Considering the alarming increase in cyberattacks and their potential financial implications, the importance of cybersecurity education and training cannot be overstated. This paper presents a systematic literature review that examines different cybersecurity education and training techniques with a focus on symmetry. It primarily focuses on traditional cybersecurity education techniques and emerging technologies, such as virtual reality (VR) and augmented reality (AR), through the lens of symmetry. The main objective of this study is to explore the existing cybersecurity training techniques, identify the challenges involved, and assess the effectiveness of cybersecurity training based on VR and AR while emphasizing the concept of symmetry. Through careful selection criteria, 66 primary studies were selected from a total of 150 pertinent research studies. This article offers valuable insights into the pros and cons of conventional training approaches, explores the use of VR and AR in cybersecurity education concerning symmetry, and thoroughly discusses the challenges associated with these technologies. The findings of this review contribute significantly to the continuing efforts in cybersecurity education by offering recommendations for improving employees’ knowledge, engagement, and motivation in cybersecurity training programs while maintaining symmetry in the learning process.

Internet of Things (IoT) has rapidly developed in multidisciplinary research topics, particularly in Cyber-Physical infrastructures, such as smart-health care, transportation systems, vehicle management surveillance systems. The smart-video surveillance system has become an essential part of almost all security applications, including academic institutions. University campuses have rich video repositories comprising almost all kinds of academic and non-academic activities. Researchers have introduced many state-of-art activity recognition methods for various application domains with the availability of several activity data sets. Unfortunately, none of these data sets or methods have been developed explicitly for academia and do not cover academic activities. With the advancement of deep learning and IoT, the processing of large-scale video data has become convenient for performing various video analysis tasks. Thus, in this work, an automated deep learning-based academic activities recognition system is presented in smart-cyber infrastructure. We explore a new academic campus domain for research and proposed a novel Convolutional Neural Network (CNN) model for academic activities recognition utilizing a realistic campus dataset. The video database typically contains long, 24-hour video streams recorded by surveillance cameras installed in campus environments. The proposed model's efficiency is tested through extensive experimentation in terms of accuracy, computation time, and memory requirement. The experimental results reveal that the proposed method attains good results with an accuracy of 98%.

The explosive growth of video data traffic brings a great pressure on the future 5G networks. Deploying caches at the radio access network and evolved packet core in the 5G network can effectively relieve this pressure by reducing duplicate content retransmission. In this paper, we propose a new collaborative cache scheme for the future 5G wireless network. The proposed scheme aims to improve the data availability and access rate locally easing collaborative local resources of the 5G wireless hotspot network. We investigate the impact of different constraints on the collaborative cache scheme, including limited storage capacity, placement of content, content popularity, and distribution of content. By considering these constraints, we effectively improve the overall performance of the 5G wireless network in terms of transmission delay, local availability of contents, and efficient utilization of caches within the network hotspot. Our proposed scheme provides an efficient utilization of the hotspot cache storage by using Zipf distribution and knapsack, and it is also able to entertain the user request partially available in the hotspot. Simulation results reveal that compared with conventional collaborative cache schemes, the proposed scheme for the 5G wireless network can significantly reduce transmission delay and increase hit-ratio within the hotspot.

Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming. Effective video summarization methods are thus needed for fast navigation and retrieval of video content. This paper introduces a keyframe extraction method to summarize academic activities to produce a short representation of the target video while preserving all the essential activities present in the original video. First, we perform fine-grain activity recognition using a realistic Campus Activities Dataset (CAD) by modeling activity attention scores using a deep CNN model. In the second phase, we use the generated attention scores for each activity category to extract significant video frames. Finally, we evaluate the inter-frame similarity index used to reduce the number of redundant frames and extract only the representative keyframes. The proposed framework is tested on different videos, and the experimental results show the performance of the proposed summarization process.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

Particle swarm optimization (PSO) is an intelligent searching technique for solving complicated and multimodal design optimization’s problems. The classical PSO algorithm is more flexible and efficient because of its ability to solve a diverse range of complex and real-world issues. Moreover, the primary deficiency of this method that it trapped and stuck to local minima during the optimization of multimodal, complex and inverse objective function. We introduce a crossover and mutation vectors in the conventional PSO to solve this deficiency. The differential evolution strategies inspired the novel vectors. The central idea of the proposal is that, the novel global best particle is updated through a mutation vector and crossover vector. The introduction of the global best particle maintains the swarm diversity at the final steps of the evolution process. Also, we designed a novel strategy for the control parameter, which will maintain a decent alignment of the candidates between the global and local searches. The performance evaluation table and trajectory curves illustrate that our proposed approach is the best compared to other methods.