Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods for static networks often fall short when applied to temporal networks. This paper introduces DGSP-GCN (Dynamic Graph Similarity Prediction based on Graph Convolutional Network), a deep learning-based framework that integrates graph convolutional networks with dynamic graph signal processing techniques to provide a unified solution for evaluating generative models and detecting anomalies in dynamic networks. DGSP-GCN assesses how well a generated network snapshot matches the expected temporal evolution, incorporating an attention mechanism to improve embedding quality and capture dynamic structural changes. The approach was tested on five real-world datasets: WikiMath, Chickenpox, PedalMe, MontevideoBus, and MetraLa. Results show that DGSP-GCN outperforms baseline methods, such as time series regression and random similarity assignment, achieving the lowest error rates (MSE of 0.0645, MAE of 0.1781, RMSE of 0.2507). These findings highlight DGSP-GCN's effectiveness in evaluating and detecting anomalies in dynamic networks, offering valuable insights for network evolution and anomaly detection research.

Abstract Protection of computer systems is a challenge facing the users, who usually define passwords, fingerprints, face detection patterns, and other identification solutions in order to secure their systems against the misuse and unauthorized access. Nevertheless, these solutions are effective in preventing anonymous people from logging in to the system. If a user leaves a system unlocked for a while or a password has already been disclosed for any reason, such trivial solutions will then fail to secure the system. In this study we introduces new dynamic features considering the time, category and type of the applications a user uses and use them in combination with existing operation-related features in a anomaly detection framework for user authentication. A combination of operation-related and application-related features are then taken into account to create a base profile for each authenticated user in order to detect any unauthorized access. The proposed method can secure systems even if an unauthorized access occurs. In other words, this method compares the current user’s behaviour with the base profile of authenticated user momentarily. If an anomaly is detected, that user is recognized as an unauthorized user and will then be prohibited from working with the system or asked to undergo a two-step authentication process.