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An Efficient Android Malware Detection Using Adaptive Red Fox Optimization Based CNN

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Abstract

Android smartphones are employed widely due to its flexible programming system with several user-oriented features in daily lives. With the substantial growth rate of smartphone technologies, cyber-attack against such devices has surged at an exponential rate. Majority of the smartphone users grant permission blindly to various arbitrary applications and hence it weakens the efficiency of the authorization mechanism. Numerous approaches were established in effective malware detection, but due to certain limitations like low identification rate, low malware detection rate as well as category detection, the results obtained are ineffective. Therefore, this paper proposes a convolutional neural network based adaptive red fox optimization (CNN-ARFO) approach to detect the malware applications as benign or malware. The proposed approach comprising of three different phases namely the pre-processing phase, feature extraction phase and the detection phase for the effective detection of android malware applications. In the pre-processing phase, the selected dataset utilizes Minmax technique to normalize the features. Then the malicious APK and the collected benign apps are investigated to identify and extract the essential features for the proper functioning of malware in the extraction phase. Finally, the android mobile applications are detected using CNN based ARFO approach. Then the results based on detecting the benign and malicious applications from the android mobiles are demonstrated by evaluating certain parameters like model accuracy rate, model loss rate, accuracy, precision, recall and f-measure. The resulting outcome revealed that the detection accuracy achieved by the proposed approach is 97.29%.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Excel Engineering College, Komarapalayam, Namakkal District, Tamil Nadu, India

    P. C. Senthil Mahesh

  2. Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, Tamil Nadu, India

    S. Hemalatha

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  1. P. C. Senthil Mahesh
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  2. S. Hemalatha
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Contributions

PCSM agreed on the content of the study. PCSM and SH collected all the data for analysis. PCSM agreed on the methodology. PCSM and SH completed the analysis based on agreed steps. Results and conclusions are discussed and written together. Both author read and approved the final manuscript.

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Correspondence to P. C. Senthil Mahesh.

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Mahesh, P.C.S., Hemalatha, S. An Efficient Android Malware Detection Using Adaptive Red Fox Optimization Based CNN. Wireless Pers Commun 126, 679–700 (2022). https://doi.org/10.1007/s11277-022-09765-0

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