Clustering using a random walk on graph for head pose estimation

Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm’s ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.

Section: Proposed Approachmentioning

confidence: 99%

“…Based on the above formulation, and assuming that the Markov chain is nondegradable, genuinely repetitive, and nonperiodic, then regardless of its initial distribution, we have [ 30 , 32 , 34 ]

…”

Section: Proposed Approachmentioning

confidence: 99%

Cyber Risk Recommendation System for Digital Education Management Platforms

Yin¹,

Chen²

2022

“…Therefore, there are many “open” triangles, and the most suitable ERGM for the occasion is the 2-star model, which is calculated as [ 34 , 38 , 39 ]follows:

where a ij is the element of the graph adjacent table A (with value 1 if there is an edge between nodes i and j , and 0 otherwise), m ( G ) is the network statistic that models the number of edges of the graph (whose influence is controlled by the hyperparameter i ), and s ( G ) is the corresponding magnitude for the number of 2 stars (whose influence is controlled by the hyperparameter t, respectively). It should also be noted that in this case, the graph is nondirectional (i.e., for the i and j elements of the neighborhood table, the equation a ij = a ji applies).…”

Section: Proposed Methodologymentioning

confidence: 99%

An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services

Chen¹,

Yang²

2022

The COVID-19 crisis has once again highlighted the vulnerabilities of some critical areas in cyberspace, especially in the field of education, as distance learning and social distance have increased their dependence on digital technologies and connectivity. Many recent cyberattacks on e-learning systems, educational content services, and trainee management systems have created severe demands for specialized technological solutions to protect the security of modern training methods. Email is one of the most critical technologies of educational organizations that are attacked daily by spam, phishing campaigns, and all kinds of malicious programs. Considering the efforts made by the global research community to ensure educational processes, this study presents an advanced deep attention collaborative filter for secure academic email services. It is a specialized application of intelligent techniques that, for the first time, examines and models the problem of spam as a system of graphs where collaborative referral systems undertake the processing and analysis of direct and indirect social information to detect and categorize spam emails. In this study, nonnegative matrix factorization (NMF) is applied to the social graph adjacent table to place users in one (or more) overlapping communities. Also, using a deep attention mechanism, it becomes personalized for each user. At the same time, with the introduction of exponential random graph models (ERGMs) in the process of factorization, local dependencies are significantly mitigated to achieve the revelation of malicious communities. This methodology is being tested successfully in implementing mail protection systems for educational organizations. According to the findings, the proposed algorithm outperforms all other compared algorithms in every metric tested.

“…But, for continuous features, we create a new distribution (normal distribution or sum of Gaussian nuclei). Under the previously mentioned conditions (assumptions), the proposed algorithm that we use in this work is as follows [ 15 , 20 – 22 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Semisupervised Majority Weighted Vote Antiphishing Attacks IDS for the Education Industry

Yin¹,

Zheng²

2022

Although the digital transformation is advancing, a significant portion of the population in all countries of the world is not familiar with the technological means that allow malicious users to deceive them and gain great financial benefits using phishing techniques. Phishing is an act of deception of Internet users. The perpetrator pretends to be a credible entity, abusing the lack of protection provided by electronic tools and the ignorance of the victim (user) to illegally obtain personal information, such as bank account codes and sensitive private data. One of the most common targets for digital phishing attacks is the education sector, as distance learning became necessary for billions of students worldwide during the pandemic. Many educational institutions were forced to transition to the digital environment with minimal or no preparation. This paper presents a semisupervised majority-weighted vote system for detecting phishing attacks in a unique case study for the education sector. A realistic majority weighted vote scheme is used to optimize learning ability in selecting the most appropriate classifier, which proves to be exceptionally reliable in complex decision-making environments. In particular, the voting naive Bayes positive algorithm is presented, which offers an innovative approach to the probabilistic part-supervised learning process, which accurately predicts the class of test snapshots using prerated training snapshots only from the positive class examples.