TY - EJOUR
AU - Mohammed, Mohammed Abdulkreem
AU - Smait, Drai Ahmed
AU - Al-Tahai, Mustafa
AU - Kamil, Israa S.
AU - Al-Majdi, Kadhum
AU - Khaleel, Shahad K.
PY - 2024
DA - February
TI - Malware Detection using Deep Neural Networks on Imbalanced Data
T2 - Majlesi Journal of Electrical Engineering
VL - 16
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/malware-detection-using-deep-neural-networks-on-imbalanced-data/
DO - 10.30486/mjee.2022.696523
N2 - Through the use of malware, particularly JavaScript, cybercriminals have turned online applications into one of their main targets for impersonation. Detection of such dangerous code in real-time, therefore, becomes crucial in order to prevent any harmful action. By categorizing the salient characteristics of the malicious code, this study suggests an effective technique for identifying malicious Java scripts that were previously unknown, employing an interceptor on the client side. By employing the wrapper approach for dimensionality reduction, a feature subset was generated. In this paper, we propose an approach for handling the malware detection task in imbalanced data situations. Our approach utilizes two main imbalanced solutions namely, Synthetic Minority Over Sampling Technique (SMOTE) and Tomek Links with the object of augmenting the data and then applying a Deep Neural Network (DNN) for classifying the scripts. The conducted experiments demonstrate the efficient performance of our approach and it achieves an accuracy of 94.00%.
IS - 4
PB - OICC Press
KW - Convolutional neural networks, Malware detection, Imbalanced Data, Tomek Links., SMOTE 
EN -