Search Results (2,285)

The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks. Full article

Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches. Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations. Methodology: In order to ensure transparency and rigor, the authors followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion and exclusion criteria, a final set of 25 articles was selected. Outcomes: The findings revealed significant advancements in AI-powered DS diagnostics across diverse data modalities. The modalities, including facial images, ultrasound scans, and genetic data, demonstrated strong potential for early DS diagnosis. Despite these advancements, this review outlined the limitations of AI approaches. Small and imbalanced datasets reduce the generalizability of the AI models. The authors present actionable strategies to enhance the clinical adoptions of these models. Full article

The incorporation of Internet of Things (IoT) edge nodes into assistive technologies greatly improves the daily lives of individuals with disabilities by facilitating real-time data processing and seamless connectivity. However, the increasing adoption of IoT edge devices intended for individuals with disabilities presents significant security challenges, particularly concerning the safeguarding of sensitive data and the heightened risk of cyber vulnerabilities. To effectively mitigate these risks, advanced cryptographic protocols, including those based on elliptic curve cryptography, have been proposed to establish robust security measures. While these protocols are effective in reducing the risk of data exposure, they often demand considerable computational resources, which poses challenges for cost-effective IoT devices. Therefore, it is essential to prioritize the effective execution of cryptographic algorithms, as they rely on finite field operations such as multiplication, inversion, and division. Among these computations, field multiplication is particularly critical, serving as the backbone for the other operations. This study intends to create an innovative hybrid systolic array design for the Dickson basis multiplier, which integrates both serial and parallel inputs to enhance overall performance. The proposed design is anticipated to significantly reduce space and power consumption, thereby enabling the secure execution of complex cryptographic algorithms on resource-limited IoT devices designed for disabled people. By addressing these pressing security issues, the study aspires to fully leverage IoT technologies to enhance the living standards of individuals with disabilities, while ensuring that their privacy and security are meticulously maintained. Full article

As renewable energy sources (RES) continue to expand and the use of power inverters has surged, inverters have become crucial for converting direct current (DC) from RES into alternating current (AC) for the grid, and their security is vital for maintaining stable grid operations. This paper investigates the security vulnerabilities of photovoltaic (PV) inverters, specifically focusing on their internal sensors, which are critical for reliable power conversion. It is found that both current and voltage sensors are susceptible to intentional electromagnetic interference (IEMI) at frequencies of 1 GHz or higher, even with electromagnetic compatibility (EMC) protections in place. These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. These effects were demonstrated on six commercial single-phase and three-phase PV inverters, as well as in a real-world microgrid, by emitting IEMI signals from 100 to 150 cm away with up to 20 W of power. This study highlights the growing security risks of power electronics in RES, which represent an emerging target for cyber-physical attacks in future RES-dominated grids. Finally, to cope with such threats, three detection methods that are adaptable to diverse threat scenarios are proposed and their advantages and disadvantages are discussed. Full article

The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness. Full article

The integration of Internet Protocol version 6 over Low-Power Wireless Personal Area Networks (6LoWPANs) provided IP technologies within wireless sensor networks that dramatically increased the Internet of Things (IoT). Therefore, to facilitate efficient mobility management for resource-constrained IP-based sensor nodes, the Proxy Mobile IPv6 (PMIPv6) standard has been introduced to reduce communication overhead. However, the standard has addressed security and mobility authentication challenges in 6LoWPANs, although recent solutions have yet to focus much on facilitating secure group handovers. Considering these issues, a Secure and Lightweight Group Mobility Authentication Scheme (SL_GAS) is proposed for 6LoWPAN’s highly constrained sensor nodes. SL_GAS innovatively utilizes one-time alias identities, temporary IDs, tickets, and an aggregated MAC with tags to ensure mutual authentication while maintaining sensor anonymity, providing a balanced security and privacy approach. SL_GAS’s robustness against a variety of security threats is validated through formal automated verification using the Scyther tool alongside SVO logic, while an informal analysis demonstrates its resilience to known attacks. Comparative analysis with existing schemes highlights SL_GAS’s advantages in reducing signal cost, transmission delay, communication, and computation overhead. SL_GAS stands out for its combination of security, privacy, and efficiency, making it a promising approach for enhancing IoT connectivity in resource-constrained settings. Full article

In modern smart grids, accurate and synchronized time signals are essential for effective monitoring, protection, and control. Various time synchronization methods exist, each tailored to specific application needs. Widely adopted solutions, such as GPS, however, are vulnerable to challenges such as signal loss and cyber-attacks, underscoring the need for reliable backup or supplementary solutions. This paper examines the timing requirements across different power grid applications and provides a comprehensive review of available time synchronization mechanisms. Through a comparative analysis of timing methods based on accuracy, flexibility, reliability, and security, this study offers insights to guide the selection of optimal solutions for seamless grid integration. Full article

As robotic systems become more integrated into our daily lives, there is growing concern about cybersecurity. Robots used in areas such as autonomous driving, surveillance, surgery, home assistance, and industrial automation can be vulnerable to cyber-attacks, which could have serious real-world consequences. Modern robotic systems face a unique set of threats due to their evolving characteristics. This paper outlines the SESAME project’s methodology for the automated security analysis of multi-robot systems (MRS) and the production of Executable Digital Dependability Identities (EDDIs). Addressing security challenges in MRS involves overcoming complex factors such as increased connectivity, human–robot interactions, and a lack of risk awareness. The proposed methodology encompasses a detailed process, starting from system description and vulnerability identification and moving to the generation of attack trees and security EDDIs. The SESAME security methodology leverages structured repositories like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) to identify potential vulnerabilities and associated attacks. The introduction of Template Attack Trees facilitates modeling potential attacks, helping security experts develop effective mitigation strategies. This approach not only identifies, but also connects, specific vulnerabilities to possible exploits, thereby generating comprehensive security assessments. By merging safety and security assessments, this methodology ensures the overall dependability of MRS, providing a robust framework to mitigate cyber–physical threats. Full article

The implementation of universal designated verifier signatures proofs (UDVSPs) enhances data privacy and security in various digital communication systems. However, practical applications of UDVSP face challenges such as high computational overhead, onerous certificate management, and complex public key initialization. These issues hinder UDVSP adoption in daily life. To address these limitations, existing solutions attempt to eliminate bilinear pairing operations, but their proposal still involves cumbersome certificate management and inherent interactive operations that can sometimes significantly degrade system efficiency. In this paper, we first utilize the identity-based (ID-based) SM2 digital signature scheme to construct an ID-based UDVSP system which sidesteps the cumbersome certificate management issue. To further remove the interactive requirement, we also employ the OR proof and Fiat–Shamir technologies to design the other ID-based UDVSP system. Our designs not only possess the same bilinear pairing-free advantage as Lin et al.’s proposal, but also achieve the certificate-free or non-interactive goals. Security proofs and performance analysis confirm the viability and efficiency of our systems. Full article

Ensuring secure data transmission in agrotechnical monitoring systems using unmanned aerial vehicles (UAVs) is critical due to increasing cyber threats, particularly with the advent of quantum computing. This study proposes the integration of Quantum Key Distribution (QKD), based on the BB84 protocol, as a secure key management mechanism to enhance data security in UAV-based geographic information systems (GIS) for monitoring agricultural fields and forest fires. QKD is not an encryption algorithm but a secure key distribution protocol that provides information-theoretic security by leveraging the principles of quantum mechanics. Rather than replacing traditional encryption methods, QKD complements them by ensuring the secure generation and distribution of encryption keys, while AES-128 is employed for efficient data encryption. The QKD framework is optimized for real-time operations through adaptive key generation and energy-efficient hardware, alongside Lempel–Ziv–Welch (LZW) compression to improve the bandwidth efficiency. The simulation results demonstrate that the proposed system achieves secure key generation rates up to 50 Mbps with minimal computational overhead, maintaining reliability even under adverse environmental conditions. This hybrid approach significantly improves data resilience against both quantum and classical cyber-attacks, offering a comprehensive and robust solution for secure agrotechnical data transmission. Full article

The growing sophistication of cyber threats necessitates robust and interpretable intrusion detection systems (IDS) to safeguard network security. While machine learning models such as Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN), and XGBoost demonstrate high effectiveness in detecting malicious activities, their interpretability decreases as their complexity and accuracy increase, posing challenges for critical cybersecurity applications. Local Interpretable Model-agnostic Explanations (LIME) is widely used to address this limitation; however, its reliance on normal distribution for perturbations often fails to capture the non-linear and imbalanced characteristics of datasets like CIC-IDS-2018. To address these challenges, we propose a modified LIME perturbation strategy using Weibull, Gamma, Beta, and Pareto distributions to better capture the characteristics of network traffic data. Our methodology improves the stability of different ML models trained on CIC-IDS datasets, enabling more meaningful and reliable explanations of model predictions. The proposed modifications allow for an increase in explanation fidelity by up to 78% compared to the default Gaussian approach. Pareto-based perturbations provide the best results. Among all distributions tested, Pareto consistently yielded the highest explanation fidelity and stability, particularly for K-NN ( = 0.9971, S = 0.9907) and DT ( = 0.9267, S = 0.9797). This indicates that heavy-tailed distributions fit well with real-world network traffic patterns, reducing the variance in attribute importance explanations and making them more robust. Full article

In modern agriculture, timely and accurate crop yield information is crucial for optimising agricultural production management and resource allocation. This study focused on improving the prediction accuracy of pear yields. Taking Alar City, Xinjiang, China as the research area, a variety of data including leaf area index (LAI), soil moisture (SM) and remote sensing data were collected, covering four key periods of pear growth. Three advanced algorithms, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF), were used to construct the regression models of LAI and vegetation index in four key periods using Sentinel-2 satellite remote sensing data. The results showed that the RF algorithm provided the best results when inverting the LAI. The coefficients of determination (R²) were 0.73, 0.72, 0.76, and 0.77 for the four periods, respectively, and the root-mean-square errors (RMSE) were 0.21 m²/m², 0.24 m²/m², 0.18 m²/m², and 0.16 m²/m², respectively. Therefore, the RF algorithm was selected as the preferred method for LAI inversion in this study. Subsequently, the study further explored the potential of data assimilation techniques in enhancing the accuracy of pear yield simulation. LAI and SM were incorporated into the World Food Studies (WOFOST) crop growth model by four assimilation algorithms, namely, the Four-Dimensional Variational Approach (4D-Var), Particle Swarm Optimisation (PSO) algorithm, Ensemble Kalman Filter (EnKF), and Particle Filter (PF) in separate and joint assimilation, respectively. The experimental results showed that the assimilated model significantly improved the accuracy of yield prediction compared to the unassimilated model. In particular, the EnKF algorithm provided the highest accuracy in yield estimation with R² of 0.82, 0.79 and RMSE of 1056 kg/ha and 1385 kg/ha when LAI alone and SM alone were assimilated, whereas 4D-Var performed the best when LAI and SM were jointly assimilated, with R² as high as 0.88, and the RMSE reduced to 923 kg/ha. In addition, it was found that assimilating LAI outperformed assimilating SM when assimilating one variable, whereas joint assimilation of LAI and SM further enhanced the predictive performance beyond that of assimilating one variable alone. In summary, the present study demonstrated great potential to provide strong support for accurate prediction of pear yield by effectively integrating LAI and SM into crop growth models through data assimilation. Full article

Private information retrieval (PIR) enables a client to retrieve a specific element from a server’s database without disclosing the index that was queried. This work introduces three improvements to the efficient single-server PIR protocol Spiral. We found that performing a modulus switching towards expanded ciphertexts can improve the server throughput. Secondly, we apply two techniques called the composite $\mathsf{NTT}$ algorithm and approximate decomposition to Spiral to further improve it. We conduct comprehensive experiments to evaluate the concrete performance of our protocol, and the results confirm an approximately 1.7 times faster overall throughput than Spiral. Full article

While existing knowledge distillation (KD) methods typically force students to mimic teacher features without considering prediction reliability, this practice risks propagating the teacher’s erroneous supervision to the student. To address this, we propose the Logitwise Distillation Network (LDN), a novel framework that dynamically quantifies sample-wise confidence through the ranking of ground truth labels in teacher logits. Specifically, LDN introduces three key innovations: (1) weighted class means that prioritize high-confidence samples, (2) adaptive feature selection based on logit ranking, and (3) positive–negative sample adjustment (PNSA) to reverse error-prone supervision. These components are unified into a feature direction (FD) loss, which guides students to selectively emulate trustworthy teacher features. Experiments on CIFAR-100 and ImageNet demonstrate that LDN achieves state-of-the-art performance, improving accuracy by 0.3–0.5% over SOTA methods. Notably, LDN exhibits stronger compatibility with homogeneous networks (2.4% gain over baselines) and requires no additional training costs when integrated into existing KD pipelines. This work advances feature distillation by addressing error propagation, offering a plug-and-play solution for reliable knowledge transfer. Full article

The future Internet of Things (IoT) will consist of energy harvesting devices and Unmanned Aerial Vehicles (UAVs) to support applications in remote areas. However, as UAVs communicate with IoT devices using broadcast channels, information leakage emerges as a critical security threat. This paper considers the problem of maximizing the minimum secrecy rate in an energy harvesting IoT network supported by two UAVs, where one acts as a server to collect data from devices, and the other is an eavesdropper to intercept data transmission. It presents a novel Mixed-Integer Nonlinear Program (MINLP), which we then linearize into a Mixed-Integer Linear Program (MILP) problem. It also proposes a heuristic solution called Fly Nearest Location (FNL). Both solutions determine (i) the UAV server’s flight routing, flight time, and computation time, as well as (ii) the energy usage and operation mode of IoT devices. Our results show that FNL achieves on average 78.15% of MILP’s performance. Full article