Defensive Measures Research Articles

De novo transcriptome assembly and global analysis of differential gene expression of aphid tolerant wild mustard Rorippa indica (L.) Hiern infested by mustard aphid Lipaphis Erysimi (L.) Kaltenbach.

Rapeseed-mustard, the oleiferous Brassica species are important oilseed crops cultivated all over the globe. Mustard aphid Lipaphis erysimi (L.) Kaltenbach is a major threat to the cultivation of rapeseed-mustard. Wild mustard Rorippa indica (L.) Hiern shows tolerance to mustard aphids as a nonhost and hence is an important source for the bioprospecting of potential resistance genes and defense measures to manage mustard aphids sustainably. We performed mRNA sequencing of the R. indica plant uninfested and infested by the mustard aphids, harvested at 24 hours post-infestation. Following quality control, the high-quality reads were subjected to de novo assembly of the transcriptome. As there is no genomic information available for this potential wild plant, the raw reads will be useful for further bioinformatics analysis and the sequence information of the assembled transcripts will be helpful to design primers for the characterization of specific gene sequences. In this study, we also used the generated resource to comprehensively analyse the global profile of differential gene expression in R. indica in response to infestation by mustard aphids. The functional enrichment analysis of the differentially expressed genes reveals a significant immune response and suggests the possibility of chitin-induced defense signaling.

PEDV nucleocapsid antagonizes zinc-finger antiviral protein by disrupting the interaction with its obligate co-factor, TRIM25

The genomes of many pathogens contain high-CpG content, which is less common in most vertebrate host genomes. Such a distinct di-nucleotide composition in a non-self invader constitutes a special feature recognized by its host’s immune system. The zinc-finger antiviral protein (ZAP) is part of the pattern recognition receptors (PRRs) that recognize CpG-rich viral RNA and subsequently initiate RNA degradation as an antiviral defense measure. To counteract such ZAP-mediated restriction, some viruses evolve to either suppress the CpG content in their genome or produce an antagonistic factor to evade ZAP sensing. We have previously shown that a coronavirus, Porcine epidermic diarrhea virus (PEDV), employs its nucleocapsid protein (PEDV-N) to suppress the ZAP-dependent antiviral activity. Here, we propose a mechanism by which PEDV-N suppresses ZAP function by interfering with the interaction between ZAP and its essential cofactor, Tripartite motif-containing protein 25 (TRIM25). PEDV-N was found to interact with ZAP through its N-terminal domain and with TRIM25 through its C-terminal domain. We showed that PEDV-N and ZAP compete for binding to the SPla and the RYanodine Receptor (SPRY) domain of TRIM25, resulting in PEDV-N preventing TRIM25 from interacting with and promoting ZAP. Our result also showed that the presence of PEDV-N in the complex reduces the E3 ligase activity of TRIM25 on ZAP, which is required for the antiviral activity of ZAP. The host-pathogen interaction mechanism presented herein provides an insight into the new function of this abundant and versatile viral protein from a coronavirus which could be a key target for development of antiviral interventions.

Exploitation and prevention of Python prototype chain pollution

As more and more enterprises and organizations migrate their business infrastructure to the internet, the significance of software security vulnerabilities becomes increasingly prominent. Python, as a flexible and efficient programming language, plays a crucial role in web application development. Consequently, research on its security is imperative. In recent years, researchers have explored Python prototype chain pollution vulnerabilities. This paper aims to further investigate the exploitation of prototype chain pollution in Python and provide an explanation of pollution prevention. The paper will introduce the working principles of the prototype chain, attack scenarios, and analyze the exploitation of prototype chain pollution in Python using experimental samples. Finally, the article will propose two possible defense measures to assist developers in better mitigating potential development risks and safeguarding cyberspace security.

Navigating autonomy and control in human-AI delegation: User responses to technology- versus user-invoked task allocation

Users can increasingly delegate to information systems (IS) – that is transferring rights and responsibilities regarding certain tasks – even to the degree that IS can act autonomously (i.e., without the intervention or supervision of users). What is more, IS increasingly offer to assume the rights and responsibilities for a task not only in response to user prompts (i.e., user-invoked delegation) but also without user prompts (i.e., IS-invoked delegation). Yet, little is known about whether, how, and why users agree to delegation when they are asked by the IS in contrast to when they self-initiate the delegation. Drawing on self-affirmation theory, we investigate user acceptance of IS- versus user-invoked delegation in two complementary online experiments in software development. Our core findings reveal that IS-invoked (vs. user-invoked) delegation increases users' perceived self-threat and thus decreases their willingness to accept delegation. This threatening effect is larger the less (vs. more) the user perceives control after the potential delegation. Taken together, we uncover defensive user responses to IS-invoked delegation. Furthermore, we shed light on the underlying and moderating mechanisms representing the reasons and contextual features that explain and mitigate these defensive measures. These findings have significant implications for IS designers seeking to improve user-IS collaboration and outcomes by employing IS-invoked delegation.

Evaluation of the effectiveness of defense measures during radiological risk assessment in gynecological brachytherapy

The implementation of risk analysis to all those practices that work with ionizing radiation is of paramount importance. Thanks to the studies published on risk assessment in radiation medicine, a culture on this subject is being created, which has led to the systematization of different methods created for this purpose. Such is the case of the Failure Mode and Effects Analysis (FMEA), the basis on which this research has been developed. The American Association of Physicist in Medicine (AAMP) TG-100 report, which provides all the details on the use of this technique, is used as main reference. On the other hand, the recommendations recently published by AAMP TG-275 report are also used, applying the FMEA methodology to the high dose rate gynecological brachytherapy (B-HDR-GYN), among other practices. The novelty of this research is the implementation of an operational algorithm that improves the analytical capacity of the traditional FMEA approach by allowing the measurement of the effectiveness of defense measures within it. In this study, when the defense measures interact in the FMEA, a decrease of four RPN values of the failure modes (FM) occurs; compared to reference values. This algorithm is based on a synergy of the risk matrix (RM) approach and the information on FMEA available in the TG-100 and TG-275.

Look away now! Defensive processing and unrealistic optimism by level of alcohol consumption

Objective Health risk information is insufficient as a means of reducing alcohol use, particularly when it evokes negative emotional states amongst those for whom it is most personally relevant. Appraisal biases, or ‘defensive processing’, may be employed to mitigate the psychological discomfort posed by such information. Few studies have evaluated the role of defensive processing in people with different levels of alcohol consumption. Design Online participants (n = 597) completed measures of defensive processing of a health risk infographic, perceived susceptibility and severity of alcohol use, efficacy for resisting alcohol use, unrealistic optimism, the Alcohol Use Disorder Identification Test – Consumption (AUDIT-C) and demographics. Results AUDIT-C scores were positively and linearly associated with all defensive processing measures (Pearson’s correlation r from.16 to .36), threat and susceptibility (r = .16) and unrealistic optimism (r = .50). AUDIT-C scores were also negatively associated with efficacy for controlling alcohol use (r = −0.48). Conclusion People with alcohol use disorder (AUD) engaged in much more defensive processing of alcohol-related messages, offering an explanation for why such messages are limited at eliciting behaviour change. High levels of unrealistic optimism in people with alcohol use disorder may reflect low problem recognition in order to maintain a problem-free drinking identity.

A Review on Cybersecurity in Fintech: Threats, Solutions, and Future Trends

The rapid growth in the fintech industry propels financial services into the digital era, bringing unprecedented convenience and efficiency. However, this transformation could be a smoother process; it faces difficulties, primarily in Cybersecurity. This comprehensive study explores the cybersecurity landscape in the fintech industry, including common threats, existing defensive measures, and innovative solutions that shape the future. Significant dangers, such as data breaches, phishing attacks, and malware complications, emphasize the need for strong cybersecurity strategies. Fintech firms address these concerns by employing various defensive measures, including encryption technology, robust multi-factor authentication, and strict compliance with legal frameworks. Examining prospects, the article explores emerging themes such as the mysterious domain of quantum-resistant cryptography, the mysterious frontier of behavioral analytics, and the shift toward decentralized identification solutions. These developments demonstrate a proactive shift in strategy, anticipating and preparing for potential hazards to prevent and minimize their impact. The conclusion presents important findings, drawing out their implications for the future and proposing sensible suggestions for further research and industrial practices. This research provides vital insights for stakeholders in the rapidly changing field of fintech, helping them navigate the complex intersection of finance and technology and guaranteeing a safe journey through unfamiliar areas.

WaTrojan: Wavelet domain trigger injection for backdoor attacks

Backdoor attacks have been proven to pose effective threats to deep neural networks in various domains, such as biometrics, authentication, and autonomous driving. Attackers compromise the integrity of the model, causing it to behave normally on benign samples under normal circumstances but perform attacker-specified actions on samples containing specific triggers. However, existing attack methods often suffer from two main drawbacks: permissions and concealment. While some attack methods may not require high levels of permissions from the attacker, the triggers are typically visible to the naked eye, significantly reducing the attack's concealment and making it susceptible to detection by existing defense mechanisms. Although many advanced attack methods enhance concealment, they often necessitate control over the model's training process, thereby significantly limiting the practical applicability of the attack. To circumvent the two aforementioned drawbacks, we propose a novel backdoor attack method called WaTrojan, which implements the attack by adding triggers in the wavelet domain. The key to this attack lies in adding perturbations to the wavelet domain of an image, thereby altering the entire spatial domain of pixels. This approach challenges many assumptions of existing defense methods and makes poisoned images nearly indistinguishable from clean images visually. We evaluate WaTrojan on five benchmark datasets, including MNIST, CIFAR-10, GTSRB, CelebA, and ImageNet. The results indicate that our attack achieves an extremely high attack success rate while causing almost no drop in accuracy on benign samples. The visual quality of the poisoned images is high, with little perceptual difference from benign images. Furthermore, we assess the performance of WaTrojan under existing defense measures, and the results show that WaTrojan is robust and can significantly evade and resist the impacts generated by these defense measures.

Navigating the Cyber Kill Chain: A modern approach to pentesting

The Cyber Kill Chain is a strategic model that outlines the stages of a cyberattack, from initial reconnaissance to achieving the final objective. This framework is often mirrored in penetration testing (pentest), a legal and authorized simulated attack on a computer system performed to evaluate its security. By understanding the steps in the Cyber Kill Chain, penetration testers can mimic the strategies of malicious attackers, exploring vulnerabilities at each stage of the chain. This approach allows organizations to evaluate their defensive measures across the full spectrum of an attack, identifying weaknesses and enhancing their security protocols accordingly. In essence, the Cyber Kill Chain provides a roadmap for pen-testers to systematically evaluate an organization's cyber defences. The research method of this article involves a systematic analysis of the Cyber Kill Chain model, examining how penetration testers can employ this strategic framework to emulate the tactics of malicious attackers and identify methodology at each stage of the chain.

Redox Biomarkers and Matrix Remodeling Molecules in Ovarian Cancer.

Ovarian cancer (OC) has emerged as the leading cause of death due to gynecological malignancies among women. Oxidative stress and metalloproteinases (MMPs) have been shown to influence signaling pathways and afflict the progression of carcinogenesis. Therefore, the assessment of matrix-remodeling and oxidative stress intensity can determine the degree of cellular injury and often the severity of redox-mediated chemoresistance. The study group comprised 27 patients with serous OC of which 18% were classified as Federation of Gynecology and Obstetrics (FIGO) stages I/II, while the rest were diagnosed grades III/IV. The control group comprised of 15 ovarian tissue samples. The results were compared with genetic data from The Cancer Genome Atlas. Nitro-oxidative stress, inflammation and apoptosis biomarkers were measured colorimetrically/fluorometrically or via real-time PCR in the primary ovarian tumor and healthy tissue. Stratification of patients according to FIGO stages revealed that high-grade carcinoma exhibited substantial alterations in redox balance, including the accumulation of protein glycoxidation and lipid peroxidation products. TCGA data demonstrated only limited prognostic usefulness of the studied genes. In conclusion, high-grade serous OC is associated with enhanced tissue oxidative/nitrosative stress and macromolecule damage that could not be overridden by the simultaneously augmented measures of antioxidant defense. Therefore, it can be assumed that tumor cells acquire adaptive mechanisms that enable them to withstand the potential toxic effects of elevated reactive oxygen species.

Study on structural damage mechanisms and defence of rectangular closed-section simplified hull girders with thinner plates under near-field underwater explosions

Understanding the damage mechanisms of typical ship structures under fatal underwater explosion loads is crucial for developing effective defensive measures. To provide references for enhancing the survivability of ships, this paper conducts analyses of the damage mechanisms of simplified hull girders (SHGs) using a combination of experimental and simulation methods, and proposes targeted structural improvements. Initially, two types of thin-walled rectangular closed-section SHGs are subjected to near-field underwater explosion experiments. Corresponding simulation models are established, and the validity of the computational results is verified. The progressive damage processes of SHGs in hogging and sagging are examined from a structural perspective. Furthermore, the thin-walled plate deformation transfer processes are analyzed. Finally, based on the analyzed damage mechanisms, targeted defensive structures for overall damage are designed. The study results show that enhancing the anti-deformation capability of the upper and lower edges of the side plates can effectively improve the overall bending resistance of the SHG.

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW

This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner attack to perturb the original images and analyze their impact on the model’s classification accuracy. Additionally, image manipulation techniques were investigated as defensive measures against adversarial attacks. The results highlighted the model’s vulnerability to conflicting examples: the Fast Gradient Signed Method effectively altered the original classifications, while the Carlini and Wagner method proved less effective. Promising approaches such as noise reduction, image compression, and Gaussian blurring were presented as effective countermeasures. These findings underscore the importance of addressing the vulnerability of machine learning models and the need to develop robust defenses against adversarial examples. This article emphasizes the urgency of addressing the threat posed by harmful standards in machine learning models, highlighting the relevance of implementing effective countermeasures and image manipulation techniques to mitigate the effects of adversarial attacks. These efforts are crucial to safeguarding model integrity and trust in an environment marked by constantly evolving hostile threats. An average 25% decrease in accuracy was observed for the VGG16 model when exposed to the Fast Gradient Signed Method and Projected Gradient Descent attacks, and an even more significant 35% decrease with the Carlini and Wagner method.

The influence of fears of perceived legal consequences on general practitioners’ practice in relation to defensive medicine – a cross-sectional survey in Germany

BackgroundMedical decisions are influenced by a variety of factors also by legal requirements and feelings of uncertainty, which results in the term defensive medicine. The aim of the study was to evaluate the influence of fears of perceived legal consequences on the practice of defensive medicine from the perspective of German general practitioners (GPs).MethodsA cross-sectional study was performed from April to May 2022. GPs were invited via an e-mail newsletter of the Institute for Continuing Education in Family Medicine in the German Association of General Practitioners and via an online platform of the German College of General Practitioners and Family Physicians. The evaluation of legal fears, the general assessment of defensive medicine and reasons for and the frequency of defensive medical measures were surveyed in this study. Beside descriptive analyses, a stepwise linear regression analysis was used to explore potential associations between for the primary outcome variable ‘fears of legal consequences’ on the practice of defensive medicine.Results413 general practitioners with an average age of 50 years (51% female) responded. The majority rated their fears of legal consequences as low to average whereas for almost a third (27%, n = 113) the fears were strong to very strong. Regarding legal fears, the physician-patient-relationship played a fairly to very large role for 48% (n = 198) of the respondents. One third estimated the probability of being sued civilly in the next 10 years as rather high to very high. 47% (n = 193) of the participants assumed that the risk of being sued could mostly to very much be reduced by defensive medicine. Legal self-protection was for 38% of the responders (n = 157) quite frequently to very frequently a reason for acting defensively. Consequently, half of the respondents stated that they performed unnecessary laboratory tests at least once per week and 40% indicated that they referred patients for radiological diagnostics without medical indication once per month.ConclusionsAs legal fears have an influence on medical practice and legal self-protection being a frequent reason for defensive behaviour, understanding and knowledge of the law should be improved by legal education at university and further training of post-graduate trainees and practicing physicians should be implemented. Additionally, a more in-depth enlightenment of society about the phenomenon of Protective and Defensive Medicine and its consequences could be a possibility to decrease the perceived fears of legal consequences on the physicians’ side.

From Detection to Prediction: AI-powered SIEM for Proactive Threat Hunting and Risk Mitigation

The evolution of cybersecurity has witnessed a transformative shift from reactive defense measures to proactive threat-hunting and risk-mitigation strategies. In response to the rapidly evolving threat landscape, the integration of Artificial Intelligence (AI) into Security Information and Event Management (SIEM) tools has emerged as a crucial solution. Historically, SIEMs primarily aggregated security data but struggled to analyze the vast, complex datasets effectively. The integration of AI, especially Machine Learning (ML) and Deep Learning (DL), revolutionized these systems. AI algorithms enable SIEMs to extract meaningful insights from massive datasets, allowing for the identification of subtle anomalies and hidden threats that may not be detected by traditional detection methods. This transition marks a fundamental shift from simple data aggregation to intelligent analysis, empowering SIEMs to move beyond detection towardproactive threat hunting. This paper highlights the role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes. It also explores the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation.

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.

A survey on vulnerability of federated learning: A learning algorithm perspective

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning.

Internet of Things Security Early Warning Model Based on Deep Learning in Edge Computing Environment

Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6[Formula: see text]ms, respectively, and it can detect and defend various network attacks well, with high security performance.

Úkryty na slovenských železniciach počas 2. svetovej vojny

The study examines civil air defence (CPO) measures aimed at protecting by sheltering at stations and premises of the Slovak Railways during World War 2. It provides a brief overview of the legislation and regulations governing CPO during this period. The author chronologi cally describes the developments in the construction of shelters, highlighting significant events throughout the war. Additionally, the text offers insights into the location, type, construction, and usage of protective structures designed to shelter railway workers and passengers. Another focus is on the protective properties of these shelters and their utilization during air raids. The study enumerates various types of shelters at more prominent stations, with a detailed focus on Bratislava. Keywords: Slovak Railways, World War 2, Civil Defence, Shelter, Bratislava

The provision of balanced diet aboard of see-going ship as a measure of defense of health of crew members

The unreasonable diet is threatening not only by development of hypodynamia but also by loosing job on board of sea vessel. Such diseases of seamen as gastric ulcer, duodenal ulcer, gastritis, pancreatitis are a basis of non-making or non-extending employment contract. The significant causes of occurrence and development of this category of occupational illness are lacking of proper diet on shipboard that considers conditions of voyages, inadequate qualification training of ship cooks, as well as insufficient funding by particular shipowners daily food allowance of crew members. The article reveals requirements of international and national normative documents regarding organization of proper diet and table service, determines daily diet of crew members of sea and river vessels. The results of survey of active sailors and cadets of higher maritime school regarding the satisfaction of organized diet are discussed. The recommendations to ensure balanced rations on board the ship are proposed.

Coupling Effect and Chain Evolution of Urban Rail Transit Emergencies

Emergency events such as fire, flood and COVID-19 occurred in urban rail transit (URT) usually triggered chain effect and evolved into huge disaster. This kind of chain with complexity and uncertainty evolution brought great challenges to the safety management of the system. Thus, the coupling effects of emergencies and then its relationship with the chain evolution is necessary to analyze emphatically. A Graph Evaluation and Review Technique Simulation (GERTS) evolution network is firstly constructed to describe the coupling effect and chain evolution of emergencies. Then, considering the internal and external influencing factors of the emergency chain, a dynamic evolution model of the emergency chain based on Coupled Map Lattice (CML) is proposed. This paper takes fire chain of URT as an example to simulate the evolution process of emergency chain, and analyze the impact of different coupling effects and various influencing factors on the evolution of emergency chain. The results of numerical simulation show that the AND-coupling can significantly inhibit the evolution of emergency events, while the OR-coupling and CO-coupling can expand the impact scope of emergency events. In addition, the evolution speed of emergency events can be controlled by increasing the coupling action time and improving the URT repair ability. When an emergency event occurs, the analysis of coupling effect and the accompanied chain evolution will help managers to make scientific judgment on the development trend of the emergency events and make targeted emergency defense measures.