Maha Adam Gumaa Osman2022-04-182022-04-182022-04http://hdl.handle.net/123456789/17161A thesis submitted in fulfillment of requirement for the degree of philosophy in Computer ScienceAbstract The number of mobile devices’ users such as; smartphones and tablets, is increasing. The invention of smartphones is one of the most important achievements in the Twenty-First century. Smartphones play a crucial role in our daily lives, in various fields. The Android operating system is one of the most widely used platforms these days, the rapid increase in the use of Android and free applications has contributed to a significant increase in building applications loaded with Malwares, which causes damage For devices such as (Adware, bot, Trojans horse) or be a reason for stealing sensitive information for users such as (Spyware, ransomwares) that locks the data on the victim’s device through encryption and demand payment for decryption of the data or re-access to the victim. These applications need to use a number of sensitive permission files during installation and runtime, Malware developers exploit this to launch attacks on users. In this research, an approach is proposed and developed based on the most imperative permissions and API calls. This was done by using a data set (Drebin) from the (Drebin) project that contains 15,036 applications and then identifying and extracting the most important features based on the graph that are effective in a process of detecting malware applications. Then use machine learning vitechniques to train and classify the malware detection tool. It was done by using four machine learning algorithms which are Random Forest Algorithm, K- Nearest Neighbor Algorithm, Decision Tree Algorithm and Logistic Regression Algorithm. The results of the experiment showed that this approach achieves an accuracy rate in the (KNN) algorithm and (DT) algorithm to 96% and an accuracy rate of up to 95% in the (Logistic Regression) algorithm. The best accuracy rate is 97% and the recall rate is 96%. When using the (Random Forest) algorithm, which proves the effectiveness and advantages of this approach.Machine LearningThesis