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Neural network models are black boxes in nature. The mechanics behind these black boxes are practically unexplainable. Having the insight into patterns identified by these algorithms can help unravel important properties of the subject in query. These artificial intelligence based algorithms are used in every domain for prediction. This research focuses on patterns formed in network traffic that can be leveraged to identify videos streaming over the network. The proposed work uses a sufficient input subset (SIS) model on two separate video identification techniques to understand and explain the patterns detected by the techniques. The first technique creates the fingerprints of videos on a period-based algorithm to handle variable bitrate inconsistencies. These fingerprints are passed to a convolutional Neural Network (CNN) for pattern recognition. The second technique is based on traffic pattern plot identification that creates a graph of packet size with respect to time for each stream before passing that to a CNN as an image. For model explainability, a sufficient input subset (SIS) model is used to identify features that are sufficient to reach the same prediction under a certain threshold of confidence by the model. The generated SIS of each input sample is clustered using DBSCAN, K-Means, and cosine-based Hierarchical clustering. The clustered SIS highlight the common patterns for each class. The SIS patterns learnt by each model of three individual videos are discussed. Furthermore, these patterns are used to investigate misclassification and provide a rationale behind it to justify the working of the classifier model.

Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users’ activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.

Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow researches to tested the use of m-learning for regular students while they are studying in distance learning environment. However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework was depended on the model of Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The results indicated that significant factors influencing distance learning students’ behavioral intention include quality of service, effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher Education are having a good impact and encouraging widespread use of m-learning.

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.