Enhancing Mobile Application Security Through Android Threat Classification

Title

Enhancing Mobile Application Security Through Android Threat Classification

Creator

Josephine, Helen V.L.; Rajagopal, Manikandan; Gayatri, S.; Lakshmi, K.

Description

The Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. This project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. This will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. The evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. 2025 The Authors. Published by Elsevier B.V.

Source

Procedia Computer Science;Volume;252;pp.154-164

Date

01-01-2025

Publisher

Elsevier B.V.

Subject

Classification; Cyber Security; Random Forest; SHAP; SMOTE; Supervised Mahcine learning; XG-Boost

Coverage

Josephine H.J.V.L., Business Analytics, School of Business and Management, Chrisut University, Bangalore, 560074, India; Rajagopal M., School of Business and Management, Chrisut University, Bangalore, 560029, India; Gayatri S., School of Computer Science and Application, Reva University, Bangalore, India; Lakshmi K., School of Business and Management, Christ Univeristy, Bangalore, 560074, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 18770509;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1016/j.procs.2024.12.017

https://www.scopus.com/pages/publications/85219166914?origin=resultslist