Abstract: One of the foremost significant challenges in the continuously increasing technological environment is the requirement to secure the authenticity of data. Network security is a primary method for securing the confidentiality of data throughout communication, one of several types of data security assurance. To secure networks against additional cyberattacks, trustworthy Anomaly Detection (AD) is essential. The drawbacks of conventional AD are gradually increasing as various types of attacks and network changes continually evolve. The researchers of the present study propose a novel approach that incorporates Weighted Long Short-Term Memory (WLSTM) networks with Dynamic Network Representation Learning (DNRL) to address these problems, referring to it as the Weighted Dynamic Network Representation Learning (WDNRL) paradigm. This investigation develops the WLSTM utilizing the Weight of Evidence (WoE), which periodically determines weights to network features in the resulting network model. The WLSTM design functions as the network's coordinator, obtaining data from the recommended model, upgrading the representation, and aggregating the features. The findings showed that the proposed model achieved high accuracy rates of 99.85% for Denial of Service (DoS) attacks and 99.55% for Distributed Denial of Service (DDoS) attacks when evaluated using two datasets, NSL-KDD and CICIDS-2017, compared to different models. Additionally, the simulation's F1-scores, recall rates, and precision are all above average, indicating that it is capable of identifying many network anomalies with minimal false positives (FP).

Xin Wei, “Anomaly Study of Computer Networks Based on Weighted Dynamic Network Representation Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160630

@article{Wei2025,
title = {Anomaly Study of Computer Networks Based on Weighted Dynamic Network Representation Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160630},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160630},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Xin Wei}
}