An Efficient Malware Detection Approach Based on Machine Learning Feature Influence Techniques for Resource-Constrained Devices

Abstract

The growing use of computer resources in modern society makes it extremely vulnerable to several cyber-attacks, including unauthorized access to equipment and computer systems’ manipulation or utter breakdown. Malicious attacks in the form of malware cause significant harm to systems with limited resources. Hence, detecting these attacks and promptly implementing a computationally efficient technique is imperative. Utilizing a machine learning (ML) based model is a superior option for promptly identifying malware. This study develops fourteen machine learning models using a five-fold cross-validation technique on the dataset it obtained for research. We compute the execution time and memory used for each of the fourteen ML model developments, considering both all features and the reduced features after applying the data preprocessing methods. We utilized the Extra Tree classifier (ETC) to identify the top ten significant contributing features based on Gini impurity scores, which led to improved accuracy and reduced processing time. After that, we compared the experimental results and found that the Random Forest (RF) classification model on the reduced features set had a prediction accuracy of 99.39% and ROC-AUC values of 0.99. The ETC model prediction yields comparable results, confirming the viability of the suggested model. The proposed model is very resilient, exhibiting an extremely small standard deviation. It is also highly responsive, with reduced execution time and memory utilization.

Keywords

• Malware detection
• random forest classifier
• feature selection
• extra tree classifier
• resource-constrained device
• execution time