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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Tue 22 Sep 2020 17:50 - 18:00 at Koala - AI for Software Engineering (1) Chair(s): Tingting Yu

This paper investigates the problem of classifying Android applications into malicious and benign. We analyze the performance of a popular malware detection tool, Drebin, on malware datasets commonly used in an academic setup and show that the high detection accuracy often stems from learning benign rather than malicious indicators. That, effectively, turns the malware detection tools into benign app detectors. Yet, in practice, malware samples are often larger and can exhibit many behaviors similar to those of benign apps. Under such a challenging setup, looking for benign indicators becomes ineffective and the ability of the tools to detect malware degrades substantially.

In this paper, we propose an approach for identifying malicious portions of an app in the presence of numerous benign features, effectively eliminating “noise” and focusing the detection on truly malicious indicators.We also propose a novel metric estimating the “reasons” for correct malware classification, i.e., whether it is based on the presence of malicious indicators or the absence of benign ones. We show that our proposed approach is effective in both increasing the “standard” classification accuracy and in making more “justifiable” classification decisions.

Tue 22 Sep

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17:10 - 18:10
AI for Software Engineering (1)NIER track / Research Papers at Koala
Chair(s): Tingting Yu University of Kentucky
DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests
Research Papers
Devjeet Roy Washington State University, Ziyi Zhang Washington State University, Maggie Ma Washington State University, Venera Arnaoudova Washington State University, Annibale Panichella Delft University of Technology, Sebastiano Panichella Zurich University of Applied Sciences, Danielle Gonzalez Rochester Institute of Technology, USA, Mehdi Mirakhorli Rochester Institute of Technology
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
Research Papers
Mohammad Jafar Mashhadi University of Calgary, Hadi Hemmati University of Calgary
On Benign Features in Malware Detection
NIER track
Michael Cao The University of British Columbia, Sahar Badihi University of British Columbia, Canada, Khaled Ahmed The University of British Columbia, Peiyu Xiong The University of British Columbia, Julia Rubin University of British Columbia, Canada