Search Results (452)

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions. Full article

Based on the theoretical framework of the phase comparison method and the computational analysis of the interference model calculation analysis, this paper designs, implements, establishes, calibrates, and verifies an interference experimental platform. The proposed methodology validates the effectiveness and practical feasibility of multiantenna synthetic interference technology in real-world applications. Experimental results demonstrate that the developed system can achieve flexible and arbitrary interference angles with desired distortion characteristics through precise amplitude–phase modulation, enabling dynamic manipulation of phase plane angles. Furthermore, the system successfully synthesizes false target positions at distances exceeding five times the baseline length from the jamming platform center. Both mathematical computations and experimental validations confirm that this multiantenna synthetic interference technology represents an advanced electromagnetic countermeasure characterized by two-dimensional planar interference coverage and robust phase parameter tolerance, while also enabling artificial angular glint generation. This technology exhibits significant potential for practical engineering applications. Full article

Addressing privacy concerns is one of the key challenges facing the development of Internet of Things (IoT)-based systems (IoTSs). As IoT devices often collect and process personal and sensitive information, strict privacy policies must be defined and enforced to keep data secure and safe, ensuring security and regulatory compliance. Any data breach could compromise the security of the system, leading to various types of threats and attacks, some of which could even endanger human life. Therefore, it is crucial to design and build a comprehensive and general privacy framework for the development of IoTSs. This framework should not be limited to specific IoTS domains but should be general enough to support and cover most IoTS domains. In this paper, we present a framework that assists developers by (i) enabling them to build IoTSs that comply with privacy standards, such as the General Data Protection Regulation (GDPR), and (ii) providing a simplified and practical approach to identifying and addressing privacy concerns. In addition, the framework enables developers to implement effective countermeasures. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

Deaths occur during the culture of sea bass, and if timely harvesting is not carried out, it will lead to water pollution and the continued spread of sea bass deaths. Therefore, it is necessary to promptly detect dead fish and take countermeasures. Existing object detection algorithms, when applied to the task of detecting dead sea bass, often suffer from excessive model complexity, high computational cost, and reduced accuracy in the presence of occlusion. To overcome these limitations, this study introduces YOLOv8n-Deadfish, a lightweight and high-precision detection model. First, the homemade sea bass death recognition dataset was expanded to enhance the generalization ability of the neural network. Second, the C2f-faster–EMA (efficient multi-scale attention) convolutional module was designed to replace the C2f module in the backbone network of YOLOv8n, reducing redundant calculations and memory access, thereby more effectively extracting spatial features. Then, a weighted bidirectional feature pyramid network (BiFPN) was introduced to achieve a more thorough integration of deep and shallow features. Finally, in order to compensate for the weak generalization and slow convergence of the CIoU loss function in detection tasks, the Inner-CIoU loss function was used to accelerate bounding box regression and further improve the detection performance of the model. The experimental results show that the YOLOv8n-Deadfish model has an accuracy, recall, and mean precision of 90.0%, 90.4%, and 93.6%, respectively, which is an improvement of 2.0, 1.4, and 1.3 percentage points, respectively, over the original base network YOLOv8n. The number of model parameters and GFLOPs were reduced by 23.3% and 18.5%, respectively, and the detection speed was improved from the original 304.5 FPS to 424.6 FPS. This method can provide a technical basis for the identification of dead sea bass in the process of intelligent aquaculture. Full article

Deterministic random bit generators (DRBG) play a crucial role in device security because they generate secret information cryptographic systems, e.g., secret keys and parameters. Thus, attacks on DRBGs can result in the exposure of important secret values, which can threaten the entire cryptographic system of the target Internet of Things (IoT) equipment and smart devices. In 2020, Meyer proposed a side-channel attack (SCA) method that recovers the output random bits by analyzing the power consumption traces of the NIST standard AES CTR DRBG. In addition, most algorithmic countermeasures against SCAs also utilize random numbers; thus, such vulnerabilities are more critical than other SCAs on cryptographic modules. Meyer’s attack recovers the secret random number in four stages of the attack using only the power traces, which the CTR DRBG processes in 256 blocks. We present an approach that employs a clustering algorithm to enhance Meyer’s attack. The proposed attack increases the attack success rate and recovers more information using a clustering attack in the first step. In addition, it improves the attack accuracy in the third and fourth steps using the information obtained from the clustering process. These results lead to the possibility of attacks at higher noise levels and increase the diversity of target devices for attacking the CTR DRBG. Experiments were conducted on an Atmel XMEGA128D4 processor to evaluate the effectiveness of the proposed attack method. We also introduced artificial noise into the power traces to compare the proposed attack’s performance at different noise levels. Our results demonstrate that the first step of the proposed attack achieves a higher success rate than Meyer’s attack at all noise levels. For example, at high noise levels, the difference in the success rates is up to 50%. In steps 3 and 4, an average performance improvement of 18.5% greater than Meyer’s proposed method is obtained. The proposed attack effectively extends the target to more noisy environments than previous attacks, thereby increasing the threat of SCA on CTR DRBGs. Full article

Interrupted sampling repeater jamming (ISRJ) is characterized by its coherent processing gains and flexible modulation techniques. ISRJ generates spurious targets along the range, which presents significant challenges to the radar systems. However, existing ISRJ countermeasure methods struggle to eliminate ISRJ signals without compromising the integrity of the real target signal, especially under low-signal-to-noise-ratio (SNR) conditions, resulting in a deteriorated sidelobe and diminished detection performance. We propose a complex-valued encoder–decoder network (CVEDNet) to address these challenges based on signal decomposition. This network offers an end-to-end ISRJ suppression approach, working on complex-valued time-domain signals without the need for additional preprocessing. The encoding and decoding structure suppresses noise components and obtains more compact echo feature representations through layer-by-layer compression and reconstruction. A stacked dual-branch structure and multi-scale dilated convolutions are adopted to further separate the echo signal and ISRJ based on high-dimensional features. A multi-domain combined loss function integrates the waveform and range-pulse-compression information to ensure the amplitude and phase integrity of the reconstructed echo waveform during the training process. The effectiveness of the proposed method was validated in terms of its jamming suppression capability, echo fidelity, and detection performance indicators under low-SNR conditions compared to conventional methods. Full article

(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; however, their effectiveness has been increasingly challenged. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs (DL-SCAs). (2) Methods: A power trace dataset was generated using a simulation environment based on Quick Emulator (QEMU) and GNU Debugger (GDB), integrating dummy traces to obfuscate execution signatures. DL models, including a Recurrent Neural Network (RNN), a Bidirectional RNN (Bi-RNN), and a Multi-Layer Perceptron (MLP), were used to evaluate classification performance. (3) Results: The models trained with dummy traces achieved high classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, DL models effectively distinguished real and dummy traces, highlighting limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against DL-SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. This study underscores the urgency of evolving security paradigms to defend against artificial intelligence-powered attacks. Full article

African swine fever virus (ASFV), the causative agent of African swine fever (ASF), poses a catastrophic threat to global swine industries through its capacity for immune subversion and rapid evolution. Multigene family genes (MGFs)-encoded proteins serve as molecular hubs governing viral evolution, immune evasion, cell tropism, and disease pathogenesis. This review synthesizes structural and functional evidence demonstrating that MGFs-encoded proteins suppress both interferon signaling and inflammasome activation, while their genomic plasticity in variable terminal regions drives strain diversification and adaptation. Translationally, targeted deletion of immunomodulatory MGFs enables the rational design of live attenuated vaccines that improve protective efficacy while minimizing residual virulence. Moreover, hypervariable MGFs provide strain-specific signatures for PCR-based diagnostics and phylogeographic tracking, directly addressing outbreak surveillance challenges. By unifying virology with translational innovation, this review establishes MGFs as priority targets for next-generation ASF countermeasures. Full article

With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article

Traditional fault feature generation models often face issues of uncontrollability, singularity, and slow convergence, limiting diagnostic accuracy. To address these challenges, this paper proposes a symmetry-aware approach that combines a conditional variational autoencoder (CVAE) and an auxiliary classifier generative adversarial network (ACGAN) for fault feature generation, leveraging symmetry characteristics inherent in fault data distributions and adversarial learning. Specifically, symmetrical Gaussian distributions in the CVAE enable robust extraction of latent fault features conditioned on fault classes, which are then input to the symmetrical adversarial framework of the ACGAN to guide the generator and discriminator toward a symmetrical Nash equilibrium. The original and generated features are jointly utilized in a convolutional neural network (CNN) for fault classification. Experimental results on the CWRU dataset show that the proposed CVAE-ACGAN achieves an average accuracy of 99.21%, precision of 97.81%, and recall of 98.24%, surpassing the baseline CNN. Similar improvements are achieved on the PADERBORN dataset. Furthermore, the model achieves significantly lower root mean square error (RMSE) and mean absolute error (MAE) than competing methods, confirming high consistency between the generated and real features and supporting its superior generalization and reliability. Visualization via confusion matrices and t-SNE further demonstrates clear boundaries between fault categories. These results affirm the value of incorporating symmetry principles into feature generation for mechanical fault diagnosis. Full article

This review synthesizes four decades (1980–2024) of marine oil spill research in the Philippines, analyzing 80 peer-reviewed publications sourced from Scopus, Web of Science, Clarivate, and Google Scholar. Findings show that oil spill research activity spikes after major spills, particularly the 2006 Guimaras incident, which accounts for over half of the reviewed studies and were mostly concentrated in the field of biology, followed by social sciences. Mangroves are the most studied as they are the widely affected ecosystem in the Philippines. Despite the number of published articles on oil spills in the Philippines, only the major events were emphasized, and small-scale spills remain under documented. Research on small-scale oil spills and the country’s two recent big oil spills (Mindoro Oil Spill and Manila Bay Oil Spill), particularly in a country’s environmentally sensitive areas, must be conducted in collaboration with academic institutions and relevant stakeholders to gain a deeper understanding and formulate appropriate countermeasures in the event of future spills. The review also highlights limited application of advanced techniques such as hydrocarbon fingerprinting, geospatial analysis, and next-generation DNA sequencing, limiting comprehensive assessments of oil fate and ecological effects. Addressing these gaps through interdisciplinary collaboration is critical to improving oil spill response, environmental management, and policy formulation in the Philippines’ complex archipelagic setting. Full article

The power spectral density (PSD) shape of the transmit waveform plays an important role in some fields of radar, such as electronic counter-countermeasures (ECCM), target detection, and target classification. In addition, radar hardware generally requires the waveform to have constant modulus (CM) characteristics. Therefore, it is a significant problem to synthesize the discrete-time CM waveform from a given PSD. To address this problem, some algorithms have been proposed in the existing literature. In this paper, based on the majorization–minimization (MM) framework, a novel algorithm is proposed to solve this problem. The proposed algorithm can be proved to converge to the stationary point, and the error reduction property can be obtained without the unitary requirements on the discrete Fourier transform (DFT) matrix. To accelerate the convergence rate of the proposed algorithm, three acceleration schemes are developed for the proposed algorithm. Considering a specific algorithm stopping condition, one of the proposed acceleration schemes shows better computation efficiency than the existing algorithms and is more robust to the initial points. Besides, when the DFT matrix is not unitary, the numerical results show that the proposed acceleration scheme has better matching performance compared with the existing algorithms. Full article

As the proportion of new energy generation continues to rise, power systems are confronted with novel challenges. Grid-forming converters, which possess voltage source characteristics and can support the grid, typically employ a VSG control strategy during normal operation to emulate the behavior of synchronous generators. This approach enhances frequency response and system stability in modern power systems. This review article systematically examines two typical fault control strategies for grid-forming converters: the switching strategy and the virtual impedance strategy. These different control strategies result in distinct fault response characteristics of the converter. Based on the analysis of fault control strategies for grid-forming converters, this study investigates the impact of the converter’s fault response characteristics on overcurrent protection, pilot protection, distance protection, and differential protection and investigates and prospects corresponding countermeasures. Finally, through simulation modeling, the fault response characteristics under different control strategies and their effects on protection are verified and analyzed. Focusing on grid-forming converters, this paper dissects the influence of their fault control strategies on relay protection, providing strong support for the wide application and promotion of grid-forming converters in new types of power systems. Full article

Federated edge learning (FEL) unites the decentralized training capabilities of multiple edge nodes to allow model gradient sharing and parameter aggregation across a peer-to-peer network. However, many intrinsic policy conflicts still exist in FEL, for example, the open accessibility of gradients will lead to the privacy leakage risk during the federal aggregation process. In this paper, we first identify that malicious users weaponized with generative artificial intelligence (GenAI) can generate fake samples that are almost identical to FEL participants’ training data. By analyzing how different configurations of GenAI affect attack effectiveness, we find that an adversary with strong patchwork and reconstruction capabilities can stealthily steal diverse training data from nearly all FEL participants. To thwart such a generative data reconstruction attack (GenDRA) scheme, we propose a novel target semantic dissolution (TSD) mechanism for enhancing the privacy-preserving ability of FEL, which encrypts only a very small number (≤10%) of gradient values in each training round that have a significant impact on human visual formation using format-preserving encryption. With TSD, the speculator cannot obtain a fake sample that is visually similar to the training sample because real gradients are actively concealed. Extensive experiments based on four benchmark datasets are performed to demonstrate the huge threat of GenAI and the effectiveness of TSD in all aspects: compelling accuracy performance, strong visual privacy guarantee, and low computing overhead. Full article