The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity threats.In response,this...The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity threats.In response,this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks(CNN),Autoencoders,and Long Short-Term Memory(LSTM)networks—to engineer an advanced Intrusion Detection System(IDS)specifically designed for IoT environments.Utilizing the comprehensive UNSW-NB15 dataset,which encompasses 49 distinct features representing varied network traffic characteristics,our methodology focused on meticulous data preprocessing including cleaning,normalization,and strategic feature selection to enhance model performance.A robust comparative analysis highlights the CNN model’s outstanding performance,achieving an accuracy of 99.89%,precision of 99.90%,recall of 99.88%,and an F1 score of 99.89%in binary classification tasks,outperforming other evaluated models significantly.These results not only confirm the superior detection capabilities of CNNs in distinguishing between benign and malicious network activities but also illustrate the model’s effectiveness in multiclass classification tasks,addressing various attack vectors prevalent in IoT setups.The empirical findings from this research demonstrate deep learning’s transformative potential in fortifying network security infrastructures against sophisticated cyber threats,providing a scalable,high-performance solution that enhances security measures across increasingly complex IoT ecosystems.This study’s outcomes are critical for security practitioners and researchers focusing on the next generation of cyber defense mechanisms,offering a data-driven foundation for future advancements in IoT security strategies.展开更多
The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security....The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.展开更多
Given the grave local and international network security landscape,a national strategic level analysis indicates that the modernization and advancement within the Industry 4.0 era are closely correlated with overall c...Given the grave local and international network security landscape,a national strategic level analysis indicates that the modernization and advancement within the Industry 4.0 era are closely correlated with overall competitive strength.Consequently,China proposed a strategy for the integration of industrialization and informatization,optimizing and adjusting its industrial structure to swiftly achieve transformation and upgrading in the Industry 4.0 era,thereby enhancing the sophistication of intelligent industrial control systems.The distributed control system in a nuclear power plant functions as an industrial control system,overseeing the operational status of the physical process.Its ability to ensure safe and reliable operation is directly linked to nuclear safety and the cybersecurity of the facility.The management of network security in distributed control systems(DCS)is crucial for achieving this objective.Due to the varying network settings and parameters of the DCS implemented in each nuclear power plant,the network security status of the system sometimes diverges from expectations.During system operation,it will undoubtedly encounter network security issues.Consequently,nuclear power plants utilize the technical criteria outlined in GB/T 22239 to formulate a network security management program aimed at enhancing the operational security of DCS within these facilities.This study utilizes existing network security regulations and standards as a reference to analyze the network security control standards based on the nuclear power plant’s control system.It delineates the fundamental requirements for network security management,facilitating integration with the entire life cycle of the research,development,and application of the nuclear power plant’s distributed control system,thereby establishing a network security management methodology that satisfies the control requirements of the nuclear power plant.Initially,it presents DCS and network security management,outlines current domestic and international network security legislation and standards,and specifies the standards pertinent to the administration of DCS in nuclear power plants.Secondly,the design of network security management for DCS is executed in conjunction with the specific context of nuclear power plants.This encompasses the deployment of network security apparatus,validation of the network security management strategy,and optimization adjustments.Consequently,recommendations beneficial to the network security management of nuclear power plants are compiled,aimed at establishing a management system and incorporating the concept of full life cycle management,which is predicated on system requirements,system design,and both software and hardware considerations.Conversely,it presents the notion of comprehensive life cycle management and suggests network security management strategies encompassing system requirements,system architecture,detailed hardware and software design and implementation,procurement,internal system integration,system validation and acceptance testing,system installation,operational maintenance,system modifications,and decommissioning.We will consistently enhance the performance and functionality of DCS in nuclear power plants,establish a safe and secure operational environment,and thereby facilitate the implementation of DCS in nuclear facilities while ensuring robust network security in the future.展开更多
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
With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these t...With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.展开更多
After an introduction to the implementation of supervisory computer control (SCC) through networks and the relevant security issues, this paper centers on the core of network security design: intelligent front-end pro...After an introduction to the implementation of supervisory computer control (SCC) through networks and the relevant security issues, this paper centers on the core of network security design: intelligent front-end processor (FEP), encryption/decryption method and authentication protocol. Some other system-specific security measures are also proposed. Although these are examples only, the techniques discussed can also be used in and provide reference for other remote control systems.展开更多
The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challe...The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.展开更多
Society is becoming increasingly dependent on cyberspace for both business and pleasure. Cyber attackers continue to attack organizational computer networks, as those same computer networks become increasing critical ...Society is becoming increasingly dependent on cyberspace for both business and pleasure. Cyber attackers continue to attack organizational computer networks, as those same computer networks become increasing critical to organizational business process. Strategic planning and managing IT security risks play an important role in the business and government planning process. Deploying defense in depth security measures can ensure that organizations continue to function in times of crisis. This quantitative study explores whether the Latin Square Design (LSD) model can be effectively applied to the prioritization of cybersecurity threats and to the linking of information assurance defense in-depth measures to those threats. The methods used in this study consisted of scanning 10 Cybersecurity Websites such as the Department of Homeland Security US CERT (United States-Computer Emergency Readiness Team [1]) and the SANS Institute (SysAdmin, Audit, Network and Security [2]) using the Likert Scale Model for the Website’s top ten list of cyber threats facing organizations and the network defense in depth measures to fight those threats. A comparison of each cybersecurity threats was then made using LSD to determine whether the Likert scale and the LSD model could be effectively applied to prioritize information assurance measures to protect organizational computing devices. The findings of the research reject the H0 null hypothesis that LSD does not affect the relationship between the ranking of 10 Cybersecurity websites top ten cybersecurity threats dependent variables and the independent variables of defense in depth measures used in protecting organizational devices against cyber-attacks.展开更多
This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intr...This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.展开更多
To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From ...To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.展开更多
As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,p...As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,providing a smart power grid with increased control,stability,and safety.Based on the secure communication requirements of cloud energy storage systems,this paper presents the design and development of a node controller for a cloud energy storage network.The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system.Safety protection measures were proposed according to the demands of the communication network,allowing the system to run safely and stably.Finally,the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou,China.The system was observed to operate safely and stably,demonstrating good peak-clipping and valley filling effects,and improving the system load characteristics.展开更多
This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactic...This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted?self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.展开更多
The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptio...The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.展开更多
Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities f...Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.展开更多
The advent of 6G wireless technology,which offers previously unattainable data rates,very low latency,and compatibility with a wide range of communication devices,promises to transform the networking environment compl...The advent of 6G wireless technology,which offers previously unattainable data rates,very low latency,and compatibility with a wide range of communication devices,promises to transform the networking environment completely.The 6G wireless proposals aim to expand wireless communication’s capabilities well beyond current levels.This technology is expected to revolutionize how we communicate,connect,and use the power of the digital world.However,maintaining secure and efficient data management becomes crucial as 6G networks grow in size and complexity.This study investigates blockchain clustering and artificial intelligence(AI)approaches to ensure a reliable and trustworthy communication in 6G.First,the mechanisms and protocols of blockchain clustering that provide a trusted and effective communication infrastructure for 6G networks are presented.Then,AI techniques for network security in 6G are studied.The integration of AI and blockchain to ensure energy efficiency in 6Gnetworks is addressed.Next,this paper presents howthe 6G’s speed and bandwidth enables AI and the easy management of virtualized systems.Using terahertz connections is sufficient to have virtualized systems move compute environments as well as data.For instance,a computing environment can follow potential security violations while leveraging AI.Such virtual machines can store their findings in blockchains.In 6G scenarios,case studies and real-world applications of AI-powered secure blockchain clustering are given.Moreover,challenges and promising future research opportunities are highlighted.These challenges and opportunities provide insights from the most recent developments and point to areas where AI and blockchain further ensure security and efficiency in 6G networks.展开更多
Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHS...Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.展开更多
The total reliance on internet connectivity and World Wide Web (WWW) based services is forcing many organizations to look for alternative solutions for providing adequate access and response time to the demand of thei...The total reliance on internet connectivity and World Wide Web (WWW) based services is forcing many organizations to look for alternative solutions for providing adequate access and response time to the demand of their ever increasing users. A typical solution is to increase the bandwidth;this can be achieved with additional cost, but this solution does not scale nor decrease users perceived response time. Another concern is the security of their network. An alternative scalable solution is to deploy a proxy server to provide adequate access and improve response time as well as provide some level of security for clients using the network. While some studies have reported performance increase due to the use of proxy servers, one study has reported performance decrease due to proxy server. We then conducted a six-month proxy server experiment. During this period, we collected access logs from three different proxy servers and analyzed these logs with Webalizer a web server log file analysis program. After a few years, in September 2010, we collected log files from another proxy server, analyzed the logs using Webalizer and compared our results. The result of the analysis showed that the hit rate of the proxy servers ranged between 21% - 39% and over 70% of web pages were dynamic. Furthermore clients accessing the internet through a proxy server are more secured. We then conclude that although the nature of the web is changing, the proxy server is still capable of improving performance by decreasing response time perceived by web clients and improved network security.展开更多
In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proo...In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proof-of-work consensus,is a fundamental measure of preventing various attacks.This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain.We use vector error correction modeling to analyze the endogenous relationships between the hashrate,Bitcoin price,and transaction fee,revealing the need for an additional variable to achieve our aim.Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes.This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.展开更多
Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
文摘The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity threats.In response,this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks(CNN),Autoencoders,and Long Short-Term Memory(LSTM)networks—to engineer an advanced Intrusion Detection System(IDS)specifically designed for IoT environments.Utilizing the comprehensive UNSW-NB15 dataset,which encompasses 49 distinct features representing varied network traffic characteristics,our methodology focused on meticulous data preprocessing including cleaning,normalization,and strategic feature selection to enhance model performance.A robust comparative analysis highlights the CNN model’s outstanding performance,achieving an accuracy of 99.89%,precision of 99.90%,recall of 99.88%,and an F1 score of 99.89%in binary classification tasks,outperforming other evaluated models significantly.These results not only confirm the superior detection capabilities of CNNs in distinguishing between benign and malicious network activities but also illustrate the model’s effectiveness in multiclass classification tasks,addressing various attack vectors prevalent in IoT setups.The empirical findings from this research demonstrate deep learning’s transformative potential in fortifying network security infrastructures against sophisticated cyber threats,providing a scalable,high-performance solution that enhances security measures across increasingly complex IoT ecosystems.This study’s outcomes are critical for security practitioners and researchers focusing on the next generation of cyber defense mechanisms,offering a data-driven foundation for future advancements in IoT security strategies.
文摘The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.
文摘Given the grave local and international network security landscape,a national strategic level analysis indicates that the modernization and advancement within the Industry 4.0 era are closely correlated with overall competitive strength.Consequently,China proposed a strategy for the integration of industrialization and informatization,optimizing and adjusting its industrial structure to swiftly achieve transformation and upgrading in the Industry 4.0 era,thereby enhancing the sophistication of intelligent industrial control systems.The distributed control system in a nuclear power plant functions as an industrial control system,overseeing the operational status of the physical process.Its ability to ensure safe and reliable operation is directly linked to nuclear safety and the cybersecurity of the facility.The management of network security in distributed control systems(DCS)is crucial for achieving this objective.Due to the varying network settings and parameters of the DCS implemented in each nuclear power plant,the network security status of the system sometimes diverges from expectations.During system operation,it will undoubtedly encounter network security issues.Consequently,nuclear power plants utilize the technical criteria outlined in GB/T 22239 to formulate a network security management program aimed at enhancing the operational security of DCS within these facilities.This study utilizes existing network security regulations and standards as a reference to analyze the network security control standards based on the nuclear power plant’s control system.It delineates the fundamental requirements for network security management,facilitating integration with the entire life cycle of the research,development,and application of the nuclear power plant’s distributed control system,thereby establishing a network security management methodology that satisfies the control requirements of the nuclear power plant.Initially,it presents DCS and network security management,outlines current domestic and international network security legislation and standards,and specifies the standards pertinent to the administration of DCS in nuclear power plants.Secondly,the design of network security management for DCS is executed in conjunction with the specific context of nuclear power plants.This encompasses the deployment of network security apparatus,validation of the network security management strategy,and optimization adjustments.Consequently,recommendations beneficial to the network security management of nuclear power plants are compiled,aimed at establishing a management system and incorporating the concept of full life cycle management,which is predicated on system requirements,system design,and both software and hardware considerations.Conversely,it presents the notion of comprehensive life cycle management and suggests network security management strategies encompassing system requirements,system architecture,detailed hardware and software design and implementation,procurement,internal system integration,system validation and acceptance testing,system installation,operational maintenance,system modifications,and decommissioning.We will consistently enhance the performance and functionality of DCS in nuclear power plants,establish a safe and secure operational environment,and thereby facilitate the implementation of DCS in nuclear facilities while ensuring robust network security in the future.
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 Research Foundation of Korea(NRF)funded by theMinistry of Science and ICT(2022K1A3A1A61014825)。
文摘With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.
文摘After an introduction to the implementation of supervisory computer control (SCC) through networks and the relevant security issues, this paper centers on the core of network security design: intelligent front-end processor (FEP), encryption/decryption method and authentication protocol. Some other system-specific security measures are also proposed. Although these are examples only, the techniques discussed can also be used in and provide reference for other remote control systems.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2025JBXT010in part by NSFC under Grant No.62171021,in part by the Project of China State Railway Group under Grant No.N2024B004in part by ZTE IndustryUniversityInstitute Cooperation Funds under Grant No.l23L00010.
文摘The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.
文摘Society is becoming increasingly dependent on cyberspace for both business and pleasure. Cyber attackers continue to attack organizational computer networks, as those same computer networks become increasing critical to organizational business process. Strategic planning and managing IT security risks play an important role in the business and government planning process. Deploying defense in depth security measures can ensure that organizations continue to function in times of crisis. This quantitative study explores whether the Latin Square Design (LSD) model can be effectively applied to the prioritization of cybersecurity threats and to the linking of information assurance defense in-depth measures to those threats. The methods used in this study consisted of scanning 10 Cybersecurity Websites such as the Department of Homeland Security US CERT (United States-Computer Emergency Readiness Team [1]) and the SANS Institute (SysAdmin, Audit, Network and Security [2]) using the Likert Scale Model for the Website’s top ten list of cyber threats facing organizations and the network defense in depth measures to fight those threats. A comparison of each cybersecurity threats was then made using LSD to determine whether the Likert scale and the LSD model could be effectively applied to prioritize information assurance measures to protect organizational computing devices. The findings of the research reject the H0 null hypothesis that LSD does not affect the relationship between the ranking of 10 Cybersecurity websites top ten cybersecurity threats dependent variables and the independent variables of defense in depth measures used in protecting organizational devices against cyber-attacks.
基金Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2024R319)funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.
文摘To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.
基金supported by the Technical Project of the State Grid Corporation of China(research and demonstration application of key technology of energy storage cloud for mobile energy storage application of electric vehicles 5419-201971217a-0-0-00)。
文摘As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,providing a smart power grid with increased control,stability,and safety.Based on the secure communication requirements of cloud energy storage systems,this paper presents the design and development of a node controller for a cloud energy storage network.The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system.Safety protection measures were proposed according to the demands of the communication network,allowing the system to run safely and stably.Finally,the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou,China.The system was observed to operate safely and stably,demonstrating good peak-clipping and valley filling effects,and improving the system load characteristics.
文摘This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted?self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.
文摘The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.
文摘Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.
文摘The advent of 6G wireless technology,which offers previously unattainable data rates,very low latency,and compatibility with a wide range of communication devices,promises to transform the networking environment completely.The 6G wireless proposals aim to expand wireless communication’s capabilities well beyond current levels.This technology is expected to revolutionize how we communicate,connect,and use the power of the digital world.However,maintaining secure and efficient data management becomes crucial as 6G networks grow in size and complexity.This study investigates blockchain clustering and artificial intelligence(AI)approaches to ensure a reliable and trustworthy communication in 6G.First,the mechanisms and protocols of blockchain clustering that provide a trusted and effective communication infrastructure for 6G networks are presented.Then,AI techniques for network security in 6G are studied.The integration of AI and blockchain to ensure energy efficiency in 6Gnetworks is addressed.Next,this paper presents howthe 6G’s speed and bandwidth enables AI and the easy management of virtualized systems.Using terahertz connections is sufficient to have virtualized systems move compute environments as well as data.For instance,a computing environment can follow potential security violations while leveraging AI.Such virtual machines can store their findings in blockchains.In 6G scenarios,case studies and real-world applications of AI-powered secure blockchain clustering are given.Moreover,challenges and promising future research opportunities are highlighted.These challenges and opportunities provide insights from the most recent developments and point to areas where AI and blockchain further ensure security and efficiency in 6G networks.
基金This work is funded by the National Natural Science Foundation of China under Grant U1636215the National key research and development plan under Grant Nos.2018YFB0803504,2016YFB0800303.
文摘Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.
文摘The total reliance on internet connectivity and World Wide Web (WWW) based services is forcing many organizations to look for alternative solutions for providing adequate access and response time to the demand of their ever increasing users. A typical solution is to increase the bandwidth;this can be achieved with additional cost, but this solution does not scale nor decrease users perceived response time. Another concern is the security of their network. An alternative scalable solution is to deploy a proxy server to provide adequate access and improve response time as well as provide some level of security for clients using the network. While some studies have reported performance increase due to the use of proxy servers, one study has reported performance decrease due to proxy server. We then conducted a six-month proxy server experiment. During this period, we collected access logs from three different proxy servers and analyzed these logs with Webalizer a web server log file analysis program. After a few years, in September 2010, we collected log files from another proxy server, analyzed the logs using Webalizer and compared our results. The result of the analysis showed that the hit rate of the proxy servers ranged between 21% - 39% and over 70% of web pages were dynamic. Furthermore clients accessing the internet through a proxy server are more secured. We then conclude that although the nature of the web is changing, the proxy server is still capable of improving performance by decreasing response time perceived by web clients and improved network security.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A2A01044485).
文摘In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proof-of-work consensus,is a fundamental measure of preventing various attacks.This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain.We use vector error correction modeling to analyze the endogenous relationships between the hashrate,Bitcoin price,and transaction fee,revealing the need for an additional variable to achieve our aim.Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes.This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
