Due to the expansion of cybercrime and cyberwarfare, the necessity for cyber security has recently expanded substantially. There are several trends in cyber security, but the most important is e-governance. E-government is regarded as one of the most essential platforms for transmitting data and services over the internet that frequently contain valuable and confidential data, making them subject to threats. The majority of egovernance systems rely on public-use services. The paper proposed a framework to detect threats in internet traffic flows. This paper uses a famous dataset that was collected from internet traffic called the UNSW-NB15 dataset, which consists of 307,099 instances. The framework consists of several steps, including pre-processing, identifying a correlation between features, and selecting the best ones. Finally, different machine learning algorithms are used to distinguish the normal traffic from the malware traffic. The findings uncover that SVM achieved very high accuracy (99.16%). Additionally, in the second part, which is called multi-class and consists of two stages, in the first one the study classified the abnormal flows into nine attacks with a lower accuracy of 77.80%. In the second stage with binary classification, the dataset contained both normal and abnormal, and the accuracy improved significantly to 97.48% for SVM.

With the rising use of Internet technologies around the world, the number of network intruders and attackers has skyrocketed. For this reason, introducing systems for detecting attacks within the network security measures stops hackers from gaining access to data. In order to detect various forms of assaults, the development of intrusion detection systems is quite crucial. The election of features and elimination of the unrelated information can improve the classifier's accuracy execution because the network traffic dataset contains many useful and unhelpful features. Along these lines, in this work, three hybrid strategies for selecting the feature were implemented, which include the constant feature by standard deviation, the Quasi constant by variance threshold, and the information gain. These approaches were used to order and rank features, after which the best higher ranking was selected for classification and intrusion detection. These features were tested on three classifiers long shortـterm memory, convolutional neural network, and CNN-LSTM. From the acquired results, a high level of attacks was detected, and classification accuracy was achieved by cross-breeding the selection of the different best features. Furthermore, the convolutional neural network classifier achieved the highest accuracy rate, which exceeded the value of 99.5%.

The main objective of the proposed study is to develop an e-learning system using augmented reality technology one of the main problems faces using AR in education is the huge computational power needed to transfer 2D animation to enrich learning facilities. Such problem increases when using smart mobile devices that suffer from hardware limitation. A promising framework is used to utilize cloud services to support augmented reality applications on the cloud. Such method significantly reduces consumption of memory and processing units when dealing with large size videos or images. Hence the augmented reality processing is speeded up to meet the requirements of E-learning systems. The proposed work was conducted on 100 students from different academic levels in the first semester of the year 2022. Three experiments were conducted for different fields of education including two-dimensional images using Unity Program (3D Software) to draw 3D objects and Vufoira software development kit. The experimental results showed promising results as the application has the flexibility to work on different platforms. Moreover the consumed memory to run the application is reduced significantly. The results also showed high performance for the application when drawing complex 3D images and when dealing with different animations. The study supported with a detailed questioner that proofs the importance of AR in the field of E-learning.