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Deep learning for proactive cooperative malware detection system

Adel Abusitta, Omar Abdul Wahab et Talal Halabi

Rapport (2020)

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Abstract

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.

Centre de recherche: GERAD - Groupe d'études et de recherche en analyse des décisions
URL de PolyPublie: https://publications.polymtl.ca/51351/
Nom de la conférence: Edge Intelligence Workshop
Lieu de la conférence: Montreal, Canada
Date(s) de la conférence: 2020-03-02 - 2020-03-03
Maison d'édition: GERAD, HEC Montréal
Numéro du rapport: G-2020-23 EIW12
URL officielle: https://www.gerad.ca/en/papers/G-2020-23-EIW12.pdf
Date du dépôt: 18 avr. 2023 15:00
Dernière modification: 03 nov. 2023 13:41
Citer en APA 7: Abusitta, A., Abdul Wahab, O., & Halabi, T. (2020). Deep learning for proactive cooperative malware detection system. (Rapport n° G-2020-23 EIW12). https://www.gerad.ca/en/papers/G-2020-23-EIW12.pdf

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