This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends t...This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].展开更多
Throughout the lifespan,an animal can encounter predators frequently,thus the ability to avoid attacks from predators is crucial for its survival.The chances of evading danger can be greatly improved if the animal can...Throughout the lifespan,an animal can encounter predators frequently,thus the ability to avoid attacks from predators is crucial for its survival.The chances of evading danger can be greatly improved if the animal can respond immediately to the threat.Therefore,when an animal detects a threat through its visual system,it must quickly direct its gaze and attention toward the source of danger,assess the threat level,and take appropriate action.展开更多
The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Tr...The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.展开更多
The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration ...The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.展开更多
As an emerging environmental contaminant,antibiotic resistance genes(ARGs)in tap water have attracted great attention.Although studies have provided ARG profiles in tap water,research on their abundance levels,composi...As an emerging environmental contaminant,antibiotic resistance genes(ARGs)in tap water have attracted great attention.Although studies have provided ARG profiles in tap water,research on their abundance levels,composition characteristics,and potential threat is still insufficient.Here,9 household tap water samples were collected from the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)in China.Additionally,75 sets of environmental sample data(9 types)were downloaded from the public database.Metagenomics was then performed to explore the differences in the abundance and composition of ARGs.221 ARG subtypes consisting of 17 types were detected in tap water.Although the ARG abundance in tap water was not significantly different from that found in drinking water plants and reservoirs,their composition varied.In tap water samples,the three most abundant classes of resistance genes were multidrug,fosfomycin and MLS(macrolide-lincosamidestreptogramin)ARGs,and their corresponding subtypes ompR,fosX and macB were also the most abundant ARG subtypes.Regarding the potential mobility,vanS had the highest abundance on plasmids and viruses,but the absence of key genes rendered resistance to vancomycin ineffective.Generally,the majority of ARGs present in tap water were those that have not been assessed and are currently not listed as high-threat level ARG families based on the World Health Organization Guideline.Although the current potential threat to human health posed by ARGs in tap water is limited,with persistent transfer and accumulation,especially in pathogens,the potential danger to human health posed by ARGs should not be ignored.展开更多
Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact t...Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact the safety and operations of rolling stock, the privacy and security of passengers and employees, and the public in general. The systematic literature review revealed that cyber threats to the railway industry can take many forms, including attacks on operational technology systems, data breaches, theft of sensitive information, and disruptions to train services. The consequences of these threats can be severe, leading to operational disruptions, financial losses, and loss of public trust in the railway system. To address these threats, railway organizations must adopt a proactive approach to security and implement robust cybersecurity measures tailored to the industry’s specific needs and challenges. This includes regular testing of systems for vulnerabilities, incident response plans, and employee training to identify and respond to cyber threats. Ensuring the system remains available, reliable, and maintainable is fundamental given the importance of railways as critical infrastructure and the potential harm that can be caused by cyber threats.展开更多
Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in ne...Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in networks and systems.Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data,learn patterns,and anticipate potential threats.Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly.Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks.This research advocates for proactive measures and adaptive technologies in defending digital environments.Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world.This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators.Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen.Furthermore,this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic.The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness,resulting in holistic protection against dynamic threats.展开更多
Beyond-visual-range(BVR)air combat threat assessment has attracted wide attention as the support of situation awareness and autonomous decision-making.However,the traditional threat assessment method is flawed in its ...Beyond-visual-range(BVR)air combat threat assessment has attracted wide attention as the support of situation awareness and autonomous decision-making.However,the traditional threat assessment method is flawed in its failure to consider the intention and event of the target,resulting in inaccurate assessment results.In view of this,an integrated threat assessment method is proposed to address the existing problems,such as overly subjective determination of index weight and imbalance of situation.The process and characteristics of BVR air combat are analyzed to establish a threat assessment model in terms of target intention,event,situation,and capability.On this basis,a distributed weight-solving algorithm is proposed to determine index and attribute weight respectively.Then,variable weight and game theory are introduced to effectively deal with the situation imbalance and achieve the combination of subjective and objective.The performance of the model and algorithm is evaluated through multiple simulation experiments.The assessment results demonstrate the accuracy of the proposed method in BVR air combat,indicating its potential practical significance in real air combat scenarios.展开更多
Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection ...Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.展开更多
Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social ...Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social partner is a crucial factor that shapes social decision-making,as oppositesex interactions are vital for fulfilling reproductive needs,whereas same-sex interactions are essential for both collaborative support and competitive behaviors.Under normal circumstances,mice typically exhibit a variety of prosocial behaviors that strengthen social bonds within their groups.展开更多
Inflammatory bowel disease(IBD),encompassing Crohn’s disease and ulcerative colitis,manifests as a chronic,recurrent,and refractory intestinal inflammatory condition significantly impacting patients’quality of life....Inflammatory bowel disease(IBD),encompassing Crohn’s disease and ulcerative colitis,manifests as a chronic,recurrent,and refractory intestinal inflammatory condition significantly impacting patients’quality of life.Despite ongoing research,its etiology and pathogenesis remain incompletely understood.Recent advancements in medical research highlight the critical role of drug combination therapies in managing IBD.This paper employs the strengths,weaknesses,opportunities,and threats framework to evaluate the four strategic elements(strengths,weaknesses,opportunities,and threats)pertaining to combination therapies for IBD.Among the strengths,the paper underscores the efficacy of multi-targeted strategies,the advancement of personalized medicine,and the mitigation of drug resistance.Nonetheless,the analysis identifies significant weaknesses,including the prohibitive cost of treatment,issues with patient compliance,and the necessity for comprehensive long-term safety data.The paper also delineates opportunities to augment therapeutic success through the incorporation of biomarkers,the application of artificial intelligence,and extensive international collaborative efforts.In contrast,the paper does not shy away from addressing the threats,which include the potential for therapeutic resistance and the logistical challenges inherent in global therapy deployment.These initiatives aim to refine future therapeutic practices,fostering safer,more effective,and personalized treatment paradigms for IBD patients.展开更多
As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respo...As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.展开更多
The Internet of Wearable Things(IoWT)or Wearable Internet of Things(WIoT)is a new paradigm that combines IoT and wearable technology.Advances in IoT technology have enabled the miniaturization of sensors embedded in w...The Internet of Wearable Things(IoWT)or Wearable Internet of Things(WIoT)is a new paradigm that combines IoT and wearable technology.Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks.IoWT devices are highly interdependent with mobile devices.However,due to their limited processing power and bandwidth,IoWT devices are vulnerable to cyberattacks due to their low level of security.Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have been actively researched.The threat analysis framework used in existing studies was limited to specific protocols and did not target IoWT devices.In addition,In the literature surveyed to date,no cyber threat analysis framework is targeting IoWT.Therefore,the threat model presented in the existing research on cyber threat analysis and modeling for IoWT is specialized for specific devices.In addition,because it does not present standardized attack tactics and techniques,there is a limitation in that it is difficult to identify attacks quickly.In this paper,we propose an Internet of Wearable Things threat analysis frameWork(IWTW)framework that can derive security threats through systematic analysis of IoWT attack cases and possible security threats and perform cyber threat analysis based on them.The methodology for developing the IWTW framework consists of three steps:Analysis,Standardization,and Compilation.IoWT attack cases and potential security threats are analyzed in the analysis stage.In the standardization stage,attack tactics and techniques derived from the analysis of attack cases and potential security threats are standardized,resulting in 3 attack categories,18 attack tactics,and 68 attack techniques.In the compilation stage,standardized security threats are combined to develop the IWTW framework ultimately.We present four case studies targeting MiBand 2,Fitbit Charge HR/Surge,Samsung Gear 3,Xiaomi Amazifit,Honor Band 5,Honor Watch ES,and Senbono CF-58 devices to validate the proposed IWTW framework.We analyzed the attack process through a case study and applied the IWTW framework to derive standardized attack categories,tactics,and techniques effectively.By applying the IWTW framework to cyber threat analysis targeting IoWT,security threats can be standardized,and the attack process can be quickly derived,enabling effective attack analysis on IoWT.展开更多
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t...In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.展开更多
Information technology is critical in coordinating patient records, smart devices, operations, and critical infrastructure in healthcare organizations, and their constantly changing digital environment, including supp...Information technology is critical in coordinating patient records, smart devices, operations, and critical infrastructure in healthcare organizations, and their constantly changing digital environment, including suppliers, doctors, insurance providers, and regulatory agencies. This dependence on interdependent systems makes this sector vulnerable to various information technology risks. Such threats include common cybersecurity risks such as data breaches and malware attacks, unique problems occurring in healthcare settings such as unauthorized access to patient records, disruptions in services provided at medical facilities, and potential harm caused to patients due to the compromise of medical devices. The threat taxonomies, such as the Open Threat Taxonomy, NIST, or ENISA, are foundational frameworks for grasping and categorizing IT threats. However, these taxonomies were not specifically designed to deal with the complexities of the healthcare industry. The problem arises from the gap between these taxonomies’ general nature and the industry-specific threats and vulnerabilities that affect healthcare organizations. As a result, many healthcare institutions fail to holistically address and eliminate the unique risks related to confidentiality, integrity, and availability of patients’ data as well as critical systems used in healthcare. This paper aims to narrow this gap by carefully assessing these taxonomies to determine the frame-work best suited for addressing the threat environment in the healthcare sector.展开更多
Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a gro...Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.展开更多
Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.展开更多
Considering the increased anthropogenic impacts,species with a limited range and low detectability often lack fundamental information and conservation actions,placing them at a high risk of endangerment.The Chinting a...Considering the increased anthropogenic impacts,species with a limited range and low detectability often lack fundamental information and conservation actions,placing them at a high risk of endangerment.The Chinting alpine toad Scutiger chintingensis is a rare mountain amphibian endemic to the eastern margin of the Qinghai-Xizang Plateau in China.Within its whole distribution range,only three known populations(Wolong,Emei,and Wawu)exist and no recent population status report has been documented for this species over the past two decades.From 2020 to 2023,we investigated the species distribution,and assessed the risk factors for the main populations.We recorded this species in all distribution areas,and updated a new distribution site with a lower elevation limit.The relative population density was 0.024±0.012 ind./m^(2)on Mount Emei,whereas only 0.008±0.017 ind./m^(2)on Mount Wawu.No significant difference was observed in the number of individuals between the two populations;however,the relative population density was significantly different.Sewage and waste discharge resulting from the construction of scenic areas,as well as disturbances from tourism,were the primary anthropogenic factors that influenced the survival of this species.Our results provide the updated information on the distribution and population status of the Chinting alpine toad,and suggest that unrecorded populations,as well as a wider elevation range,may exist for this species.Our findings emphasise the importance of timely updates of species distribution and population information and offer a basis for the future conservation of endangered amphibians.展开更多
The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advan...The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advanced tools and techniques for attacking targets with specific goals.Even countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted attack.APT is a sophisticated attack that involves multiple stages and specific strategies.Besides,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security system.However,APTs are generally implemented in multiple stages.If one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack failure.The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges.This survey paper will provide knowledge about APT attacks and their essential steps.This follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the work.Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.展开更多
The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated...The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.展开更多
文摘This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].
基金supported by the National Natural Science Foundation of China(32471055 and 82171090)Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)ZJLab,Shanghai Center for Brain Science and Brain-Inspired Technology,the Lingang Laboratory(LG-QS-202203-12).
文摘Throughout the lifespan,an animal can encounter predators frequently,thus the ability to avoid attacks from predators is crucial for its survival.The chances of evading danger can be greatly improved if the animal can respond immediately to the threat.Therefore,when an animal detects a threat through its visual system,it must quickly direct its gaze and attention toward the source of danger,assess the threat level,and take appropriate action.
文摘The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.
文摘The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.
基金supported by the National Key R&D Program of China(No.2022YFE0103200)the Hubei Provincial Natural Science Foundation of China(No.2021CFB016)the National Natural Science Foundation of China(No.52100217).
文摘As an emerging environmental contaminant,antibiotic resistance genes(ARGs)in tap water have attracted great attention.Although studies have provided ARG profiles in tap water,research on their abundance levels,composition characteristics,and potential threat is still insufficient.Here,9 household tap water samples were collected from the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)in China.Additionally,75 sets of environmental sample data(9 types)were downloaded from the public database.Metagenomics was then performed to explore the differences in the abundance and composition of ARGs.221 ARG subtypes consisting of 17 types were detected in tap water.Although the ARG abundance in tap water was not significantly different from that found in drinking water plants and reservoirs,their composition varied.In tap water samples,the three most abundant classes of resistance genes were multidrug,fosfomycin and MLS(macrolide-lincosamidestreptogramin)ARGs,and their corresponding subtypes ompR,fosX and macB were also the most abundant ARG subtypes.Regarding the potential mobility,vanS had the highest abundance on plasmids and viruses,but the absence of key genes rendered resistance to vancomycin ineffective.Generally,the majority of ARGs present in tap water were those that have not been assessed and are currently not listed as high-threat level ARG families based on the World Health Organization Guideline.Although the current potential threat to human health posed by ARGs in tap water is limited,with persistent transfer and accumulation,especially in pathogens,the potential danger to human health posed by ARGs should not be ignored.
文摘Given the unique challenges facing the railway industry, cybersecurity is a crucial issue that must be addressed proactively. This paper aims to provide a systematic review of cybersecurity threats that could impact the safety and operations of rolling stock, the privacy and security of passengers and employees, and the public in general. The systematic literature review revealed that cyber threats to the railway industry can take many forms, including attacks on operational technology systems, data breaches, theft of sensitive information, and disruptions to train services. The consequences of these threats can be severe, leading to operational disruptions, financial losses, and loss of public trust in the railway system. To address these threats, railway organizations must adopt a proactive approach to security and implement robust cybersecurity measures tailored to the industry’s specific needs and challenges. This includes regular testing of systems for vulnerabilities, incident response plans, and employee training to identify and respond to cyber threats. Ensuring the system remains available, reliable, and maintainable is fundamental given the importance of railways as critical infrastructure and the potential harm that can be caused by cyber threats.
基金funded by Ongoing Research Funding program(ORF-2025-749),King Saud University,Riyadh,Saudi Arabia.
文摘Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in networks and systems.Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data,learn patterns,and anticipate potential threats.Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly.Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks.This research advocates for proactive measures and adaptive technologies in defending digital environments.Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world.This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators.Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen.Furthermore,this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic.The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness,resulting in holistic protection against dynamic threats.
基金National Natural Science Foundation of China(62006193,62103338)Aeronautical Science Foundation of China(2022Z023053001)+1 种基金Key Research and Development Program of Shaanxi Province(2024GX-YBXM-115)Fundamental Research Funds for the Central Universities(D5000230150)。
文摘Beyond-visual-range(BVR)air combat threat assessment has attracted wide attention as the support of situation awareness and autonomous decision-making.However,the traditional threat assessment method is flawed in its failure to consider the intention and event of the target,resulting in inaccurate assessment results.In view of this,an integrated threat assessment method is proposed to address the existing problems,such as overly subjective determination of index weight and imbalance of situation.The process and characteristics of BVR air combat are analyzed to establish a threat assessment model in terms of target intention,event,situation,and capability.On this basis,a distributed weight-solving algorithm is proposed to determine index and attribute weight respectively.Then,variable weight and game theory are introduced to effectively deal with the situation imbalance and achieve the combination of subjective and objective.The performance of the model and algorithm is evaluated through multiple simulation experiments.The assessment results demonstrate the accuracy of the proposed method in BVR air combat,indicating its potential practical significance in real air combat scenarios.
基金funded by Universiti Teknologi Malaysia under the UTM RA ICONIC Grant(Q.J130000.4351.09G61).
文摘Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.
基金supported by grants from the National Natural Science Foundation of China(32471074 and 32200825)the STI2030-Major Projects(2021ZD0203000 and 2021ZD0203002)+1 种基金the Shandong Provincial Taishan Scholars Project(tsqn202306174)the Shandong Provincial Natural Science Foundation(ZR2022QC173).
文摘Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social partner is a crucial factor that shapes social decision-making,as oppositesex interactions are vital for fulfilling reproductive needs,whereas same-sex interactions are essential for both collaborative support and competitive behaviors.Under normal circumstances,mice typically exhibit a variety of prosocial behaviors that strengthen social bonds within their groups.
文摘Inflammatory bowel disease(IBD),encompassing Crohn’s disease and ulcerative colitis,manifests as a chronic,recurrent,and refractory intestinal inflammatory condition significantly impacting patients’quality of life.Despite ongoing research,its etiology and pathogenesis remain incompletely understood.Recent advancements in medical research highlight the critical role of drug combination therapies in managing IBD.This paper employs the strengths,weaknesses,opportunities,and threats framework to evaluate the four strategic elements(strengths,weaknesses,opportunities,and threats)pertaining to combination therapies for IBD.Among the strengths,the paper underscores the efficacy of multi-targeted strategies,the advancement of personalized medicine,and the mitigation of drug resistance.Nonetheless,the analysis identifies significant weaknesses,including the prohibitive cost of treatment,issues with patient compliance,and the necessity for comprehensive long-term safety data.The paper also delineates opportunities to augment therapeutic success through the incorporation of biomarkers,the application of artificial intelligence,and extensive international collaborative efforts.In contrast,the paper does not shy away from addressing the threats,which include the potential for therapeutic resistance and the logistical challenges inherent in global therapy deployment.These initiatives aim to refine future therapeutic practices,fostering safer,more effective,and personalized treatment paradigms for IBD patients.
文摘As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2021-II210493,5G Massive Next Generation Cyber Attack Deception Technology Development,90%)the Gachon University research fund of 2022(GCU-202300750001,10%).
文摘The Internet of Wearable Things(IoWT)or Wearable Internet of Things(WIoT)is a new paradigm that combines IoT and wearable technology.Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks.IoWT devices are highly interdependent with mobile devices.However,due to their limited processing power and bandwidth,IoWT devices are vulnerable to cyberattacks due to their low level of security.Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have been actively researched.The threat analysis framework used in existing studies was limited to specific protocols and did not target IoWT devices.In addition,In the literature surveyed to date,no cyber threat analysis framework is targeting IoWT.Therefore,the threat model presented in the existing research on cyber threat analysis and modeling for IoWT is specialized for specific devices.In addition,because it does not present standardized attack tactics and techniques,there is a limitation in that it is difficult to identify attacks quickly.In this paper,we propose an Internet of Wearable Things threat analysis frameWork(IWTW)framework that can derive security threats through systematic analysis of IoWT attack cases and possible security threats and perform cyber threat analysis based on them.The methodology for developing the IWTW framework consists of three steps:Analysis,Standardization,and Compilation.IoWT attack cases and potential security threats are analyzed in the analysis stage.In the standardization stage,attack tactics and techniques derived from the analysis of attack cases and potential security threats are standardized,resulting in 3 attack categories,18 attack tactics,and 68 attack techniques.In the compilation stage,standardized security threats are combined to develop the IWTW framework ultimately.We present four case studies targeting MiBand 2,Fitbit Charge HR/Surge,Samsung Gear 3,Xiaomi Amazifit,Honor Band 5,Honor Watch ES,and Senbono CF-58 devices to validate the proposed IWTW framework.We analyzed the attack process through a case study and applied the IWTW framework to derive standardized attack categories,tactics,and techniques effectively.By applying the IWTW framework to cyber threat analysis targeting IoWT,security threats can be standardized,and the attack process can be quickly derived,enabling effective attack analysis on IoWT.
基金Supported by the National Natural Science Foundation of China(No.62203390)the Science and Technology Project of China TobaccoZhejiang Industrial Co.,Ltd(No.ZJZY2022E004)。
文摘In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.
文摘Information technology is critical in coordinating patient records, smart devices, operations, and critical infrastructure in healthcare organizations, and their constantly changing digital environment, including suppliers, doctors, insurance providers, and regulatory agencies. This dependence on interdependent systems makes this sector vulnerable to various information technology risks. Such threats include common cybersecurity risks such as data breaches and malware attacks, unique problems occurring in healthcare settings such as unauthorized access to patient records, disruptions in services provided at medical facilities, and potential harm caused to patients due to the compromise of medical devices. The threat taxonomies, such as the Open Threat Taxonomy, NIST, or ENISA, are foundational frameworks for grasping and categorizing IT threats. However, these taxonomies were not specifically designed to deal with the complexities of the healthcare industry. The problem arises from the gap between these taxonomies’ general nature and the industry-specific threats and vulnerabilities that affect healthcare organizations. As a result, many healthcare institutions fail to holistically address and eliminate the unique risks related to confidentiality, integrity, and availability of patients’ data as well as critical systems used in healthcare. This paper aims to narrow this gap by carefully assessing these taxonomies to determine the frame-work best suited for addressing the threat environment in the healthcare sector.
文摘Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.
基金supported by the National Natural Science Foundation of China (6202201562088101)+1 种基金Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)Shanghai Municip al Commission of Science and Technology Project (19511132101)。
文摘Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
基金supported by the National Natural Science Foundation of China(32271737,32071544)the Interdisciplinary Innovation Team of the Chinese Academy of Sciences(CAS)“Light of West China”Program(xbzg-zdsys-202207)+1 种基金the Shenzhen Zhilan Foundation(2021070451A)Nature Science Foundation of Sichuan Province(2022NSFSC0125).
文摘Considering the increased anthropogenic impacts,species with a limited range and low detectability often lack fundamental information and conservation actions,placing them at a high risk of endangerment.The Chinting alpine toad Scutiger chintingensis is a rare mountain amphibian endemic to the eastern margin of the Qinghai-Xizang Plateau in China.Within its whole distribution range,only three known populations(Wolong,Emei,and Wawu)exist and no recent population status report has been documented for this species over the past two decades.From 2020 to 2023,we investigated the species distribution,and assessed the risk factors for the main populations.We recorded this species in all distribution areas,and updated a new distribution site with a lower elevation limit.The relative population density was 0.024±0.012 ind./m^(2)on Mount Emei,whereas only 0.008±0.017 ind./m^(2)on Mount Wawu.No significant difference was observed in the number of individuals between the two populations;however,the relative population density was significantly different.Sewage and waste discharge resulting from the construction of scenic areas,as well as disturbances from tourism,were the primary anthropogenic factors that influenced the survival of this species.Our results provide the updated information on the distribution and population status of the Chinting alpine toad,and suggest that unrecorded populations,as well as a wider elevation range,may exist for this species.Our findings emphasise the importance of timely updates of species distribution and population information and offer a basis for the future conservation of endangered amphibians.
文摘The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advanced tools and techniques for attacking targets with specific goals.Even countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted attack.APT is a sophisticated attack that involves multiple stages and specific strategies.Besides,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security system.However,APTs are generally implemented in multiple stages.If one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack failure.The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges.This survey paper will provide knowledge about APT attacks and their essential steps.This follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the work.Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.
文摘The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.
