Dynamic Analysis Based Mobile Malware Classification Using Supervised Machine Learning Methods

Abstract

Due to the rapid growth of android applications and mobile users in this technological era, there is a large increase in cyber attacks through mobile phones. During Pandemic period, mobile malware attacks are one of the top most cyber attacks observed in android mobile users to steal the user personal credentials by intrusion of adware, spyware, banking malware, SMS malware, riskware, viruses, Trojan horse, worms, keylogger and many more. Machine learning methods are very useful and amicable to detect mobile malwares. Automation of the mobile malware detection is the need of the hour and it is imperative to identify the most suitable machine learning techniques. This book entitled “Dynamic Analysis based Mobile Malware Classification using Supervised Machine Learning Methods” investigates the evaluation of supervised machine learning algorithms that are applied to detect and classify the mobile malwares. A systematic method of evaluation of supervised machine learning model to detect the malware data points and to classify them into binary classification as malware or benign is essential. The purpose of evaluating the supervised machine learning algorithms is to identify the best supervised machine learning model for mobile malware detection with high efficacy rate.  All important performance measures like Precision, Recall, F1 score, R2 score, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error are applied and the entire experiments are conducted using benchmark dataset taken from kaggle community. Nine Supervised Machine Learning methods such as Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, AdaBoost, Multi-layer Perceptron, Logistic Regression and Linear Discriminant Analysis are experimented and the results are discussed. +