Search Results (1,152)

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Traditional machine learning approaches for 5G network management relieve data from operational networks, which are often noisy and confounded, making it difficult to identify key influencing factors. This research addresses the critical gap between correlation-based prediction and interpretable, data-driven explanation. To this end, a software-defined standalone 5G architecture was developed using srsRAN and Open5GS to support multi-user scenarios. A multi-user environment was then simulated with GNU Radio, from which the initial dataset was collected. This dataset was further generated using a Conditional Tabular Generative Adversarial Network (CTGAN) to improve diversity and balance. Several machine learning models, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were trained and evaluated for predicting network performance. Among them, XGBoost achieved the best results, with an R² score of 0.998. To interpret the model, we conducted a SHAP (SHapley Additive exPlanations) analysis, which revealed that the download-to-upload bitrate ratio (dl_ul_ratio) and upload bitrate (brate_ul) were the most influential features. By leveraging a controlled experimental 5G environment, this study demonstrates how machine learning can move beyond predictive accuracy to uncover the fundamental principles governing 5G system performance, providing a robust foundation for future network optimization. Full article

Fuzzing has established itself as a cornerstone technique for uncovering defects in both stand-alone executables and software libraries. In the domain of library testing, prior research has predominantly concentrated on the automated generation of fuzz drivers-code harnesses that invoke individual Application Programming Interfaces (APIs) under test. While these approaches successfully orchestrate API calls in the correct sequence, they often neglect a critical factor: the semantic relevance and structural validity of the input data supplied to each API parameter. Unlike monolithic programs, where inputs are typically drawn from well-defined file or network formats, API parameters may span a broad spectrum of primitive and composite data types-ranging from integers and floating-point values to strings, containers, and user-defined aggregates—each of which demands tailored mutation strategies to exercise deep code paths and trigger latent faults. To address this gap, we introduce PointFuzz, a novel fuzzing framework that integrates type-aware input generation into existing harness generation pipelines. PointFuzz begins by statically analyzing the API’s function signatures and associated type definitions to accurately identify the data type of every parameter. It then applies a suite of specialized mutation operators. This data-type-guided mutation maximizes the likelihood of traversing previously untested execution branches. Moreover, PointFuzz incorporates an innovative feedback mechanism that dynamically adjusts mutation priorities based on real-time coverage gains. By assigning quantitative scores to parameter-specific operators, our system continuously learns which strategies yield the most valuable inputs, and reallocates computational effort accordingly. Empirical evaluation across multiple widely used C/C++ libraries demonstrates that PointFuzz achieves superior API coverage compared to generic, agnostic-type fuzzers. These results validate the efficacy of combining type-aware mutation with adaptive feedback to advance the state of library API fuzzing. Full article

Protecting communications within vehicular networks is of paramount importance, particularly when data are transmitted using wireless ad-hoc technologies such as Dedicated Short-Range Communications (DSRC). Vulnerabilities in Vehicle-to-Everything (V2X) communications, especially along highways, pose significant risks, such as unauthorized interception or alteration of vehicle data. This study proposes a Software-Defined Radio (SDR)-based tool designed to assess the protection level of V2X communication systems against cyber attacks. The proposed tool can emulate both reception and transmission of IEEE 802.11p packets while testing DSRC implementation and robustness. The results of this investigation offer valuable contributions toward shaping cybersecurity strategies and frameworks designed to protect the integrity of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Full article

Optimizing printing parameters is crucial for enhancing the efficiency, surface quality, and dimensional accuracy of Fused Deposition Modeling (FDM) processes. A review of numerous publications reveals that most scholars analyze factors such as nozzle diameter and printing speed, while few investigate the impact of layer thickness, infill density, and shell layer count on print quality. Therefore, this study employed 3D slicing software to process the three-dimensional model and design printing process parameters. It systematically investigated the effects of layer thickness, infill density, and number of shells on printing time and geometric accuracy, quantifying the evaluation through volumetric error. Using an ABS connecting rod model, optimal parameters were determined within the defined range through orthogonal experimental design and signal-to-noise ratio (S/N) analysis. Subsequently, a backpropagation (BP) neural network was constructed to establish a predictive model for process optimization. Results indicate that parameter selection significantly impacts print duration and surface quality. Validation confirmed that the combination of 0.1 mm layer thickness, 40% infill density, and 5-layer shell configuration achieves the highest dimensional accuracy (minimum volumetric error and S/N value). Under this configuration, the volumetric error rate was 3.062%, with an S/N value of −9.719. Compared to other parameter combinations, this setup significantly reduced volumetric error, enhanced surface texture, and improved overall print precision. Statistical analysis indicates that the BP neural network model achieves a Mean Absolute Percentage Error (MAPE) of no more than 5.41% for volume error rate prediction and a MAPE of 5.58% for signal-to-noise ratio prediction. This validates the model’s high-precision predictive capability, with the established prediction model providing effective data support for FDM parameter optimization. Full article

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

This research presents a methodology for the detection of distributed denial-of-service (DDoS) attacks in software-defined networks (SDNs). An SDN was configured using the Mininet simulator, the Open Daylight controller, and a web server, which acted as the target to execute a DDoS attack on the HTTP protocol. The attack tools GoldenEye, Slowloris, HULK, Slowhttptest, and XerXes were used, and two datasets were built using the CICFlowMeter and NTLFlowLyzer flow and feature generation tools, with 424,922 and 731,589 flows, respectively, as well as two independent test datasets. These tools were used to compare their functionalities and efficiency in generating flows and features. Finally, the XGBoost and Random Forest models were evaluated with each dataset, with the objective of identifying the model that provides the best classification result in the detection of malicious traffic. For the XGBoost model, the accuracy results were 99.48% and 97.61%, while for the Random Forest model, better results were obtained with 99.97% and 99.99% using the CIC-Dataset and NTL-Dataset, respectively, in both cases. This allows determining that the Random Forest model outperformed XGBoost in classification, as it achieved the lowest false negative rate of 0.00001 using the NTL-Dataset. Full article

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation. Full article

As smart cities evolve, the demand for real-time, secure, and adaptive network monitoring, continues to grow. Software-Defined Networking (SDN) offers a centralized approach to managing network flows; However, anomaly detection within SDN environments remains a significant challenge, particularly at the intelligent edge. This paper presents a conceptual Kafka-enabled ML framework for scalable, real-time analytics in SDN environments, supported by offline evaluation and a prototype streaming demonstration. A range of supervised ML models covering traditional methods and ensemble approaches (Random Forest, Linear Regression & XGBoost) were trained and validated using the InSDN intrusion detection dataset. These models were tested against multiple cyber threats, including botnets, dos, ddos, network reconnaissance, brute force, and web attacks, achieving up to 99% accuracy for ensemble classifiers under offline conditions. A Dockerized prototype demonstrates Kafka’s role in offline data ingestion, processing, and visualization through PostgreSQL and Grafana. While full ML pipeline integration into Kafka remains part of future work, the proposed architecture establishes a foundation for secure and intelligent Software-Defined Vehicular Networking (SDVN) infrastructure in smart cities. Full article

Software-defined networks are a modern approach to computer networks. With this concept, network devices can be monitored and configured centrally. While the lower layers of a software-defined network—devices and controllers—are relatively well known and standardized, the upper layers consist of APIs and software applications and are not standard. This article aims to propose one possible way to interact with a software-defined network and to build applications for monitoring and configuring such networks. Full article

Software-defined networking (SDN) is a transformative approach for managing modern network architectures, particularly in Internet-of-Things (IoT) applications. However, ensuring the optimal SDN performance and security often needs a robust sensitivity analysis (SA). To complement existing SA methods, this study proposes a new SA framework that integrates design of experiments (DOE) and machine-learning (ML) techniques. Although existing SA methods have been shown to be effective and scalable, most of these methods have yet to hybridize anomaly detection and classification (ADC) and data augmentation into a single, unified framework. To fill this gap, a targeted application of well-established existing techniques is proposed. This is achieved by hybridizing these existing techniques to undertake a more robust SA of a typified SDN-reliant IoT network. The proposed hybrid framework combines Latin hypercube sampling (LHS)-based DOE and generative adversarial network (GAN)-driven data augmentation to improve SA and support ADC in SDN-reliant IoT networks. Hence, it is called DOE-GAN-SA. In DOE-GAN-SA, LHS is used to ensure uniform parameter sampling, while GAN is used to generate synthetic data to augment data derived from typified real-world SDN-reliant IoT network scenarios. DOE-GAN-SA also employs a classification and regression tree (CART) to validate the GAN-generated synthetic dataset. Through the proposed framework, ADC is implemented, and an artificial neural network (ANN)-driven SA on an SDN-reliant IoT network is carried out. The performance of the SDN-reliant IoT network is analyzed under two conditions: namely, a normal operating scenario and a distributed-denial-of-service (DDoS) flooding attack scenario, using throughput, jitter, and response time as performance metrics. To statistically validate the experimental findings, hypothesis tests are conducted to confirm the significance of all the inferences. The results demonstrate that integrating LHS and GAN significantly enhances SA, enabling the identification of critical SDN parameters affecting the modeled SDN-reliant IoT network performance. Additionally, ADC is also better supported, achieving higher DDoS flooding attack detection accuracy through the incorporation of synthetic network observations that emulate real-time traffic. Overall, this work highlights the potential of hybridizing LHS-based DOE, GAN-driven data augmentation, and ANN-assisted SA for robust network behavioral analysis and characterization in a new hybrid framework. Full article

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming. Full article

Traditional Software-Defined Wide Area Network (SD-WAN) solutions lack adaptive load-balancing mechanisms, leading to inefficient traffic distribution, increased latency, and performance degradation. This paper presents an Application-Aware Dynamic Load Balancing (AADLB) framework designed for open-source SD-WAN environments. The proposed solution enables dynamic traffic routing based on real-time network performance indicators, including CPU utilization, memory usage, connection delay, and packet loss, while considering application-specific requirements. Unlike conventional load-balancing methods, such as Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Priority Queuing (PQ), and Deficit Round Robin (DRR), AADLB continuously updates traffic weights based on application requirements and network conditions, ensuring optimal resource allocation and improved Quality of Service (QoS). The AADLB framework leverages a heuristic-based dynamic weight assignment algorithm to redistribute traffic in a multi-cloud environment, mitigating congestion and enhancing system responsiveness. Experimental results demonstrate that compared to these traditional algorithms, the proposed AADLB framework improved CPU utilization by an average of 8.40%, enhanced CPU stability by 76.66%, increased RAM utilization stability by 6.97%, slightly reduced average latency by 2.58%, and significantly enhanced latency consistency by 16.74%. These improvements enhance SD-WAN scalability, optimize bandwidth usage, and reduce operational costs. Our findings highlight the potential of application-aware dynamic load balancing in SD-WAN, offering a cost-effective and scalable alternative to proprietary solutions. Full article