A Dimensionality Reduction Approach Based on Deep Learning and Black-Winged Kite Algorithm for Android Malware Detection
Subject Areas : مهندسی برق و کامپیوتر
mohsen Eghbali
1
,
M. Mollakhalili Meybodi
2
,
kamal mirzaie
3
1 - Dept. of Comp. Eng., Maybod Branch, Islamic Azad University, Maybod, Iran
2 - Dept. of Comp. Eng., Maybod Branch, Islamic Azad University, Maybod, Iran
3 - Dept. of Comp. Eng., Maybod Branch, Islamic Azad University, Maybod, Iran
Keywords: Machine learning, deep learning, feature selection, dimensionality reduction, black-winged kite (BKA) algorithm, Android malware.,
Abstract :
Today, with the increase in mobile devices, malware has also spread to the Android platform. These malware are written in more complex ways that are difficult to detect. Machine learning and deep learning methods are used to detect them because they can identify complex malware patterns. One challenge in malware detection with machine learning and deep learning methods is the high dimensionality of training samples. In this paper, a binary version of the Black-winged kite (BKA) algorithm is presented to reduce the dimensionality of training samples for the detection of Android malware. In the proposed method, the first stage extracts malware features using the BKA algorithm, which are then fed to the LSTM neural network. The LSTM's role is to classify Android malware samples as benign or malignant. To improve LSTM accuracy, its meta-parameters are also optimized using an Arithmetic optimization algorithm (AOA). Experiments on the CICandMal2017 dataset showed that the proposed method achieved accuracies of 98.63%, 98.29%, and 97.48% for accuracy, sensitivity, and precision, respectively. In the proposed approach, when balancing is performed using the GAN method on the CICandMal2017 dataset, the average accuracy, sensitivity, and precision of the proposed method increase to 99.62%, 98.93%, and 98.52%, respectively. Experiments show that the proposed method is more accurate at detecting malware than dimensionality-reduction methods such as WOA, HHO, and AVOA. The proposed method is about 16.4% more accurate than the LSTM neural network.
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