Search Results (58)

The covert nature of DNS covert channels makes them a widely utilized method for data exfiltration by malicious attackers. In response to this challenge, the present study proposes a detection methodology for DNS covert channels that employs a Deep Boltzmann Machine with Enhanced Security (DBM-ENSec). This approach entails the creation of a dataset through the collection of malicious traffic associated with various DNS covert channel attacks. Time-dependent grouping features are excluded, and feature optimization is conducted on individual traffic data through feature selection and normalization to minimize redundancy, enhancing the differentiation and stability of the features. The result of this process is the extraction of 23-dimensional features for each DNS packet. The extracted features are converted to gray scale images to improve the interpretability of the model and then fed into an improved Deep Boltzmann Machine for further optimization. The optimized features are then processed by an ensemble of classifiers (including Random Forest, XGBoost, LightGBM, and CatBoost) for detection purposes. Experimental results show that the proposed method achieves 99.92% accuracy in detecting DNS covert channels, with a validation accuracy of up to 98.52% on publicly available datasets. Full article

Background: Under oxidative stress conditions, the increased levels of reactive oxygen species (ROS) within cells disrupt the intracellular homeostasis. Tartary buckwheat peptides exert their effects by scavenging oxidative free radicals, such as superoxide anion and hydrogen peroxide, thereby reducing oxidative damage within cells. Meanwhile, these peptides safeguard mitochondria by maintaining the mitochondrial membrane potential, decreasing the production of mitochondrial oxygen free radicals, and regulating mitochondrial biogenesis and autophagy to preserve mitochondrial homeostasis. Through these mechanisms, Tartary buckwheat peptides restore the intracellular redox balance, sustain cellular energy metabolism and biosynthesis, and ensure normal cellular physiological functions, which is of great significance for cell survival and adaptation under oxidative stress conditions. Objectives: In this experiment, a classical cellular oxidative stress model was established. Indicators related to antioxidant capacity and mitochondrial membrane potential changes, as well as pathways associated with oxidative stress, were selected for detection. The aim was to elucidate the effects of Tartary buckwheat oligopeptides on the metabolism of cells in response to oxidative stress. Methods: In this study, we established an oxidative damage model of mouse skeletal muscle myoblast (SOL8) cells using hydrogen peroxide (H₂O₂), investigated the pre-protective effects of Tartary buckwheat oligopeptides on H₂O₂-induced oxidative stress damage in SOL8 cells at the cellular level, and explored the possible mechanisms. The CCK-8 method is a colorimetric assay based on WST-8-[2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium, monosodiumsalt], which is used to detect cell proliferation and cytotoxicity. Results: The value of CCK-8 showed that, when the cells were exposed to 0.01 mmol/L H₂O₂ for 1 h and 10 mg/mL Tartary buckwheat oligopeptides intervention for 48 h, these were the optimal conditions. Compared with the H₂O₂ group, the intervention group (KB/H₂O₂ group) showed that the production of ROS was significantly reduced (p < 0.001), the malondialdehyde (MDA) content was significantly decreased (p < 0.05), and the activity of catalase (CAT) was significantly increased (p < 0.01); the mitochondrial membrane potential in the KB/H₂O₂ group tended to return to the level of the control group, and they all showed dose-dependent effects. Compared with the H₂O₂ group, the mRNA expression of KEAP1 in the KB/H₂O₂ group decreased, while the mRNA expression of NRF2α, HO-1, nrf1, PGC-1, P62, and PINK increased. Conclusions: Therefore, Tartary buckwheat oligopeptides have a significant pre-protective effect on H₂O₂-induced SOL8 cells, possibly by enhancing the activity of superoxide dismutase, reducing ROS attack, balancing mitochondrial membrane potential, and maintaining intracellular homeostasis. Full article

Background and clinical significance: Functional/dissociative seizures (FDSs) in adolescents are paroxysmal events which superficially resemble epileptic seizures or syncope. This study evaluated the effectiveness of brief cognitive analytic therapy (CAT). Case presentation: The patient was a 17-year-old white cisgender male with a diagnosis of non-epileptic attack disorder. The functional/dissociative seizures were treated with 8-session CAT, with follow-up at 5 weeks. Two target problems (TPs) and associated target problem procedures (TPPs) were rated for recognition and revision at each session and at follow-up. An A-B-C-FU single-case experimental evaluation of the TP/TPPs was conducted. Nomothetic outcome measures (DES-2 and RCADS) were administered at session 1, session 8, and at follow-up, and the YP-CORE and the Session Rating Scale were completed at each session. The patient was independently interviewed using the Change Interview 13 weeks after completing therapy. The results show that CAT effectively increased the recognition and revision of TPs/TPPs, four specific changes occurred (including cessation of functional seizures). There were pre–post reliable and clinically significant improvements to psychological wellbeing, but these were not maintained at follow-up. Conclusions: This study indicates that CAT was a partially effective intervention. The use of CAT as a treatment for FND in adolescents holds promise, but more research is needed. Full article

The landscape of 5G communication introduces heightened risks from malicious attacks, posing significant threats to network security and availability. The unique characteristics of 5G networks, while enabling advanced communication, present challenges in distinguishing between legitimate and malicious traffic, making it more difficult to detect anonymous traffic. Current methodologies for intrusion detection within 5G communication exhibit limitations in accuracy, efficiency, and adaptability to evolving network conditions. In this study, we explore the application of an adaptive optimized machine learning-based framework to improve intrusion detection system (IDS) performance in wireless network access scenarios. The framework used involves developing a lightweight model based on a convolutional neural network with 11 layers, referred to as CSO-2D-CNN, which demonstrates fast learning rates and excellent generalization capabilities. Additionally, an optimized attention-based XGBoost classifier is utilized to improve model performance by combining the benefits of parallel gradient boosting and attention mechanisms. By focusing on the most relevant features, this attention mechanism makes the model suitable for complex and high-dimensional traffic patterns typical of 5G communication. As in previous approaches, it eliminates the need to manually select features such as entropy, payload size, and opcode sequences. Furthermore, the metaheuristic Cat Swarm Optimization (CSO) algorithm is employed to fine-tune the hyperparameters of both the CSO-2D-CNN and the attention-based XGBoost classifier. Extensive experiments conducted on a recent dataset of network traffic demonstrate that the system can adapt to both binary and multiclass classification tasks for high-dimensional and imbalanced data. The results show a low false-positive rate and a high level of accuracy, with a maximum of 99.97% for multilabel attack detection and 99.99% for binary task classification, validating the effectiveness of the proposed framework in the 5G wireless context. Full article

The rapid development of communication in the last decade has heightened the necessity to create a secure platform for transferring data, including images, more than in previous years. One of the methods of secure image transmission is the encryption method. In this work, an encryption algorithm for multiple images is introduced. In the first step of the proposed algorithm, a key generation algorithm based on a chaotic system and wavelet transform is introduced, and in the next step, the encryption algorithm is developed by introducing rearrange and shift functions based on a chaotic system. One of the most important tools used in the proposed algorithm is the hybrid chaotic system, which is obtained by fractional derivatives and the Cat map. Different types of tests used to study the behavior of this system demonstrate the efficiency of the proposed hybrid system. In the last step of the proposed method, various statistical and security tests, including histogram analysis, correlation coefficient analysis, data loss and noise attack simulations, have been performed on the proposed algorithm. The results show that the proposed algorithm performs well in secure transmission. Full article

We analyse data from a rescue database collected at the Innsbruck Alpenzoo (Tyrol, Austria). The sample covers 33 years (1988–2020), and more than 5250 wild birds from 145 species originating from Innsbruck and the surrounding Inn Valley, one of the most densely populated areas in Europe. Both, the total number of birds as well as the number of bird species yearly admitted have increased since 1988. Orphaned nestlings and victims of glass collisions were the most common reasons for admission and responsible for the increase. Species’ susceptibility to accidental causes increased with regional abundance and degree of urbanisation. More urbanised species are characterised by a high proportion of nestlings and juveniles in the sample. The seasonal patterns of deliveries in these species show a peak in the late breeding season, and young birds are particularly susceptible to glass collisions and cat attacks. The species list also includes regionally rare wetland, upland and forest breeders and foreign migrants. Such species show a high proportion of admissions in autumn and collisions with windows play a greater role for short-distance migrants. Our data also suggest that small birds (<15 g body mass) are more likely to collide with glass panes than larger species. In conclusion, our data suggest that basically all bird groups and species are at least occasionally affected by human structures and activities in urbanised landscapes but support the notion that juveniles and migrants are more prone for accidents due to the lack of experience with anthropogenic structures in new areas. Full article

As the proliferation of Internet of Things (IoT) devices grows, challenges in security, privacy, and interoperability become increasingly significant. IoT devices often have resource constraints, such as limited computational power, energy efficiency, bandwidth, and storage, making it difficult to implement advanced security measures. Additionally, the diversity of IoT devices creates vulnerabilities and threats that attackers can exploit, including spoofing, routing, man-in-the-middle, and denial-of-service. To address these evolving threats, Intrusion Detection Systems (IDSs) have become a vital solution. IDS actively monitors network traffic, analyzing incoming and outgoing data to detect potential security breaches, ensuring IoT systems remain safeguarded against malicious activity. This study introduces an IDS framework that integrates ensemble learning with rule induction for enhanced model explainability. We study the performance of five ensemble algorithms (Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost) for developing effective IDS for IoT. The results show that XGBoost outperformed the other ensemble algorithms on two publicly available datasets for intrusion detection. XGBoost achieved 99.91% accuracy and 99.88% AUC-ROC on the CIC-IDS2017 dataset, as well as 98.54% accuracy and 93.06% AUC-ROC on the CICIoT2023 dataset, respectively. We integrate model explainability to provide transparent IDS system using a rule induction method. The experimental results confirm the efficacy of the proposed approach for providing a lightweight, transparent, and trustworthy IDS system that supports security analysts, end-users, and different stakeholders when making decisions regarding intrusion and non-intrusion events. Full article

Smart power grids (SGs) enhance efficiency, reliability, and sustainability by integrating distributed energy resources (DERs) such as solar panels and wind turbines. A key challenge in SGs is managing home battery charging during periods of insufficient renewable energy generation to ensure fairness, efficiency, and customer satisfaction. This paper introduces a secure reinforcement learning (RL)-based framework for optimizing battery charging coordination while addressing privacy concerns and false data injection (FDI) attacks. Privacy is preserved through Federated Learning (FL), enabling collaborative model training without sharing sensitive State of Charge (SoC) data that could reveal personal routines. To combat FDI attacks, Deep Learning (DL)-based detectors are deployed to identify malicious SoC data manipulation. To improve FL efficiency, the Change and Transmit (CAT) technique reduces communication overhead by transmitting only model parameters that experience enough change comparing to the last round. Extensive experiments validate the framework’s efficacy. The RL-based charging coordinator ensures fairness by maintaining SoC levels within thresholds and reduces overall power utilization through optimal grid power allocation. The CAT-FL approach achieves up to 93.5% communication overhead reduction, while DL-based detectors maintain high accuracy, with supervised models reaching 99.84% and anomaly detection models achieving 92.1%. Moreover, the RL agent trained via FL demonstrates strong generalization, achieving high cumulative rewards and equitable power allocation when applied to new data owners which did not participate in FL training. This framework provides a scalable, privacy-preserving, and efficient solution for energy management in SGs, offering high accuracy against FDI attacks and paving the way for the future of smart grid deployments. Full article

Moniliophthora perniciosa is one of the main pathogens affecting cocoa, and controlling it generally involves planting resistant genotypes followed by phytosanitary pruning. The identification of plant genes related to defense mechanisms is crucial to unravel the molecular basis of plant–pathogen interactions. Among the candidate genes, BiP stands out as a molecular chaperone located in the endoplasmic reticulum that facilitates protein folding and is induced under stress conditions, such as pathogen attacks. In this study, the SoyBiPD gene was expressed in Solanum lycopersicum plants and the plants were challenged with M. perniciosa. The control plants exhibited severe symptoms of witches’ broom disease, whereas the transgenic lines showed no or mild symptoms. Gel-free proteomics revealed significant changes in the protein profile associated with BiP overexpression. Inoculated transgenic plants had a higher abundance of resistance-related proteins, such as PR2, PR3, and PR10, along with increased activity of antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), guaiacol peroxidase, and fungal cell wall-degrading enzymes (glucanases). Additionally, transgenic plants accumulated less H₂O₂, indicating more efficient control of reactive oxygen species (ROS). The interaction network analysis highlighted the activation of defense-associated signaling and metabolic pathways, conferring a state of defensive readiness even in the absence of pathogens. These results demonstrate that BiP overexpression increases the abundance of defense proteins, enhances antioxidant capacity, and confers greater tolerance to biotic stress. This study demonstrates the biotechnological potential of the BiP gene for genetic engineering crops with increased resistance to economically important diseases, such as witches’ broom in cocoa. Full article

Human interaction with birds has never been more positive and supported by so many private citizens and professional groups. However, direct mortality of birds from anthropogenic causes has increased and has led to significant annual losses of birds. We know of the crucial impact of habitat loss on the survival of birds and its effects on biodiversity. Direct mortality via anthropogenic causes is an additive but biologically important cause of avian decline. This is the focus of this paper. This paper synthesises and interprets the data on direct anthropogenic causes of mortality in birds, and it also discusses emerging and relatively hidden problems, including new challenges that birds may not be able to manage. This paper points out that such deaths occur indiscriminately and have negative behavioural and reproductive consequences even for survivors. All of these factors are important to address, because any functional habitat depends on birds. This paper suggests that some of this death toll can be reduced substantially and immediately, even some of the seemingly intractable problems. This paper also proposes cross-disciplinary solutions, bearing in mind that “ecosystem services” provided by birds benefit us all, and that the continued existence of avian diversity is one cornerstone for human survival. Full article

The immune response in plants is regulated by several phytohormones and involves the overexpression of defense genes, including the pathogenesis-related (PR-) genes. The data reported in this paper indicate that nematodes can suppress the immune response by inhibiting the expression of defense genes. Transcripts from nine defense genes were detected by qRT-PCR in the roots of tomato plants at three and seven days post-inoculation (dpi) with living juveniles (J2s) of Meloidogyne incognita (root-knot nematodes, RKNs). All the salicylic acid (SA)-responsive genes tested (PR-1, PR-2, PR-4b, PR-5) were down-regulated in response to nematode infection. On the contrary, the expression of jasmonic acid (JA)-responsive genes, including ACO (encoding the enzyme 1-aminocyclopropane-1-carboxylic acid oxidase, which catalyzes the last step of ethylene (ET) biosynthesis) and JERF3 (Jasmonate Ethylene Response Factor 3), was unaffected by the infection. Conversely, the effect of nematode attack on the activities of the defense enzymes endoglucanase and endochitinase, encoded by PR-2 and PR-3, respectively, changed depending on the tested dpi. At 5 dpi, both enzymes were inhibited in inoculated plants compared to healthy controls. The genes encoding glutathione peroxidase (GPX) and catalase (CAT), both part of the antioxidant plant system, were highly overexpressed. Additionally, the activity of the antioxidant enzymes superoxide dismutase (SOD), CAT, and ascorbate peroxidase (APX) was enhanced in infected roots. Isoelectrofocusing of root extracts revealed novel SOD isoforms in samples from inoculated plants. Furthermore, plants were pre-treated with an array of key compounds, including hormone generators, inhibitors of SA or JA-mediated defense pathways, reactive oxygen species (ROS) scavengers and generators, inhibitors of ROS generation, and compounds that interfere with calcium-mediated metabolism. After treatments, plants were inoculated with RKNs, and nematodes were allowed to complete their life cycle. Factors of plant growth and infection level in treated plants were compared with those from untreated inoculated plants. Generally, compounds that decreased SA and/or ROS levels increased infection severity, while those that reduced JA/ET levels did not affect infection rates. ROS generators induced resistance against the pests. Compounds that silence calcium signaling by preventing its intake augmented infection symptoms. The data shown in this paper indicate that SA-mediated plant immune responses are consistently suppressed during the early stages of nematode infection, and this restriction is associated with the activation of the antioxidant ROS-scavenging system. Full article

Feline infectious peritonitis (FIP) is a fatal disease in cats caused by infection with feline coronavirus (FCoV). Despite severe inflammatory changes, defense mechanisms fail to achieve virus clearance. Some studies focused on various immune evasion mechanisms, but none of these studies elucidated the inefficacy of the complement system, which is one major player in FIP-associated immune pathogenesis. This study aimed to investigate the involvement of complement-regulating factors (CRFs). CRFs help modulate the immune response and prevent host tissue damage. Archived tissue samples from 31 deceased, FIP-affected cats were evaluated using multiplex immunohistochemistry for the spatial expression of the complement-regulating factors CD46 and CD59 in association with FIP lesions and their colocalization with complement-activating factor C1q and membrane attack complex C9 in relation to the presence and proximity of FCoV-infected cells. The FIP lesions of all 31 cats exhibited marked expression of both complement-regulating factors in proximity to FCoV-infected macrophages. Moreover, their expression in all 31 animals was significantly lower than the expression of the complement-activating factors C1q and C9 compared to areas farther distal to FCoV-infected cells. In conclusion, FCoV-infected macrophages in cats with FIP appear to use autocrine and paracrine expression of complement-regulating factors in their immediate environment to shield themselves from destruction by the complement system. Full article

The security of Wireless Sensor Networks (WSNs) is of the utmost importance because of their widespread use in various applications. Protecting WSNs from harmful activity is a vital function of intrusion detection systems (IDSs). An innovative approach to WSN intrusion detection (ID) utilizing the CatBoost classifier (Cb-C) and the Lyrebird Optimization Algorithm is presented in this work (LOA). As is typical in ID settings, Cb-C excels at handling datasets that are imbalanced. The lyrebird’s remarkable capacity to imitate the sounds of its surroundings served as inspiration for the LOA, a metaheuristic optimization algorithm. The WSN-DS dataset, acquired from Prince Sultan University in Saudi Arabia, is used to assess the suggested method. Among the models presented, LOA-Cb-C produces the highest accuracy of 99.66%; nevertheless, when compared with the other methods discussed in this article, its error value of 0.34% is the lowest. Experimental results reveal that the suggested strategy improves WSN-IoT security over the existing methods in terms of detection accuracy and the false alarm rate. Full article

The advanced metering infrastructure (AMI) of the smart grid plays a critical role in energy management and billing by enabling the periodic transmission of consumers’ power consumption readings. To optimize data collection efficiency, AMI employs a “change and transmit” (CAT) approach. This approach ensures that readings are only transmitted when there is enough change in consumption, thereby reducing data traffic. Despite the benefits of this approach, it faces security challenges where malicious consumers can manipulate their readings to launch cyberattacks for electricity theft, allowing them to illegally reduce their bills. While this challenge has been addressed for supervised learning CAT settings, it remains insufficiently addressed in unsupervised learning settings. Moreover, due to the distortion introduced in the power consumption readings due to using the CAT approach, the accurate prediction of future consumption for energy management is a challenge. In this paper, we propose a two-stage approach to predict future readings and detect electricity theft in the smart grid while optimizing data collection using the CAT approach. For the first stage, we developed a predictor that is trained exclusively on benign CAT power consumption readings, and the output of the predictor is the actual readings. To enhance the prediction accuracy, we propose a cluster-based predictor that groups consumers into clusters with similar consumption patterns, and a dedicated predictor is trained for each cluster. For the second stage, we trained an autoencoder and a one-class support vector machine (SVM) on the benign reconstruction errors of the predictor to classify instances of electricity theft. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results indicate that the prediction error is very small and the accuracy of detection of the electricity theft attacks is high. Full article

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications. Full article