Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection

Haonan Hu1; Yue Liu2; Yanjie Zhao2; Yonghui Liu2; Xiaoyu Sun2; Chakkrit Tantithamthavorn2; Li Li3

10.1109/ASEW60602.2023.00007

2023

SEP 11-15, 2023

2151-0830

979-8-3503-3033-5

2023 38th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)

17-23

11-15 Sept. 2023

Luxembourg, Luxembourg

Machine learning (ML) has exhibited great potential in Android malware detection. Yet, the reliability of these ML models, as well as the fairness of their evaluation, hinge significantly on the quality of the datasets used. A significant issue compromising these aspects is the presence of temporal inconsistencies within datasets, which could lead to overestimated detection performance. While previous research has acknowledged the impact of temporal inconsistencies, the proposed detection approaches often falter in accuracy and practicality. Previous studies have had limitations when it comes to dealing with complex cases of temporal inconsistencies. Additionally, their approaches require knowledge of a dataset's temporal attributes, which is often not realistic in real-world applications. In response to these challenges, we propose a novel ML-based approach to comprehensively and effectively detect temporal inconsistencies in Android malware datasets, regardless of the magnitude of these inconsistencies. Distinguishing itself from prior attempts, our approach accurately identifies inconsistencies in unknown datasets, without making any assumptions about their temporal attributes. Moreover, we introduce a new benchmark dataset of 78,000 diverse Android samples, spanning malware to benign samples from 2010 to 2022, for exploring temporal inconsistency. A rigorous evaluation of our approach using this dataset reveals its proficiency in managing temporal inconsistencies, achieving a remarkable 98.3% detection accuracy. We further validate the efficacy of our feature selection procedure and demonstrate the robustness of our approach when applied to unknown datasets. Collectively, our findings pioneer a novel performance standard in Android malware detection assessments, contributing to the enhancement of reliability in ML-based techniques.

Conferences Refining Machine Learning Benchmark Testing Fasteners Feature Extraction Malware

第一

英语

CPCI-S ; EI

WOS:001096603000005

20234915149978

Android (operating system) ; Chemical detection ; Mobile security ; Petroleum reservoir evaluation ; Quality control

Petroleum Deposits : Development Operations:512.1.2 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Chemistry:801 ; Quality Assurance and Control:913.3

IEEE

1.Southern University of Science and Technology
2.Monash University
3.Beihang University

Haonan Hu,Yue Liu,Yanjie Zhao,et al. Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection[C],2023:17-23.