Electrical Engineering


Recent Submissions

Now showing 1 - 2 of 2
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    Hybrid Anomaly Based Android Malware Detection Using Deep Neural Networks
    (Al-Quds University, 2023-08-29) Maher George Mousa Maria; ماهر جورج موسى ماريا
    A malware detection system for mobile devices contributes to the field of computer security. Cybersecurity is a major current problem mainly motivated by the growing number of malwares; data loss due to computer breaches cost a great loss. In addition ethical problems. Due to the popularity of smartphones and tablets, mobile devices are becoming the target of malware and cyberattacks. It is therefore essential to explore new ways to prevent, detect and counter cyberattacks. In these detection mechanisms, machine learning is used to create classifiers that determine whether an application is compromised. The advantage of a neural network is that it allows you to adapt to new situations. Therefore, we used this new technology to be able to identify types of malicious behavior and to be able to generalize it to future malicious programs. The goal of this thesis is to propose a malware detection model on Android based on deep neural networks classification driven by sets of hybrid features. We reviewed and classified existing methods into two groups: the static methods which consist of examining the code of the mobile application and the dynamic methods which analyze the behavior of an application when it is running on a mobile terminal. Our goal is to use these two methods to take advantage of the both groups. To do this, we used the hybrid database “AMD” composed of 85 features. We are also conducted an experiment plan composed of hundreds of trainings in order to adjust the values of the hyperparameters improving the learning on this dataset as well as to select the most relevant remaining features, through this thesis, we work according to the most effective features from the AMD Dataset. And to improve detection accuracy that have time-dependent frequencies such as attacks, three new input features (s_sessiontime, r_sessiontime, and sr_sessionime) are devised by aggregating the flows based on source, destination, and timestamp attributes using a time window of one minute. Also, after preprocessing the input features, the most important 45 input features are selected. Moreover, the model’s parameters are learned using many multiclass labeled flows from the AMD dataset. The hyperparameters of the model are optimized for best performance in terms of accuracy, recall, precision, and training time of the model. The experimental results confirmed the high performance of the proposed model when tested from the “AMD” dataset. In addition, the optimal model architecture consists of one input layer, three hidden layers and one output layer. The model achieved an accuracy of 99.8 %, a false positive rate of less than 1%, and an area under the receiver operating characteristic curve (ROC-AUC) of 0.999. Also, the detection accuracy of the multiclass classifier is 99.6% When the proposed model is compared with other recent models in literature, that was evaluated on similar datasets like” AMD”, the experimental results show that the proposed model outperforms other models in terms of precision and recall.
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    تحليل الأخطاء الكتابية في اللغة الانجليزية كلغة أجنبية للدارسين من الطلبة الفلسطينيين في مدارس أريحا الحكومية
    (AL-Quds University, 2011-05-10) زياد مصطفى محمود دهنون بريقع; Ziyyad Mustafa Mahmoud 'Dahnoun Breiq'; سمير رمال; Ahmad Jaber; Adnan Shehadeh