynamic malware research based on machine learning. Many malicious software-scanning tools are available for Windows PCs, but they perform static analysis, which consumes a lot of time and resources. To address a solution to this problem, an imaging technique to detect malware effectively by converting malware binaries into .exe files and applying machine learning to those .exe files. A comparison of different Windows PC malware detection techniques with different machine learning classifiers is undertaken to detect reliably.
Article Details
Unique Paper ID: 155843

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 108 - 113
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews