Truong Thu Huong

Duc V. Nguyen, Nguyen Thi Quynh Ly, Truong Thu Huong

A dynamic 3D mesh is a key component in Virtual Reality applications. However, this type of content demands a significant processing resource for real-time rendering. To reduce processing requirements while preserving the user experience, adjusting the level of detail of 3D meshes based on viewing distance has been proposed. In this paper, we conduct an extensive subjective quality evaluation to investigate the effects of the level of detail and viewing distance on user perception of dynamic 3D meshes in a VR environment. Our evaluation results in a subjective dataset containing user ratings of 320 test stimuli generated from eight dynamic 3D meshes. Result analysis shows that it is possible to remove half of a mesh's faces without causing noticeable degradation in user Quality of Experience (QoE). An evaluation of popular objective quality metrics reveals that both 2D-based and 3D-based metrics have low correlation with subjective scores. Based on the subjective dataset, we develop a novel QoE prediction model that can accurately predict the MOS of a dynamic 3D mesh at a given level of detail and viewing distance. In addition, a QoE-aware resource allocation framework is proposed and evaluated under different resource constraints, showing significant improvement in the total QoE compared to conventional methods.

Vu Thi Hai Yen, Duc V. Nguyen, Cao Anh Minh Huy et al.

Short-form videos have become one of the most popular user-generated content formats nowadays. Popular short-video platforms use a simple streaming approach that preloads one or more videos in the recommendation list in advance. However, this approach results in significant data wastage, as a large portion of the downloaded video data is not used due to the user's early skip behavior. To address this problem, the chunk-based preloading approach has been proposed, where videos are divided into chunks, and preloading is performed in a chunk-based manner to reduce data wastage. To optimize chunk-based preloading, it is important to understand the user's viewing behavior in short-form video streaming. In this paper, we conduct a measurement study to construct a user behavior dataset that contains users' viewing times of one hundred short videos of various categories. Using the dataset, we evaluate the performance of standard time-series forecasting algorithms for predicting user viewing time in short-form video streaming. Our evaluation results show that Auto-ARIMA generally achieves the lowest and most stable forecasting errors across most experimental settings. The remaining methods, including AR, LR, SVR, and DTR, tend to produce higher errors and exhibit lower stability in many cases. The dataset is made publicly available at https://nvduc.github.io/shortvideodataset.

Truong Thu Huong, Ta Phuong Bac, Dao M. Long et al.

Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the workload of the cloud. We also propose a multi attack detection mechanism called LocKedge Low Complexity Cyberattack Detection in IoT Edge Computing, which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.

Huyen T. T. Tran, Duc V. Nguyen, Nam Pham Ngoc et al.

Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the human quality perception of omnidirectional images, focusing on different zones surrounding the foveation point. For that purpose, an extensive subjective experiment is carried out to assess the perceptual quality of omnidirectional images with non-uniform quality. Through experimental results, the impacts of different zones are analyzed. Moreover, nineteen objective quality metrics, including foveal quality metrics, are evaluated using our database. It is quantitatively shown that the zones corresponding to the fovea and parafovea of human eyes are extremely important for quality perception, while the impacts of the other zones corresponding to the perifovea and periphery are small. Besides, the investigated metrics are found to be not effective enough to reflect the quality perceived by viewers.

Trung V. Phan, T M Rayhan Gias, Syed Tasnimul Islam et al.

Software Defined Networking (SDN) enables flexible and scalable network control and management. However, it also introduces new vulnerabilities that can be exploited by attackers. In particular, low-rate and slow or stealthy Denial-of-Service (DoS) attacks are recently attracting attention from researchers because of their detection challenges. In this paper, we propose a novel machine learning based defense framework named Q-MIND, to effectively detect and mitigate stealthy DoS attacks in SDN-based networks. We first analyze the adversary model of stealthy DoS attacks, the related vulnerabilities in SDN-based networks and the key characteristics of stealthy DoS attacks. Next, we describe and analyze an anomaly detection system that uses a Reinforcement Learning-based approach based on Q-Learning in order to maximize its detection performance. Finally, we outline the complete Q-MIND defense framework that incorporates the optimal policy derived from the Q-Learning agent to efficiently defeat stealthy DoS attacks in SDN-based networks. An extensive comparison of the Q-MIND framework and currently existing methods shows that significant improvements in attack detection and mitigation performance are obtained by Q-MIND.