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Title: :  Mobile Botnet Detection Using Convolutional Neural Networks(CNN)
PaperId: :  23822
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-23822
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Vaishnavi Haribhau AwariDattakala Group of Institutions Faculty of Engineering
Pradnya Baban KalangeDattakala Group of Institutions Faculty of Engineering
Sanjivni Shivaji MundeDattakala Group of Institutions Faculty of Engineering
Supriya Hirachand PhalleDattakala Group of Institutions Faculty of Engineering
Dr. Sachin Sukhadeo BereDattakala Group of Institutions Faculty of Engineering
Prof. Swati Mahadev Atole Dattakala Group of Institutions Faculty of Engineering

Abstract

Computer engineering
Machine learning, Deep learning, Convolutional Neural Networks (CNN), System call analysis, Botnet detection, Mobile botnets.
Botnets have been a serious threat to the Internet security. With their constant sophistication and the resilience of them, a new trend has emerged, shifting botnets from the traditional desktop to the mobile environment. As in the desktop domain, detecting mobile botnets is essential to minimize the threat that they impose. Along the diverse set of strategies applied to detect these botnets, the ones that show the best and most generalized results involved is- covering patterns in their anomalous behavior. In the mobile botnet field, one way to detect these patterns is by analyzing the operation parameters of this kind of application. In this paper, we present an anomaly-based and host-based approach to detect mobile botnets. The proposed approach uses machine learning algorithms to identify anomalous behaviors in statistical features extracted from system calls. We were able to test the performance of our approach in a close-to reality scenario. The proposed approach achieved great results, including low false positive rates and high true detection rates. Index Terms—Machine learning, Deep learning, Convolutional Neural Networks (CNN), Botnet Detection, etc.

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IJARIIE Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . "Mobile Botnet Detection Using Convolutional Neural Networks(CNN)" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 1443-1451
MLA Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . "Mobile Botnet Detection Using Convolutional Neural Networks(CNN)." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 1443-1451.
APA Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, & Prof. Swati Mahadev Atole . (2024). Mobile Botnet Detection Using Convolutional Neural Networks(CNN). International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 1443-1451.
Chicago Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . "Mobile Botnet Detection Using Convolutional Neural Networks(CNN)." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 1443-1451.
Oxford Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . 'Mobile Botnet Detection Using Convolutional Neural Networks(CNN)', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 1443-1451. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Mobile_Botnet_Detection_Using_Convolutional_Neural_Networks_CNN__ijariie23822.pdf (Accessed : 22 May 2024).
Harvard Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . (2024) 'Mobile Botnet Detection Using Convolutional Neural Networks(CNN)', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 1443-1451IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Mobile_Botnet_Detection_Using_Convolutional_Neural_Networks_CNN__ijariie23822.pdf (Accessed : 22 May 2024)
IEEE Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole , "Mobile Botnet Detection Using Convolutional Neural Networks(CNN)," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 1443-1451, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Mobile_Botnet_Detection_Using_Convolutional_Neural_Networks_CNN__ijariie23822.pdf [Accessed : 22 May 2024].
Turabian Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . "Mobile Botnet Detection Using Convolutional Neural Networks(CNN)." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 (22 May 2024).
Vancouver Vaishnavi Haribhau Awari, Pradnya Baban Kalange, Sanjivni Shivaji Munde, Supriya Hirachand Phalle, Dr. Sachin Sukhadeo Bere, and Prof. Swati Mahadev Atole . Mobile Botnet Detection Using Convolutional Neural Networks(CNN). International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 22 May 2024]; 10(3) : 1443-1451. Available from: https://ijariie.com/AdminUploadPdf/Mobile_Botnet_Detection_Using_Convolutional_Neural_Networks_CNN__ijariie23822.pdf
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