Dynamic Analysis Based Mobile Malware Classification Using Supervised Machine Learning Methods | Book Publisher International

 In this modern era, the rapid expansion of android applications and mobile users has resulted in a significant surge in cyber attacks via mobile phones. Mobile malware attacks are one of the most common cyber attacks detected among Android users during the Pandemic period, with intrusions of adware, spyware, banking malware, SMS malware, riskware, viruses, Trojan horses, worms, keyloggers, and other malware stealing the user's personal credentials. Machine learning technologies are quite helpful and friendly when it comes to detecting mobile viruses. The need for automation of mobile malware detection is urgent, and it is critical to discover the most appropriate machine learning algorithms. This book, "Dynamic Analysis-based Mobile Malware Classification Using Supervised Machine Learning Methods," examines the effectiveness of supervised machine learning algorithms for detecting and classifying mobile malware. It is critical to have a systematic process for evaluating supervised machine learning models to detect malware data points and classify them as malware or benign. The goal of evaluating supervised machine learning algorithms is to find the most effective supervised machine learning model for detecting mobile malware. All major performance indicators are used, including Precision, Recall, F1 score, R2 score, Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error, and the full experiment is run on a benchmark dataset from the kaggle community. Nine supervised machine learning algorithms are tested and the findings are described, including Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Nave Bayes, AdaBoost, Multi-layer Perceptron, Logistic Regression, and Linear Discriminant Analysis.

Author(s) Details

Padmavathi Ganapathi
Department of Computer Science, School of Physical Sciences and Computational Sciences Professor, Avinashilingam Institute for Home Science and Higher Education for Women (Deemed to be University),Coimbatore, Tamilnadu, India.

D. Shanmugapriya
Department of Information Technology, Avinashilingam Institute for Home Science and Higher Education for Women (Deemed to be University),Coimbatore, Tamilnadu, India.

A. Roshni
Centre for Cyber Intelligence DST - CURIE - AI, Avinashilingam Institute for Home Science and Higher Education for Women (Deemed to be University),Coimbatore, Tamilnadu, India.

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