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Title: :  ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES
PaperId: :  26477
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26477
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
S. Saiful IslamKV SUBBA REDDY ENGINEERING COLLEGE
J. Lokesh ReddyKV SUBBA REDDY ENGINEERING COLLEGE
S. Suhel AhmadKV SUBBA REDDY ENGINEERING COLLEGE
Sk. FarhanKV SUBBA REDDY ENGINEERING COLLEGE
G Emmanuel RajuKV SUBBA REDDY ENGINEERING COLLEGE

Abstract

COMPUTER SCIENCE AND ENGINEERING
Android Malware Detection, Ensemble Learning, Deep Learning, Hunter-Prey Optimization, Cybersecurity
Current technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that is often utilized to launch cyberattacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification and detection more challenging. New techniques that are different from conventional systems should be utilized for effectively combating new malware variants. Machine learning (ML) methods are ineffective in identifying all complex and new malware variants. The deep learning (DL) method can be a promising solution to detect all malware variants. This project presents an Automated Android Malware Detection using Optimal Ensemble Learning Approach for Cybersecurity (AAMDOELAC) technique. The major aim of the AAMD-OELAC technique lies in the automated classification and identification of Android malware. To achieve this, the AAMD- OELAC technique performs data preprocessing at the preliminary stage. For the Android malware detection process, the AAMD-OELAC technique follows an ensemble learning process using three ML models, namely Least Square Support Vector Machine (LS- SVM), kernel extreme learning machine (KELM), and Regularized random vector functional link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is exploited for the optimal parameter tuning of the three DL models, and it helps accomplish improved malware detection results. To denote the supremacy of the AAMD-OELAC method, a comprehensive experimental analysis is conducted.

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IJARIIE S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. "ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 474-480
MLA S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. "ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 474-480.
APA S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, & G Emmanuel Raju. (2025). ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 474-480.
Chicago S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. "ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 474-480.
Oxford S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. 'ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 474-480. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/ANDROID_MALWARE_DETECTION_USING_MACHINE_LEARNING_TECHNIQUES_ijariie26477.pdf (Accessed : ).
Harvard S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. (2025) 'ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 474-480IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/ANDROID_MALWARE_DETECTION_USING_MACHINE_LEARNING_TECHNIQUES_ijariie26477.pdf (Accessed : )
IEEE S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju, "ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 474-480, May-Jun 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/ANDROID_MALWARE_DETECTION_USING_MACHINE_LEARNING_TECHNIQUES_ijariie26477.pdf [Accessed : ].
Turabian S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. "ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver S. Saiful Islam, J. Lokesh Reddy, S. Suhel Ahmad, Sk. Farhan, and G Emmanuel Raju. ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 474-480. Available from: https://ijariie.com/AdminUploadPdf/ANDROID_MALWARE_DETECTION_USING_MACHINE_LEARNING_TECHNIQUES_ijariie26477.pdf
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