A Novel Machine Learning Framework for Analyzing Performance of Different Prediction Models by Using Automatic Malware Detection (AMD) Algorithm

Abstract

The efficiency of the machine learning techniques in context to the detection of malware's has been proved by state-of-the-art research works. Since adversaries are developing various kinds of malware, its detection has become a challenging task. Unfortunately, there is every growing threat to cyberspace due to different kinds of malicious programs termed as malware. Malware detection has become challenging task now days. Supervised Machine Learning Algorithms are used to detect malware in emerging technologies such as Internet of Things (IoT) and distributed computing, there is seamless integration of heterogeneous applications with interoperability 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 analyzing 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 Perception (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 perception, gradient boosting