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Title: :  AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY
PaperId: :  23551
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23551
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
T.SUNDARARAJULUSIDDHARTH INSTITUTE OF ENGINEERING AND TECHNOLOGY
M.LAKSHMANSIDDHARTH INSTITUTE OF ENGINEERING AND TECHNOLOGY
K.OMPRAKASHSIDDHARTH INSTITUTE OF ENGINEERING AND TECHNOLOGY
M.BHAVYA SRISIDDHARTH INSTITUTE OF ENGINEERING AND TECHNOLOGY
P.ELIYAZ KHANSIDDHARTH INSTITUTE OF ENGINEERING AND TECHNOLOGY

Abstract

COMPUTER SCIENCE AND ENGINEERING
Malware, Android, optimal, Cyber security
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 T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. "AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 5165-5173
MLA T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. "AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 5165-5173.
APA T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, & P.ELIYAZ KHAN. (2024). AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 5165-5173.
Chicago T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. "AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 5165-5173.
Oxford T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. 'AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 5165-5173. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/AUTOMATED_ANDROID_MALWARE_DETECTION_USING_OPTIMAL_ENSEMBLE_LEARNING_APPROACH_FOR_CYBER_SECURITY_ijariie23551.pdf (Accessed : ).
Harvard T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. (2024) 'AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 5165-5173IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/AUTOMATED_ANDROID_MALWARE_DETECTION_USING_OPTIMAL_ENSEMBLE_LEARNING_APPROACH_FOR_CYBER_SECURITY_ijariie23551.pdf (Accessed : )
IEEE T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN, "AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 5165-5173, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/AUTOMATED_ANDROID_MALWARE_DETECTION_USING_OPTIMAL_ENSEMBLE_LEARNING_APPROACH_FOR_CYBER_SECURITY_ijariie23551.pdf [Accessed : ].
Turabian T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. "AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver T.SUNDARARAJULU, M.LAKSHMAN, K.OMPRAKASH, M.BHAVYA SRI, and P.ELIYAZ KHAN. AUTOMATED ANDROID MALWARE DETECTION USING OPTIMAL ENSEMBLE LEARNING APPROACH FOR CYBER SECURITY. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 5165-5173. Available from: https://ijariie.com/AdminUploadPdf/AUTOMATED_ANDROID_MALWARE_DETECTION_USING_OPTIMAL_ENSEMBLE_LEARNING_APPROACH_FOR_CYBER_SECURITY_ijariie23551.pdf
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