Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Wi-Fi router security is a real concern for universities and research centers that rely on strong, dependable networks for everything they do. In this study, we took a close look at four popularWi-Fi router firmwares using open-source tools such as Binwalk, CVE-Bin- Tool, and Semgrep. We carefully examined the file systems, cross-referenced them with the National Vulnerability Database (NVD), and searched for outdated software like BusyBox and OpenSSL. What we found was clear: proprietary firmwares had more Critical and High vulnerabilities, while OpenWrt stood out for being more secure, easier to update, and openly maintained by its community. Our reproducible process automates how we gather evidence and map vulnerabilities, making firmware auditing more practical and trustworthy. These results make a strong case for using open-source firmware as a safer, more manageable choice for institutional networks....
The increasing digital integration of Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCSs), has improved operational efficiency while simultaneously increasing exposure to cyber threats. Traditional signature-based intrusion detection systems are limited in detecting novel and stealthy attacks in dynamic industrial environments. This study presents a deep learning–based anomaly detection framework for ICS cybersecurity using multivariate time-series data from sensors, actuators, and network traffic. Three architectures, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer models, are evaluated using the HAI Security Dataset. Experimental results show that the Transformer model achieves the highest accuracy (92%), followed by CNN (91%) and LSTM (90%), with all models attaining an F1-score of 91%. The Transformer demonstrates superior generalization by effectively modelling complex temporal dependencies. Key challenges, including data imbalance, overfitting, and limited interpretability, are discussed alongside potential mitigation strategies such as hybrid modelling, federated learning, and digital twin integration. The findings demonstrate the effectiveness of deep learning for scalable, real-time cybersecurity threat detection in industrial control environments. To address challenges such as class imbalance and overfitting, the study discusses mitigation strategies including regularization, early stopping, cost-sensitive learning, and future integration of data balancing and federated learning techniques for improved robustness and generalization....
We developed an AI-assisted zero-trust control system at low capital expenditure to retrofit brownfield Ethernet environments without disruptive hardware upgrades or costly software-defined networking migration. Legacy network infrastructures in small and medium-sized enterprises (SMEs) lack the flexibility and programmability required by modern zero-trust architectures, creating a persistent security gap between static Layer- 1 deployments and dynamic cyber threats. The developed system addresses this gap through a modular architecture that integrates genetic-algorithm-based virtual local area network (VLAN) optimization, large language model-guided firewall rule synthesis, threatintelligence- driven policy automation, and telemetry-triggered adaptive isolation. Network assets are enumerated and evaluated through a risk-aware clustering model to enable microsegmentation that aligns with the principle of least privilege. Optimized segmentation outputs are translated into pfSense firewall policies through structured prompt engineering and dual-stage validation, ensuring syntactic correctness and semantic consistency. A retrieval-augmented generation pipeline connects live telemetry with historical vulnerability intelligence, enabling rapid policy adjustments and automated containment responses. The system operates as an overlay on existing managed switches, orchestrating configuration changes through standards-compliant interfaces such as simple network management protocol and network configuration protocol. Experimental evaluation in a representative SME testbed demonstrates substantial improvements in segmentation granularity, refining seven flat subnets into thirty-four purpose-specific VLANs. Compliance scores improved significantly, with the International Organization for Standardization/International Electrotechnical Commission 27001 rising from 62.3 to 94.7% and the National Institute of Standards and Technology Cybersecurity Framework alignment increasing from 58.9 to 91.2%. All 851 automatically generated firewall rules passed dual-agent validation, ensuring reliable enforcement and enhanced auditability. The results indicate that the system developed provides an operationally feasible pathway for legacy networks to achieve zero-trust segmentation with minimal cost and disruption. Future extensions will explore adaptive learning mechanisms and hybrid cloud support to further enhance scalability and contextual responsiveness....
This paper presents a method for detecting anomalies directly from binary data using Deep Learning techniques. We aimed to support information security in network environments. The approach employs Deep Learning models based on neural networks to analyze raw binary data without preprocessing or transformation, allowing the detection of low-level deviations in the data patterns. The model’s performance with raw binary data was evaluated using three publicly available datasets: CIC-IDS2017, CIC-IDS2018, and IoT-IDS. Evaluation results indicated that the method can detect various types of attacks, with consistent performance on all tested datasets: the F1-scores were .9674 (CIC-IDS2017), .9911 (CICIDS2018), and .9957 (IoT-IDS). The paper outlines the method’s design and includes the model architecture, evaluation procedures, and observed performance metrics for anomaly detection tasks. The detection results are also presented and analyzed in detail....
AI-driven network security relies increasingly on Large Language Models (LLMs) to detect sophisticated threats; however, their deployment on resource-constrained edge devices is severely hindered by immense parameter scales. While unstructured pruning offers a theoretical reduction in model size, commodity Graphics Processing Unit (GPU) architectures fail to efficiently leverage element-wise sparsity due to the mismatch between fine-grained pruning patterns and the coarse-grained parallelism of Tensor Cores, leading to latency bottlenecks that compromise real-time analysis of high-volume security telemetry. To bridge this gap, we propose SPARTA (Sparse Parallel Architecture for Real-Time Threat Analysis), an algorithm–architecture co-design framework. Specifically, we integrate a hardware-based address remapping interface to enable flexible row-offset access. This mechanism facilitates a novel graph-based column vector merging strategy that aligns sparse data with Tensor Core parallelism, complemented by a pipelined execution scheme to mask decoding latencies. Evaluations on Llama2-7B and Llama2-13B benchmarks demonstrate that SPARTA achieves an average speedup of 2.35× compared to Flash-LLM, with peak speedups reaching 5.05×. These findings indicate that hardware-aware microarchitectural adaptations can effectively mitigate the penalties of unstructured sparsity, providing a viable pathway for efficient deployment in resource-constrained edge security....
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