BotDroid: Permission-based Android Botnet Detection Using Neural Networks

Saeed Seraj; Elias Pimenidis; Michalis Pavlidis; Stelios Kapetanakis; Marcello Trovati; Nikolaos Polatidis

doi:10.1007/978-3-031-34204-2_7

BotDroid: Permission-based Android Botnet Detection Using Neural Networks

Saeed Seraj, Elias Pimenidis, Michalis Pavlidis, Stelios Kapetanakis, Marcello Trovati, Nikolaos Polatidis

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Android devices can now offer a wide range of services. They support a variety of applications, including those for banking, business, health, and entertainment. The popularity and functionality of Android devices, along with the open-source nature of the Android operating system, have made them a prime target for attackers. One of the most dangerous malwares is an Android botnet, which an attacker known as a botmaster can remotely control to launch destructive attacks. This paper investigates Android botnets by using static analysis to extract features from reverse-engineered applications. Furthermore, this article delivers a new dataset of Android apps, including botnet or benign, and an optimized multilayer perceptron neural network (MLP) for detecting botnets infected by malware based on the permissions of the apps. Experimental results show that the proposed methodology is both practical and effective while outperforming other standard classifiers in various evaluation metrics.

Original language	English
Title of host publication	Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings
Editors	Lazaros Iliadis, Ilias Maglogiannis, Serafin Alonso, Chrisina Jayne, Elias Pimenidis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	71-84
Number of pages	14
Volume	1826
ISBN (Print)	9783031342035
DOIs	https://doi.org/10.1007/978-3-031-34204-2_7
Publication status	Published - 7 Jun 2023
Event	24th International Conference on Engineering Applications of Neural Networks - León, Spain Duration: 14 Jun 2023 → 17 Jun 2023 Conference number: 24th https://eannconf.org/2023/

Publication series

Name	Communications in Computer and Information Science
Volume	1826 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	24th International Conference on Engineering Applications of Neural Networks
Abbreviated title	EANN
Country/Territory	Spain
City	León
Period	14/06/23 → 17/06/23
Internet address	https://eannconf.org/2023/

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Android Malware detection
Botnets
Neural Networks
New dataset

Access to Document

10.1007/978-3-031-34204-2_7

EANNAccepted author manuscript, 463 KBLicence: Other

Cite this

Seraj, S., Pimenidis, E., Pavlidis, M., Kapetanakis, S., Trovati, M., & Polatidis, N. (2023). BotDroid: Permission-based Android Botnet Detection Using Neural Networks. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Eds.), Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings (Vol. 1826, pp. 71-84). (Communications in Computer and Information Science; Vol. 1826 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34204-2_7

Seraj, Saeed ; Pimenidis, Elias ; Pavlidis, Michalis et al. / BotDroid : Permission-based Android Botnet Detection Using Neural Networks. Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings. editor / Lazaros Iliadis ; Ilias Maglogiannis ; Serafin Alonso ; Chrisina Jayne ; Elias Pimenidis. Vol. 1826 Springer Science and Business Media Deutschland GmbH, 2023. pp. 71-84 (Communications in Computer and Information Science).

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abstract = "Android devices can now offer a wide range of services. They support a variety of applications, including those for banking, business, health, and entertainment. The popularity and functionality of Android devices, along with the open-source nature of the Android operating system, have made them a prime target for attackers. One of the most dangerous malwares is an Android botnet, which an attacker known as a botmaster can remotely control to launch destructive attacks. This paper investigates Android botnets by using static analysis to extract features from reverse-engineered applications. Furthermore, this article delivers a new dataset of Android apps, including botnet or benign, and an optimized multilayer perceptron neural network (MLP) for detecting botnets infected by malware based on the permissions of the apps. Experimental results show that the proposed methodology is both practical and effective while outperforming other standard classifiers in various evaluation metrics.",

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Seraj, S, Pimenidis, E, Pavlidis, M, Kapetanakis, S, Trovati, M & Polatidis, N 2023, BotDroid: Permission-based Android Botnet Detection Using Neural Networks. in L Iliadis, I Maglogiannis, S Alonso, C Jayne & E Pimenidis (eds), Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings. vol. 1826, Communications in Computer and Information Science, vol. 1826 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 71-84, 24th International Conference on Engineering Applications of Neural Networks, León, Spain, 14/06/23. https://doi.org/10.1007/978-3-031-34204-2_7

BotDroid: Permission-based Android Botnet Detection Using Neural Networks. / Seraj, Saeed; Pimenidis, Elias; Pavlidis, Michalis et al.
Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings. ed. / Lazaros Iliadis; Ilias Maglogiannis; Serafin Alonso; Chrisina Jayne; Elias Pimenidis. Vol. 1826 Springer Science and Business Media Deutschland GmbH, 2023. p. 71-84 (Communications in Computer and Information Science; Vol. 1826 CCIS).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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AU - Polatidis, Nikolaos

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ER -

Seraj S, Pimenidis E, Pavlidis M, Kapetanakis S, Trovati M, Polatidis N. BotDroid: Permission-based Android Botnet Detection Using Neural Networks. In Iliadis L, Maglogiannis I, Alonso S, Jayne C, Pimenidis E, editors, Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings. Vol. 1826. Springer Science and Business Media Deutschland GmbH. 2023. p. 71-84. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-34204-2_7

BotDroid: Permission-based Android Botnet Detection Using Neural Networks

Abstract

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Bibliographical note

Keywords

Access to Document

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