Open Access

ARTICLE

Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

by Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

1 SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
2 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
3 Computer Engineering Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
4 School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, China

* Corresponding Authors: Nida Aslam. Email: ; Tariq Hussain. Email:

(This article belongs to the Special Issue: AI-driven Cybersecurity in Cyber Physical Systems enabled Healthcare, Current Challenges, Requirements and Future research Foresights)

Computers, Materials & Continua 2023, 76(3), 3167-3188. https://doi.org/10.32604/cmc.2023.039721

Received 13 February 2023; Accepted 07 June 2023; Issue published 08 October 2023

Abstract

One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface (API)-based features from a recent dataset. To overcome the dataset imbalance issue, RandomOverSampler, synthetic minority oversampling with tomek links (SMOTETomek), and RandomUnderSampler were applied to the Dataset in different experiments. The results indicated that the extra tree (ET) classifier achieved the highest accuracy of 99.53% within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler technique. Furthermore, the explainable Artificial Intelligence (EAI) technique has been applied to add transparency to the high-performance ET classifier. The global explanation using the Shapely values indicated that the top three features contributing to the goodware class are: Ljava/net/URL;->openConnection, Landroid/location/LocationManager;->getLastKgoodwarewnLocation, and Vibrate. On the other hand, the top three features contributing to the malware class are Receive_Boot_Completed, Get_Tasks, and Kill_Background_Processes. It is believed that the proposed model can contribute to proactively detecting malware events in Android devices to reduce the number of victims and increase users’ trust.

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Keywords

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