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Zero-day Android botnet detection using neural networks.

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Title: Zero-day Android botnet detection using neural networks.
Authors: Seraj, Saeed1 (AUTHOR), Pimenidis, Elias2 (AUTHOR), Trovati, Marcello3 (AUTHOR) Marcello.Trovati@Edgehill.ac.uk, Polatidis, Nikolaos4 (AUTHOR)
Source: Neural Computing & Applications. Jun2025, Vol. 37 Issue 17, p10795-10805. 11p.
Subjects: Android (Operating system), Botnets, Artificial neural networks, Computer security vulnerabilities, Machine learning, Malware, Mobile communication system security
Abstract: Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Botnets%22">Botnets</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+security+vulnerabilities%22">Computer security vulnerabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+communication+system+security%22">Mobile communication system security</searchLink>
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  Data: Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-024-10818-7
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      – Code: eng
        Text: English
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      – SubjectFull: Android (Operating system)
        Type: general
      – SubjectFull: Botnets
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Computer security vulnerabilities
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Malware
        Type: general
      – SubjectFull: Mobile communication system security
        Type: general
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              Text: Jun2025
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              Y: 2025
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