Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
In this study, a pioneer selective video encryption (PSVE) algorithm is proposed based on the pseudorandom number generator (PRNG) of the Zipf distribution (Z-PRNG). It is a general algorithm with high efficiency and security. The encryption process is completely separable from the video coding process. In the PSVE algorithm, Z-PRNG is designed based on the 3D SCL-HMC hyperchaotic map. Firstly, encapsulated byte sequence payloads (EBSPs) are extracted from the video bitstream. Secondly, random numbers of the Zipf distribution are generated by Z-PRNG, and they are used to randomly select encrypted data from each EBSP. Lastly, the extracted data are encrypted by AES-CTR to obtain the encrypted video. Compared with existing algorithms, the encryption position is more flexible, and the key space is further enhanced.Thehigh efficiency video coding (HEVC) video and the advanced video coding (AVC) video are taken as examples to test the PSVE algorithm.The analysis results show that the proposed scheme can effectively resist common attacks, and its time complexity is much less than most existing algorithms....
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....
Compression is an essential process to reduce the amount of information by reducing the number of bits; this process is necessary for uploading images, audio, video, storage services, and TV transmission. In this paper, image compressions with losses from this action will be shown for some common patterns. The compression process uses different mathematical equations that have different methods and efficiencies, so some common mathematical methods for each style are presented taking into consideration the pros and cons of each method. In this paper, it is demonstrated that there is a quality improvement by applying anisotropic interpolation to edge enhancement for its ability to satisfy the dispersed data of the propagation process, which leads to faster compression due to concern for optimum quality rather than fast algorithms. The test images for these patterns showed a discrepancy in the image resolution when the compression coefficient was increased, as the results using three types of image compression methods proved a clear superiority when using “partial differential equations (PDE)”....
COVID-19 has changed the way we use networks, as multimedia content now represents an even more significant portion of the traffic due to the rise in remote education and telecommuting. In this context, in which Wi-Fi is the predominant radio access technology (RAT), multicast transmissions have become a way to reduce overhead in the network when many users access the same content. However, Wi-Fi lacks a versatile multicast transmission method for ensuring efficiency, scalability, and reliability. Although the IEEE 802.11aa amendment defines different multicast operation modes, these perform well only in particular situations and do not adapt to different channel conditions. Moreover, methods for dynamically adapting them to the situation do not exist. In view of these shortcomings, artificial intelligence (AI) and machine learning (ML) have emerged as solutions to automating network management. However, the most accurate models usually operate as black boxes, triggering mistrust among human experts. Accordingly, research efforts have moved towards using Interpretable-AI models that humans can easily track. Thus, this work presents an Interpretable-AI solution designed to dynamically select the best multicast operation mode to improve the scalability and efficiency of this kind of transmission. The evaluation shows that our approach outperforms the standard by up to 38%....
Virtual reality technology (VR) has been widely used in education and teaching, but the results of a large number of experimental studies on the impact of VR on students’ technical skill learning are not consistent. In this study, a meta-analysis method was used to conduct in-depth quantitative analysis of 32 experimental or quasi-experimental research literatures based on virtual reality in the past 20 years. The results showed that: (1) virtual reality technology has a moderately positive effect on the learning of technical skills of science and engineering college students (SMD = 0:363); (2) the effect of VR on cognitive strategies (SMD = 0:487) was significantly greater than that of mental skills (SMD = 0:275) for technical skills under different measurement dimensions. (3) Different test periods, previous experience levels, and auxiliary methods have different degrees of positive influence on improving students’ technical skills, among which the 40-60 minutes experiment period (SMD = 0:484) has the greatest influence on the improvement of technical skills. According to the research conclusions, we suggest to make an overall planning for the construction of VR enabling teaching application scenarios and create a human-machine collaborative interactive teaching model of “theory+VR simulation+feedback” based on flow theory....
Loading....