Network, Volume 5, Issue 1 (March 2025) – 8 articles

The security of a network is based on the foundation of confidentiality, integrity, and availability, often referred to as the CIA triad. The privacy of data over a network, maintained by confidentiality, has long been one of the major issues in network settings. With the decoupling of the data plane and control plane in the software-defined networking (SDN) environment, this challenge is significantly amplified. This paper aims to address the challenges of confidentiality in SDN by introducing a genetic algorithm-based hybrid encryption network policy to secure messages across the network. The proposed approach achieved an average entropy of 0.989, revealing a significant improvement in the strength of the encryption with the hybrid mechanism. However, the method exhibited processing overhead, significantly increasing the transmission time for encrypted messages compared to unencrypted transmission. Compared to standalone AES, DES, and RSA, this approach shows better encryption randomness, but a trade-off between security and network performance is evident in the absence of load-balancing techniques. Full article

The paper intends to contribute to readers’ comprehension of the Greek telecommunications market, focusing on the strategic decisions associated with network tower-sharing analysis in Greece. The Greek telecommunications industry is described for the first time following the Structure–Conduct–Performance (SCP) paradigm of Industrial Organisation (IO), as a methodological tool of analysis. In that respect, an SCP model in its extended form is constructed, aiming to examine how structure, conduct, and performance interrelate to each other. More precisely, the SCP model explains how strategic decisions regarding tower infrastructure sharing between 2013–2022 were developed, as a result of a series of interactions and feedback effects, amongst market structure, operators’ conducts, and performances, resulting in strengthening competition and reshaping market structure with the entrance of a new player in the Greek mobile market, an independent TowerCo (Athens, Greece) in Greece. International tendencies and competition issues influencing domestic growth potentialities and alternative operators’ concentration will be addressed, too. The paper concludes with presenting a basically qualitative, explanatory interpretive analysis of the perspectives of network tower-sharing analysis in the Greek telecommunication industry, including policy recommendations for the near future and thoughts on future research, as well. Full article

Machine learning (ML) promises advances in automation and threat detection for the future generations of communication networks. However, new threats are introduced, as adversaries target ML systems with malicious data. Adversarial attacks on tree-based ML models involve crafting input perturbations that exploit non-smooth decision boundaries, causing misclassifications. These so-called evasion attacks are imperceptible, as they do not significantly alter the input data distribution and have been shown to degrade the performance of tree-based models across various tasks. Adversarial training and genetic algorithms have been proposed as potential defenses against these attacks. In this paper, we explore the robustness of tree-based models for network intrusion detection systems. This study evaluates an optimization approach inspired by genetic algorithms to generate adversarial samples and studies the impact of adversarial training on the accuracy of attack detection. This paper exposed random forest and extreme gradient boosting classifiers to various adversarial samples generated from communication network-related CIC-IDS2019 and 5G-NIDD datasets. The results indicate that the improvements of robustness to adversarial attacks come with a cost to the accuracy of the network intrusion detection models. These costs can be optimized with intelligent, use case-specific feature engineering. Full article

The Advanced Message Queuing Protocol (AMQP) is a widely used communication standard in IoT systems due to its robust and reliable message delivery capabilities. However, its increasing adoption has made it a target for various cyber threats, including Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and brute force attacks. This study presents a comprehensive analysis of AMQP-specific vulnerabilities and introduces a statistical model for the detection and classification of malicious activities in IoT networks. Leveraging a custom-designed IoT testbed, realistic attack scenarios were simulated, and a dataset encompassing normal, malicious, and mixed traffic was generated. Unique attack signatures were identified and validated through repeated experiments, forming the foundation of a signature-based detection mechanism tailored for AMQP networks. The proposed model demonstrated high accuracy in detecting and classifying attack-specific traffic while maintaining a low false positive rate for benign traffic. Notable results include effective detection of RST packets in DDoS scenarios, precise classification of MitM attack patterns, and identification of brute force attempts on AMQP systems. This research highlights the efficacy of signature-based approaches in enhancing IoT security and offers a benchmark for future machine learning-driven detection systems. By addressing AMQP-specific challenges, the study contributes to the development of resilient and secure IoT ecosystems. Full article

The issue of organizing efficient interaction between vehicle-to-everything (V2X) system elements has become increasingly critical in recent years. Utilizing V2X technology enables achieving the necessary balance of safety, reducing system load, and ensuring a high degree of vehicle automation. This study aims to develop a simulation system for V2X applications in various element placement configurations and conduct a numerical analysis of several V2X system interaction schemes. The research analyzes various methods, including clustering, edge computing, and fog computing, aimed at minimizing system losses. The results demonstrate that each proposed model can be effectively implemented on mobile nodes. The results also provide insights into the average expected request processing times, thereby enhancing the organization of the V2X system. The authors propose a model that enables the distribution of system parameters and resources for diverse computational tasks, which is essential for the successful implementation and utilization of V2X technology. Full article

Sequential decision-making in dynamic and interconnected environments is a cornerstone of numerous applications, ranging from communication networks and finance to distributed blockchain systems and IoT frameworks. The multi-armed bandit (MAB) problem is a fundamental model in this domain that traditionally assumes independent and identically distributed (iid) rewards, which limits its effectiveness in capturing the inherent dependencies and state dynamics present in some real-world scenarios. In this paper, we lay a theoretical framework for a modified MAB model in which each arm’s reward is generated by a hidden Markov process. In our model, each arm undergoes Markov state transitions independent of play in a way that results in varying reward distributions and heightened uncertainty in reward observations. The number of states for each arm can be up to three states. A key challenge arises from the fact that the underlying states governing each arm’s rewards remain hidden at the time of selection. To address this, we adapt traditional index-based policies and develop a modified index approach tailored to accommodate Markovian transitions and enhance selection efficiency for our model. Our proposed proposed Markovian Upper Confidence Bound (MC-UCB) policy achieves logarithmic regret. Comparative analysis with the classical UCB algorithm reveals that MC-UCB consistently achieves approximately a 15% reduction in cumulative regret. This work provides significant theoretical insights and lays a robust foundation for future research aimed at optimizing decision-making processes in complex, networked systems with hidden state dependencies. Full article

In a global context where reducing the carbon footprint has become an urgent necessity, this article presents a hybrid CNN-LSTM prediction model to estimate CO₂ emission rates of Paris road traffic using spatio-temporal data. Our hybrid prediction model relies on a real-time road traffic database that we built by fusing several APIs and datasets. In particular, we trained two specialized models: a CNN to extract spatial patterns and an LSTM to capture temporal dynamics. By merging their outputs, we leverage both spatial and temporal dependencies, ensuring more accurate predictions. Thus, this article aims to compare various strategies and configurations, allowing us to identify the optimal architecture and parameters for our CNN-LSTM model. Moreover, to refine the predictive learning evolution of our hybrid model, we used optimization techniques like gradient descent to monitor the learning progress. The results show that our hybrid CNN-LSTM model achieved an R² value of 0.91 and an RMSE of 0.086, outperforming conventional models regarding CO₂ emission rate prediction accuracy. These results validate the efficiency and relevance of using hybrid CNN-LSTM models for the spatio-temporal modelling of CO₂ emissions in the context of road traffic. Full article

Artificial intelligence (AI) transforms communication networks by enabling more efficient data management, enhanced security, and optimized performance across diverse environments, from dense urban 5G/6G networks to expansive IoT and cloud-based systems. Motivated by the increasing need for reliable, high-speed, and secure connectivity, this study explores key AI applications, including traffic prediction, load balancing, intrusion detection, and self-organizing network capabilities. Through detailed case studies, I illustrate AI’s effectiveness in managing bandwidth in high-density urban networks, securing IoT devices and edge networks, and enhancing security in cloud-based communications through real-time intrusion and anomaly detection. The findings demonstrate AI’s substantial impact on creating adaptive, secure, and efficient communication networks, addressing current and future challenges. Key directions for future work include advancing AI-driven network resilience, refining predictive models, and exploring ethical considerations for AI deployment in network management. Full article