Adel Abusitta, Omar Abdul Wahab
and Talal Halabi
Report (2020)
Document published while its authors were not affiliated with Polytechnique Montréal
An external link is available for this itemAbstract
The past few years have seen the ability of cooperative Malware Detection Systems (MDS) to detect complex and unknown malware. In a cooperative setting, an MDS can consult other MDS sabout suspicious malware and make a final decision using an aggregation mechanism. However, large delays may arise from both applying an aggregation mechanism and waiting to receive feedback from consulted MDSs. These short comings render the decisions produced by existing cooperative MDS approaches ineffective in real-time. To address the above-mentioned problem, we propose a deep learning-based cooperative MDS that efficiently exploits historical feedback data to foster proactive decision-making. More specifically, the proposed approach is based on Denoising Autoencoder (DA), which allows us to learn how to reconstruct complete MDSs’ feedback from partial feedback. Our results show the effectiveness of the proposed framework on a real-life dataset.
Research Center: | GERAD - Research Group in Decision Analysis |
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PolyPublie URL: | https://publications.polymtl.ca/51351/ |
Conference Title: | Edge Intelligence Workshop |
Conference Location: | Montreal, Canada |
Conference Date(s): | 2020-03-02 - 2020-03-03 |
Publisher: | GERAD, HEC Montréal |
Report number: | G-2020-23 EIW12 |
Official URL: | https://www.gerad.ca/en/papers/G-2020-23-EIW12.pdf |
Date Deposited: | 18 Apr 2023 15:00 |
Last Modified: | 08 Apr 2025 07:19 |
Cite in APA 7: | Abusitta, A., Abdul Wahab, O., & Halabi, T. (2020). Deep learning for proactive cooperative malware detection system. (Report n° G-2020-23 EIW12). https://www.gerad.ca/en/papers/G-2020-23-EIW12.pdf |
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