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Tue 11 Oct 2022 11:40 - 12:00 at Gold A - Technical Session 4 - Mobile Apps I Chair(s): Jacques Klein

Machine learning based Android malware detection has attracted a great deal of research work in recent years. A reliable malware dataset is critical to evaluate the effectiveness of malware detection approaches. Unfortunately, existing malware datasets used in our community are mainly labelled by taking advantage of existing anti-virus services (i.e., VirusTotal), which are prone to mislabelling. This, however, would lead to the inaccurate evaluation of the malware detection techniques. Removing the label noises from Android malware datasets can be quite challenging, especially at a large data scale. To address this problem, we propose an effective approach called MalWhiteout to reduce the label errors in Android malware datasets. Specifically, we creatively introduce Confident Learning (CL), an advanced noise estimation approach, to the domain of Android malware detection. To combat false positives introduced by CL, we incorporate the idea of ensemble learning and inter-app relation to achieve a more robust capability in noise detection. We evaluate MalWhiteout on a curated large-scale and reliable benchmark dataset. Experimental results show that MalWhiteout is capable of detecting label noises with over 94% accuracy even at a high noise ratio (i.e., 30%) of the dataset. MalWhiteout outperforms the state-of-the-art approach in terms of both effectiveness (8% to 218% improvement) and efficiency (70 to 249 times faster) across different settings. By reducing label noises, we further show that the performance of existing malware detection approaches can be improved.

Tue 11 Oct

Displayed time zone: Eastern Time (US & Canada) change

10:30 - 12:30
Technical Session 4 - Mobile Apps IResearch Papers / NIER Track / Industry Showcase / Journal-first Papers / Tool Demonstrations at Gold A
Chair(s): Jacques Klein University of Luxembourg
Research paper
Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility Issues
Research Papers
Xiaoyu Sun Monash University, Xiao Chen Monash University, Yanjie Zhao Monash University, Pei Liu Monash University, John Grundy Monash University, Li Li Monash University
DOI Pre-print
Automated, Cost-effective, and Update-driven App TestingVirtual
Journal-first Papers
Chanh-Duc Ngo University of Luxembourg, Fabrizio Pastore University of Luxembourg, Lionel Briand University of Luxembourg; University of Ottawa
Link to publication
Industry talk
Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement LearningVirtual
Industry Showcase
Zhengwei Lv ByteDance, Chao Peng ByteDance, China, Zhao Zhang Bytedance Network Technology, Ting Su East China Normal University, Kai Liu Bytedance, Ping Yang Bytedance Network Technology
Vision and Emerging Results
Right to Know, Right to Refuse: Towards UI Perception-Based Automated Fine-Grained Permission Controls for Android AppsVirtual
NIER Track
Vikas K. Malviya Singapore Management University, Chee Wei Leow Singapore Management University, Ashok Kasthuri Singapore Management University, Yan Naing Tun Singapore Management University, Lwin Khin Shar Singapore Management University, Lingxiao Jiang Singapore Management University
Pre-print Media Attached
Research paper
MalWhiteout: Reducing Label Errors in Android Malware DetectionVirtual
Research Papers
Liu Wang Beijing University of Posts and Telecommunications, Haoyu Wang Huazhong University of Science and Technology, China, Xiapu Luo Hong Kong Polytechnic University, Yulei Sui University of Technology Sydney
AUSERA: Automated Security Vulnerability Detection for Android AppsVirtual
Tool Demonstrations
Sen Chen Tianjin University, Yuxin Zhang Tianjin University, Lingling Fan Nankai University, Jiaming Li Tianjin University, Yang Liu Nanyang Technological University
Research paper
A Comprehensive Evaluation of Android ICC Resolution TechniquesVirtual
Research Papers
Jiwei Yan Institute of Software at Chinese Academy of Sciences, China, Shixin Zhang Beijing Jiaotong University, China, Yepang Liu Southern University of Science and Technology, Xi Deng Institute of Software, Chinese Academy of Sciences, Jun Yan Institute of Software at Chinese Academy of Sciences, China, Jian Zhang Institute of Software at Chinese Academy of Sciences, China
DOI Pre-print