Smartphones have become essential in our daily lifelives. It alsoThey can do a lot of work and can, browse the Internetinternet, and download many applications for each device, through the available store. As a result, the number of malware applications downloaded also increases.
This malware carries out various activities behind the scenes; Such, such as breach of confidentiality, breach of privacy, loss of confidentiality, system breakdown, theft of sensitive information, etc.
Many types of research and studies thathave proposed different techniques to detect malicious programs, but they containedthese measure contain weak points, which are illustrated by efficiency, speed, and lack of comprehensiveness.
In this paper, a proposed system is designed and implemented to detect malware in smartphones and using anomaly detection technology. Thatthat begins to extract the important features that play an effective role in detecting malicious code and applying machine learning algorithms.
The proposed system has been tested by using a hybrid genetic algorithm, and the SVM data has been registered with an accuracy of (0.9282).
The experimental results indicate that the proposed system has a high average accuracy rate compared towith other existing methods where itthere is a (0.8848) average accuracy using PNN, while theand average accuracy isaccuracies of (0.8835) and (0.8715) respectively with SVM and K-NN respectively.