A Machine Learning Framework for Automatic Detection of Malware

Abstract

AbstractCyberspace is every 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.KeywordsMalware detectionMachine learningDecision treeLogistic regressionRandom forestMultilayer perceptronGradient boosting