Search

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.

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.

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode.