Search Results (191)

Background/Objectives: In this work, a series of six new indazole-benzimidazole hybrids (M1–M6) were designed, synthesized, and fully characterized. The design of these compounds was based on the combination of two pharmacophoric units, indazole and benzimidazole, both known for their broad spectrum of biological activities. Methods: The molecular hybridization strategy was planned to combine these scaffolds through an effective synthetic pathway, using 6-nitroindazole, two 2-mercaptobenzimidazoles, and 1,3- or 1,5-dihaloalkanes as key precursors, affording the desired hybrids in good yields and with enhanced biological activity. Quantum chemical calculations were performed to investigate the structural, electronic, and electrostatic properties of M1–M6 molecules using Density Functional Theory (DFT) at the B3LYP/6-311++G(d,p) level. The antimicrobial activity efficacy of these compounds was assessed in vitro against four Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis, Bacillus cereus, and Lactobacillus plantarum), four Gram-negative bacteria (Salmonella enteritidis, Escherichia coli, Campylobacter coli, Campylobacter jejuni), and four fungal strains (Saccharomyces cerevisiae, Candida albicans, Candida tropicalis, and Candida glabrata) using ampicillin and tetracycline as reference standard drugs. Results: Among the series, compound M6 exhibited remarkable antimicrobial activity, with minimum inhibitory concentrations (MIC) of 1.95 µg/mL against S. cerevisiae and C. tropicalis, and 3.90 µg/mL against S. aureus, B. cereus, and S. enteritidis, while the standards Ampicillin (AmB) (MIC ≥ 15.62 µg/mL) and Tetracycline (TET) (MIC ≥ 7.81 µg/mL) exhibited higher MIC values. To gain molecular insights into the compounds, an in silico docking study was performed to determine the interactions of M1–M6 ligands against the antimicrobial target beta-ketoacyl-acyl carrier protein (ACP) synthase III complexed with malonyl-COA (PDB ID: 1HNJ). Molecular modeling data provided valuable information on the structure-activity relationship (SAR) and the binding modes influencing the candidate ligand-protein recognition. Amino acid residues, such as Arg249, located in the solvent-exposed region, were essential for hydrogen bonding with the nitro group of the 6-nitroindazole moiety. Furthermore, polar side chains such as Asn274, Asn247, and His244 participated in interactions mediated by hydrogen bonding with the 5-nitrobenzimidazole moiety of these compound series. Conclusions: The hybridization of indazole and benzimidazole scaffolds produced compounds with promising antimicrobial activity, particularly M6, which demonstrated superior potency compared to standard antibiotics. Computational and docking analyses provided insights into the structure–activity relationships, highlighting these hybrids as potential candidates for antimicrobial drug development. Full article

Person Re-identification (ReID), a critical technology in intelligent surveillance, aims to accurately match specific individuals across non-overlapping camera networks. However, factors in real-world scenarios such as variations in illumination, viewpoint, and pose continuously challenge the matching accuracy of existing models. Although Transformer-based models like TransReID have demonstrated a strong capability for capturing global context in feature extraction, the features they produce still have room for optimization at the metric matching stage. To address this issue, this study proposes a hybrid framework that combines advanced feature extraction with post-processing optimization. We employed a fixed, pre-trained TransReID model as the feature extractor and introduced a camera-aware Jaccard distance re-ranking algorithm (CA-Jaccard) as a post-processing module. Without retraining the main model, this framework refines the initial distance metric matrix by analyzing the local neighborhood topology among feature vectors and incorporating camera information. Experiments were conducted on two major public datasets, Market-1501 and MSMT17. The results show that our framework significantly improved the overall ranking quality of the model, increasing the mean Average Precision (mAP) on Market-1501 from 88.2% to 93.58% compared to using TransReID alone, achieving a gain of nearly 4% in mAP on MSMT17. This research confirms that advanced post-processing techniques can effectively complement powerful feature extraction models, providing an efficient pathway to enhance the robustness of ReID systems in complex scenarios. Additionally, it is the first-ever to analyze how the modified distance metric improves the ReID task when used specifically with the ViT-based feature extractor TransReID. Full article

Person Re-Identification (Re-ID), a critical component of intelligent surveillance and security systems, seeks to match individuals across disjoint camera networks under complex real-world conditions. While deep learning has revolutionized Re-ID through enhanced feature representation and domain adaptation, a holistic synthesis of its advancements, unresolved challenges, and ethical implications remains imperative. This survey offers a structured and critical examination of Re-ID in the deep learning era, organized into three pillars: technological innovations, persistent barriers, and future frontiers. We systematically analyze breakthroughs in deep architectures (e.g., transformer-based models, hybrid global-local networks), optimization paradigms (contrastive, adversarial, and self-supervised learning), and robustness strategies for occlusion, pose variation, and cross-domain generalization. Critically, we identify underexplored limitations such as annotation bias, scalability-accuracy trade-offs, and privacy-utility conflicts in real-world deployment. Beyond technical analysis, we propose emerging directions, including causal reasoning for interpretable Re-ID, federated learning for decentralized data governance, open-world lifelong adaptation frameworks, and human-AI collaboration to reduce annotation costs. By integrating technical rigor with societal responsibility, this review aims to bridge the gap between algorithmic advancements and ethical deployment, fostering transparent, sustainable, and human-centric Re-ID systems. Full article

University advising at matriculation must operate under strict information constraints, typically without any post-enrolment interaction history.We present a unified, leakage-free pipeline for predicting early dropout risk and generating cold-start programme recommendations from pre-enrolment signals alone, with an optional early-warning variant incorporating first-term academic aggregates. The approach instantiates lightweight multimodal architectures: tabular RNNs, DistilBERT encoders for compact profile sentences, and a cross-attention fusion module evaluated end-to-end on a public benchmark (UCI id 697; n = 3630 students across 17 programmes). For dropout, fusing text with numerics yields the strongest thresholded performance (Hybrid RNN–DistilBERT: f1-score ≈ 0.9161, MCC ≈ 0.7750, and simple ensembling modestly improves threshold-free discrimination (Area Under Receiver Operating Characteristic Curve (AUROC) up to ≈0.9488). A text-only branch markedly underperforms, indicating that numeric demographics and early curricular aggregates carry the dominant signal at this horizon. For programme recommendation, pre-enrolment demographics alone support actionable rankings (Demographic Multi-Layer Perceptron (MLP): Normalized Discounted Cumulative Gain @ 10 (NDCG@10) ≈ 0.5793, Top-10 ≈ 0.9380, exceeding a popularity prior by $25–27$ percentage points in NDCG@10); adding text offers marginal gains in hit rate but not in NDCG on this cohort. Methodologically, we enforce leakage guards, deterministic preprocessing, stratified splits, and comprehensive metrics, enabling reproducibility on non-proprietary data. Practically, the pipeline supports orientation-time triage (high-recall early-warning) and shortlist generation for programme selection. The results position matriculation-time advising as a joint prediction–recommendation problem solvable with carefully engineered pre-enrolment views and lightweight multimodal models, without reliance on historical interactions. Full article

The exponential growth of Internet of Things (IoT) devices introduces significant security challenges due to their resource constraints and diverse attack surfaces. To address these issues, this paper proposes the Centralized Two-Tiered Tree-Based Intrusion Detection System (C2T-IDS), a lightweight framework designed for efficient and scalable threat detection in IoT networks. The system employs a hybrid edge-centralized architecture, where the first tier, deployed on edge gateways, performs real-time binary classification to detect anomalous traffic using optimized tree-based models. The second tier, hosted on a centralized server, conducts detailed multi-class classification to diagnose specific attack types using advanced ensemble methods. Evaluated on the realistic CIC-IoT-2023 dataset, C2T-IDS achieves a Macro F1-Score of up to 0.94 in detection and 0.80 in diagnosis, outperforming direct multi-class classification by 5–15%. With inference times as low as 6 milliseconds on edge devices, the framework demonstrates a practical balance between accuracy, efficiency, and deployability, offering a robust solution for securing resource-constrained IoT environments. Full article

Cloud infrastructure supports modern services across different sectors, such as business, education, lifestyle, government and so on. With the high demand for cloud computing, the security of network communication is also an important consideration. Distributed denial-of-service (DDoS) attacks pose a significant threat. Therefore, detection and mitigation are critically important for reliable operation of cloud-based systems. Intrusion detection systems (IDS) play a vital role in detecting and preventing attacks to avoid damage to reliability. This article presents DDoS detection using a convolutional neural network (CNN) and recurrent neural network (RNN) model enhancement with a multi-head attention mechanism for cloud infrastructure protection enhances the contextual relevance and accuracy of the DDoS detection. Preprocessing techniques were applied to optimize model performance, such as information gained to identify important features, normalization, and synthetic minority oversampling technique (SMOTE) to address class imbalance issues. The results were evaluated using confusion metrics. Based on the performance indicators, our proposed method achieves an accuracy of 97.78%, precision of 98.66%, recall of 94.53%, and F1-score of 96.49%. The hybrid model with multi-head attention achieved the best results among the other deep learning models. The model parameter size was moderately lightweight at 413,057 parameters with an inference time in a cloud environment of less than 6 milliseconds, making it suitable for application to cloud infrastructure. Full article

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline “novelty detection → active labelling → incremental supervised update” and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of ≈92–94% and resistance to distribution drift are noted. Full article

Securing Industrial Control Systems (ICSs) is critical, but it is made challenging by the constant evolution of cyber threats and the scarcity of labeled attack data in these specialized environments. Standard intrusion detection systems (IDSs) often fail to adapt when transferred to new networks with limited data. To address this, this paper introduces an adaptive intrusion detection framework that combines a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with a novel transfer learning strategy. We employ a Reinforcement Learning (RL) agent to intelligently guide the fine-tuning process, which allows the IDS to dynamically adjust its parameters such as layer freezing and learning rates in real-time based on performance feedback. We evaluated our system in a realistic data-scarce scenario using only 50 labeled training samples. Our RL-Guided model achieved a final F1-score of 0.9825, significantly outperforming a standard neural fine-tuning model (0.861) and a target baseline model (0.759). Analysis of the RL agent’s behavior confirmed that it learned a balanced and effective policy for adapting the model to the target domain. We conclude that the proposed RL-guided approach creates a highly accurate and adaptive IDS that overcomes the limitations of static transfer learning methods. This dynamic fine-tuning strategy is a powerful and promising direction for building resilient cybersecurity defenses for critical infrastructure. Full article

The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging attack tactics. To address these limitations, this study employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize realistic network traffic that preserves both temporal and statistical characteristics. Using the CIC-IDS-2017 dataset, which encompasses diverse attack scenarios including brute-force, Heartbleed, botnet, DoS/DDoS, web, and infiltration attacks, two training methodologies are proposed. The first trains a single conditional WGAN-GP on the entire dataset to capture the global distribution. The second employs multiple generators tailored to individual attack types, while sharing a discriminator pretrained on the complete traffic set, thereby ensuring consistent decision boundaries across classes. The quality of the generated traffic was evaluated using a Train on Synthetic, Test on Real (TSTR) protocol with LSTM and Random Forest classifiers, along with distribution similarity measures in the embedding space. The proposed approach achieved a classification accuracy of 97.88% and a Fréchet Inception Distance (FID) score of 3.05, surpassing baseline methods by more than one percentage point. These results demonstrate that the proposed synthetic traffic generation strategy provides advantages in scalability, diversity, and privacy, thereby enriching cyber range training scenarios and supporting the development of adaptive intrusion detection systems that generalize more effectively to evolving threats. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

Software-Defined Wide Area Networks (SD-WAN) have emerged as a rapidly evolving technology designed to meet the growing demand for flexible, secure, and scalable network infrastructures. This paper provides a review of SD-WAN techniques, focusing on their principles of operation, mechanisms, and evolution, with particular attention to applications in resource-constrained environments such as mobile, satellite, and radio networks. The analysis highlights key architectural elements, including security mechanisms, monitoring methods and metrics, and management protocols. A classification of both commercial (e.g., Cisco SD-WAN, Fortinet Secure SD-WAN, VMware SD-WAN, Palo Alto Prisma SD-WAN, HPE Aruba EdgeConnect) and research-based solutions is presented. The overview covers overlay protocols such as Overlay Management Protocol (OMP), Dynamic Multipath Optimization (DMPO), App-ID, OpenFlow, and NETCONF, as well as tunneling mechanisms such as IPsec and WireGuard. The discussion further covers control plane architectures (centralized, distributed, and hybrid) and network monitoring methods, including latency, jitter, and packet loss measurement. The growing importance of Artificial Intelligence (AI) in optimizing path selection and improving threat detection in SD-WAN environments, especially in resource-constrained networks, is emphasized. Analysis of solutions indicates that SD-WAN improves performance, reduces latency, and lowers operating costs compared to traditional WAN architectures. The paper concludes with guidelines and recommendations for using SD-WAN in resource-constrained environments. Full article

The development of tropical maize populations with high heterosis potential is essential for sustaining genetic progress in hybrid breeding programs, yet accurate selection remains challenging due to genotype–phenotype interactions and inbreeding depression. This study evaluated the efficiency of five selection strategies in recurrent selection programs using F₂ populations derived from commercial maize hybrids: Smith–Hazel Index (SHI), Base Index (BIA), Mulamba–Mock Index (MMI), REML/BLUP for grain yield (BLUP_GY), and REML/BLUP for inbreeding depression (BLUP_ID). Consistency among methods was assessed with a heatmap, and predicted genetic gains were compared with realized field performance. Predicted gains were highest with MMI and BIA for grain yield and ear weight, although realized results revealed discrepancies, particularly for BLUP-based approaches. Notably, BLUP_GY, which had the lowest predicted yield (4025 kg ha⁻¹), achieved a realized yield of 5620 kg ha⁻¹, surpassing BIA and SHI. This indicates that additive potential was underestimated in predictions, likely due to dominance and environmental effects in early F₂ cycles. Overall, BLUP-based methods proved effective in identifying progenies with higher additive value, and their integration with phenotypic indices is recommended to combine short-term realized gains with sustained genetic improvement. Full article

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

The increasing adoption of electric vehicles (EVs) within intelligent transportation systems (ITSs) has elevated the importance of cybersecurity, especially with the rise in Vehicle-to-Everything (V2X) communications. Traditional intrusion detection systems (IDSs) struggle to address the evolving and complex nature of cyberattacks in such dynamic environments. To address these challenges, this study introduces a novel deep learning-based IDS designed specifically for EV communication networks. We present a hybrid model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) layers, and adaptive learning strategies. The model was trained and validated using the VeReMi dataset, which simulates a wide range of attack scenarios in V2X networks. Additionally, an ablation study was conducted to isolate the contribution of each of its modules. The model demonstrated strong performance with 98.73% accuracy, 97.88% precision, 98.91% sensitivity, and 98.55% specificity, as well as an F1-score of 98.39%, an MCC of 0.964, a false-positive rate of 1.45%, and a false-negative rate of 1.09%, with a detection latency of 28 ms and an AUC-ROC of 0.994. Specifically, this work fills a clear gap in the existing V2X intrusion detection literature—namely, the lack of scalable, adaptive, and low-latency IDS solutions for hardware-constrained EV platforms—by proposing a hybrid CNN–LSTM architecture coupled with an elastic weight consolidation (EWC)-based adaptive learning module that enables online updates without full retraining. The proposed model provides a real-time, adaptive, and high-precision IDS for EV networks, supporting safer and more resilient ITS infrastructures. Full article