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Introducing Quantum Computing in Mobile Malware Detection

Authors:

Giovanni Ciaramella,

Giacomo Iadarola,

Francesco Mercaldo,

Antonella Santone,

Fabio MartinelliAuthors Info & Claims

ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security

Article No.: 88, Pages 1 - 8

https://doi.org/10.1145/3538969.3543816

Published: 23 August 2022 Publication History

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Abstract

Mobile malware are increasing their complexity to be able to evade the current detection mechanism by gathering our sensitive and private information. For this reason, an active research field is represented by malware detection, with a great effort in the development of deep learning models starting from a set of malicious and legitimate applications. The recent introduction of quantum computing made possible quantum machine learning i.e., the integration of quantum algorithms within machine learning algorithms. In this paper, we propose a comparison between several deep learning models, by taking into account also a hybrid quantum malware detector. We explore the effectiveness of different architectures for malicious family detection in the Android environment: LeNet, AlexNet, a Convolutional Neural Network model designed by authors, VGG16 and a Hybrid Quantum Convolutional Neural Network i.e., a model where the first layer is a quantum convolution that uses transformations in circuits to simulate the behavior of a quantum computer. Experiments performed on a real-world dataset composed of 8446 Android malicious and legitimate applications allow us to compare the various models, with particular regard to the quantum model concerning the other ones.

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Cited By

Kaur MJain KSingla AKadian K(2024)Quantum Exploration in Ransomware Detection with Conventional Machine Learning Approaches2024 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC460750.2024.10649082(1-8)Online publication date: 15-Mar-2024
https://doi.org/10.1109/InC460750.2024.10649082
Padha ASahoo A(2024)Quantum deep neural networks for time series analysisQuantum Information Processing10.1007/s11128-024-04404-y23:6Online publication date: 24-May-2024
https://doi.org/10.1007/s11128-024-04404-y
Mercaldo FCiaramella GSantone AMartinelli F(2023)Obfuscated Mobile Malware Detection by Means of Dynamic Analysis and Explainable Deep LearningProceedings of the 18th International Conference on Availability, Reliability and Security10.1145/3600160.3605037(1-10)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3600160.3605037
Show More Cited By

Index Terms

Introducing Quantum Computing in Mobile Malware Detection
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Software and application security

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cover image ACM Other conferences

ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security

August 2022

1371 pages

ISBN:9781450396707

DOI:10.1145/3538969

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 23 August 2022

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ARES 2022

ARES 2022: The 17th International Conference on Availability, Reliability and Security

August 23 - 26, 2022

Vienna, Austria

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

Kaur MJain KSingla AKadian K(2024)Quantum Exploration in Ransomware Detection with Conventional Machine Learning Approaches2024 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC460750.2024.10649082(1-8)Online publication date: 15-Mar-2024
https://doi.org/10.1109/InC460750.2024.10649082
Padha ASahoo A(2024)Quantum deep neural networks for time series analysisQuantum Information Processing10.1007/s11128-024-04404-y23:6Online publication date: 24-May-2024
https://doi.org/10.1007/s11128-024-04404-y
Mercaldo FCiaramella GSantone AMartinelli F(2023)Obfuscated Mobile Malware Detection by Means of Dynamic Analysis and Explainable Deep LearningProceedings of the 18th International Conference on Availability, Reliability and Security10.1145/3600160.3605037(1-10)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3600160.3605037
Ciaramella GMartinelli FMercaldo FSantone A(2023)Exploring Quantum Machine Learning for Explainable Malware Detection2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191964(1-6)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191964
Mercaldo FCiaramella GIadarola GStorto MMartinelli FSantone A(2022)Towards Explainable Quantum Machine Learning for Mobile Malware Detection and ClassificationApplied Sciences10.3390/app12231202512:23(12025)Online publication date: 24-Nov-2022
https://doi.org/10.3390/app122312025
Ciaramella GIadarola GMartinelli FMercaldo FSantone A(2022)Continuous and Silent User Authentication Through Mouse Dynamics and Explainable Deep Learning: A Proposal2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020235(6628-6630)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020235

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