No abstract

Mobile malware is malicious software designed specifically for targeting various mobile gadgets like tablets, smartphones, and so forth, in which any type of malicious code affecting the mobile devices without the knowledge of the user. The increasing number of users encourages the hacker for generating various malware applications. Therefore, in this paper, we utilized three vital phases namely the pre‐processing process, Feature extraction process as well as classification process in which the malicious data are detected. In the pre‐processing phase, an Androguard tool is used for decompiling and disassembling the android applications. The API call features are extracted in the feature extraction phase and in the classification phase, long short term memory based electro search optimization (LSTM‐ESO) is employed to detect the unknown mobile applications as benign or malicious. The malicious mobile detecting accuracy deals in requesting permission and exhibiting malicious code applications. In order to enhance the identification of various malware applications, this paper utilized frequency analysis and permissions of API calls. Finally, the experimental analysis is performed by evaluating the performance measures like accuracy, precision, recall, and F‐measure. From the evaluation outcome, it is observed that the classification accuracy obtained is 97.69%.

No abstract