Details

Gainfully Using Machine Learning Algorithm in Enhancing the Efficacy of Evaluating Malware Detection System

Rishit Garkhel

38-46

Vol: 10, Issue: 3, 2020

Receiving Date: 2020-07-05 Acceptance Date:

2020-08-04

Publication Date:

2020-09-10

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http://doi.org/10.37648/ijrst.v10i03.005

Abstract

The malware is an executable program that is very dangerous for pc or laptops. Some malware examples are adware, ransomware, bot, keyloggers, viruses, trojan horses etc. The aggressive hike of malware is very risky in confidential data. The limitation of existing classification algorithms is their detection performance and blocking the malware from affecting the systems. So, it is important to create a machine learning algorithm that helps detect and remove malware. With the help of this technique, we can remove the malware. Our research is accompanied by some classification algorithms such as Naive Bayes, Random Forest, MLP classifier, Bagging, AdaBoost, etc. We have evaluated the algorithms by accuracy, precision. Recall, frequency measure and others. For evaluation, we have used the WEKA machine learning and data mining tool. After implementing various algorithms, we concluded that the best accuracy of 99.2% is achieved by random forest.

Keywords: Machine Learning Algorithm; malware detection system; malicious code

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