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Android is currently one of the most popular mobile operating systems in the world. The popularity and flexibility of the Android platform makes it a prime target for malicious attackers. Android malware with malicious charges and privacy theft poses a serious threat to users' security. With the rapid update of Android apps and the growing number of malware types, existing Android app stores all lack comprehensive malware detection methods and prevention mechanisms. Previous studies have shown that the detection methods based on feature codes are easily bypassed by deformed malicious codes and rely on the feature code database. However, the detection method based on machine learning can deal with different varieties of malicious code to some extent, but this method needs to transform the application program into feature vector through feature engineering, and then build a classifier to detect and classify the malware quickly. In the field of machine learning, the feature information selected by traditional feature selection methods suffers from low contribution value and high redundancy, which leads to the low accuracy of detection models. The classical genetic algorithm also has disadvantages such as slow convergence speed, poor local search ability, and easily influenced by parameters. Therefore, this paper proposes an improved hybrid adaptive genetic algorithm (HAGA) as feature selection and combines with a machine learning classifier, and then uses the accuracy of the classifier as an evaluation criterion to provide an effective method for Android malware detection.
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