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

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

1 Citation (Scopus)

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. EANN 2023. Communications in Computer and Information Science
Editors	Lazaros Iliadis, Ilias Maglogiannis, Serafin Alonso, Chrisina Jayne, Elias Pimenidis
Publisher	Springer
Pages	71-84
Number of pages	14
Volume	1826
ISBN (Electronic)	978-3-031-34204-2
ISBN (Print)	978-3-031-34203-5
DOIs	https://doi.org/10.1007/978-3-031-34204-2_7
Publication status	Published - 7 Jun 2023

Publication series

Name	Communications in Computer and Information Science (CCIS)
Publisher	Springer
Volume	1856
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Keywords

BotDroid
Android Botnet
Botnet Detection
Neural Networks
Permission-Based Detection
Android Security
Mobile Malware
Machine Learning
Cybersecurity
Artificial Intelligence
Mobile Application Permissions
Threat Detection
Anomaly Detection
Malware Analysis
Security Algorithms

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Cite this

Seraj, S., Pimenidis, E., Pavlidis, M., Kapetanakis, S., TROVATI, MARCELLO., & 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. EANN 2023. Communications in Computer and Information Science (Vol. 1826, pp. 71-84). (Communications in Computer and Information Science (CCIS); Vol. 1856). Springer. 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. EANN 2023. Communications in Computer and Information Science. editor / Lazaros Iliadis ; Ilias Maglogiannis ; Serafin Alonso ; Chrisina Jayne ; Elias Pimenidis. Vol. 1826 Springer, 2023. pp. 71-84 (Communications in Computer and Information Science (CCIS)).

@inproceedings{acbb52a5b9dc4fec88fe67b5c1825f88,

title = "BotDroid: Permission-Based Android Botnet Detection Using Neural Networks",

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.",

keywords = "BotDroid, Android Botnet, Botnet Detection, Neural Networks, Permission-Based Detection, Android Security, Mobile Malware, Machine Learning, Cybersecurity, Artificial Intelligence, Mobile Application Permissions, Threat Detection, Anomaly Detection, Malware Analysis, Security Algorithms",

author = "Saeed Seraj and Elias Pimenidis and Michalis Pavlidis and Stelios Kapetanakis and MARCELLO TROVATI and Nikolaos Polatidis",

year = "2023",

month = jun,

day = "7",

doi = "10.1007/978-3-031-34204-2_7",

language = "English",

isbn = "978-3-031-34203-5",

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pages = "71--84",

editor = "Iliadis, {Lazaros } and Maglogiannis, {Ilias } and Alonso, {Serafin } and Jayne, {Chrisina } and Pimenidis, {Elias }",

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}

Seraj, S, Pimenidis, E, Pavlidis, M, Kapetanakis, S, TROVATI, MARCELLO & 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. EANN 2023. Communications in Computer and Information Science. vol. 1826, Communications in Computer and Information Science (CCIS), vol. 1856, Springer, pp. 71-84. 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. EANN 2023. Communications in Computer and Information Science. ed. / Lazaros Iliadis; Ilias Maglogiannis; Serafin Alonso; Chrisina Jayne; Elias Pimenidis. Vol. 1826 Springer, 2023. p. 71-84 (Communications in Computer and Information Science (CCIS); Vol. 1856).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

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T1 - BotDroid: Permission-Based Android Botnet Detection Using Neural Networks

AU - Seraj, Saeed

AU - Pimenidis, Elias

AU - Pavlidis, Michalis

AU - Kapetanakis, Stelios

AU - TROVATI, MARCELLO

AU - Polatidis, Nikolaos

PY - 2023/6/7

Y1 - 2023/6/7

N2 - 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.

AB - 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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KW - Permission-Based Detection

KW - Android Security

KW - Mobile Malware

KW - Machine Learning

KW - Cybersecurity

KW - Artificial Intelligence

KW - Mobile Application Permissions

KW - Threat Detection

KW - Anomaly Detection

KW - Malware Analysis

KW - Security Algorithms

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T3 - Communications in Computer and Information Science (CCIS)

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BT - Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science

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A2 - Maglogiannis, Ilias

A2 - Alonso, Serafin

A2 - Jayne, Chrisina

A2 - Pimenidis, Elias

PB - Springer

ER -

Seraj S, Pimenidis E, Pavlidis M, Kapetanakis S, TROVATI MARCELLO, 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. EANN 2023. Communications in Computer and Information Science. Vol. 1826. Springer. 2023. p. 71-84. (Communications in Computer and Information Science (CCIS)). doi: 10.1007/978-3-031-34204-2_7

BotDroid: Permission-Based Android Botnet Detection Using Neural Networks

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