Acta Scientific Computer Sciences

Research Article Volume 4 Issue 12

Research Methodology on A Machine Learning Framework and Algorithms for Automatic Detection of Malware

M Atheequllah Khan1,Imtiyaz Khan2, Pankaj Kawadkar3, M Upendra Kumar4* and D Shravani5

1,2Assistant Professor, CS and AI Dept, MJCET, OU, India
3Associate professor and HOD (CSE/IT/MCA),SSSUTMS Sehore, Madhya Pradesh, India
4Professor and Associate, Head CS and AI Dept, MJCET, OU, India
5Associate Professor, ADCE Stanley College of Engineering and Technology for Women, OU, India

*Corresponding Author: M Upendra Kumar, Professor and Associate, Head CS and AI Dept, MJCET, OU, India.

Received: November 20, 2022; Published: November 29, 2022


Cyberspace is ever expanding with inclusion of diversified networks and systems. With the emerging technologies such as Internet of Things (IoT) and distributed computing, there is seamless integration of heterogeneous applications with interoperability. This has brought unprecedented use cases and applications in various domains. Unfortunately, there is every growing threat to cyberspace due to different kinds of malicious programs termed as malware. Since adversaries are developing various kinds of malware, its detection has become a challenging task. Of late, machine learning (ML) techniques are widely used to solve problems in real world applications. Plenty of supervised learning methods came into existence. The objective of this paper is to explore and evaluate different ML models with empirical study. In this paper, we proposed a ML framework for analysing performance of different prediction models. An algorithm known as Machine Learning based Automatic Malware Detection (ML-AMD) is proposed. This algorithm is used to realize the framework with supervised learning. This empirical study has resulted in knowledge about ML models such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Multilayer Perceptron (MLP) and Gradient Boosting (GB). Random Forest model has exhibited highest accuracy with 97.96%. The research outcomes in this paper help in triggering further investigations towards automatic detection of malware.

Keywords: Malware Detection; Machine Learning; Decision Tree; Logistic Regression; Random Forest; Multilayer Perceptron and Gradient Boosting


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Citation: M Upendra Kumar., et al. “Research Methodology on A Machine Learning Framework and Algorithms for Automatic Detection of Malware". Acta Scientific Computer Sciences 4.12 (2022): 62-66.


Copyright: © 2022 M Upendra Kumar., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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