Author Login IJIRT Partner Network (IPN) Login

Malware Prediction Classifier Using Random Forest Algorithm

  • Unique Paper ID: 155843
  • Volume: 9
  • Issue: 2
  • PageNo: 108-113
  • 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.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 2
  • PageNo: 108-113

Malware Prediction Classifier Using Random Forest Algorithm

Related Articles

29 Jun, 2022

Spammer detection and fake user identification using Machine Learning

Read More
Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry