The ability of technology to profoundly affect our lives is exem- plified by the digital transformation that is occurring in many aspects of our lives and being played out in the virtual world of cyberspace. Cyberspac...The ability of technology to profoundly affect our lives is exem- plified by the digital transformation that is occurring in many aspects of our lives and being played out in the virtual world of cyberspace. Cyberspace provides unparalleled connectivity and glo- bal reach, and is central to societal and economic well-being.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulner...Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap betwee...The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.展开更多
Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monit...Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.展开更多
As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds...As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.展开更多
The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver ...The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver operational improvements, the heightened interconnectivity introduces significant cybersecurity challenges, particularly within military logistics, where mission-critical operations and life-safety concerns are paramount. This paper examines these unique cybersecurity requirements, focusing on advanced persistent threats, supply chain poisoning, and data breaches that could compromise sensitive operations. The study proposes a hybrid cybersecurity framework tailored to military logistics, integrating resilience, redundancy, and cross-jurisdictional security measures. Real-world applicability is validated through simulations, offering strategies for securing supply chains while balancing security, efficiency, and flexibility.展开更多
This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 emp...This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.展开更多
The digital transformation in Cameroon presents critical cybersecurity challenges that demand immediate attention and strategic intervention. This comprehensive analysis examines the evolving cybersecurity landscape i...The digital transformation in Cameroon presents critical cybersecurity challenges that demand immediate attention and strategic intervention. This comprehensive analysis examines the evolving cybersecurity landscape in Cameroon from 2020 to 2023, during which cyber-attacks increased by 156% and financial losses from digital fraud exceeded $45 million. This research identifies significant vulnerabilities in Cameroon’s cybersecurity ecosystem through a rigorous assessment of national infrastructure, policy frameworks, and institutional capacities. Recent data indicates that while digital service adoption has grown exponentially, with internet penetration reaching 35.2% in 2023, cybersecurity measures have lagged significantly behind international standards. This analysis draws on comprehensive data from multiple sectors, including financial services, government institutions, and telecommunications, incorporating findings from the National Cybersecurity Assessment Program and the Digital Infrastructure Security Report. The research reveals that 73% of organizations lack dedicated security teams, while response times to cyber incidents average 72 hours—three times than the global standard. Based on these findings, this paper proposes evidence-based solutions for enhancing digital resilience, including policy modernization, capacity-building initiatives, and technical infrastructure development. The recommendations encompass short-term tactical responses, medium-term strategic improvements, and long-term structural changes, providing a comprehensive roadmap for strengthening Cameroon’s national cybersecurity frameworks.展开更多
The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber ris...The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber risks. Although comprehensive, the complexity of the NIST CSF can be overwhelming, especially for those lacking extensive cybersecurity resources. Current implementation tools often cater to larger companies, neglecting the specific needs of SMEs, which can be vulnerable to cyber threats. To address this gap, our research proposes a user-friendly, open-source web platform designed to simplify the implementation of the NIST CSF. This platform enables organizations to assess their risk exposure and continuously monitor their cybersecurity maturity through tailored recommendations based on their unique profiles. Our methodology includes a literature review of existing tools and standards, followed by a description of the platform’s design and architecture. Initial tests with SMEs in Burkina Faso reveal a concerning cybersecurity maturity level, indicating the urgent need for improved strategies based on our findings. By offering an intuitive interface and cross-platform accessibility, this solution aims to empower organizations to enhance their cybersecurity resilience in an evolving threat landscape. The article concludes with discussions on the practical implications and future enhancements of the tool.展开更多
Small and Medium-sized Enterprises (SMEs) are considered the backbone of global economy, but they often face cyberthreats which threaten their financial stability and operational continuity. This work aims to offer a ...Small and Medium-sized Enterprises (SMEs) are considered the backbone of global economy, but they often face cyberthreats which threaten their financial stability and operational continuity. This work aims to offer a proactive cybersecurity approach to safeguard SMEs against these threats. Furthermore, to mitigate these risks, we propose a comprehensive framework of practical and scalable cybersecurity measurements/protocols specifically for SMEs. These measures encompass a spectrum of solutions, from technological fortifications to employee training initiatives and regulatory compliance strategies, in an effort to cultivate resilience and awareness among SMEs. Additionally, we introduce a specially designed a Java-based questionnaire software tool in order to provide an initial framework for essential cybersecurity measures and evaluation for SMEs. This tool covers crucial topics such as social engineering and phishing attempts, implementing antimalware and ransomware defense mechanisms, secure data management and backup strategies and methods for preventing insider threats. By incorporating globally recognized frameworks and standards like ISO/IEC 27001 and NIST guidelines, this questionnaire offers a roadmap for establishing and enhancing cybersecurity measures.展开更多
The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making...The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.展开更多
This paper investigates the integration of ideological and political education within cybersecurity postgraduate curricula.Through systematic analysis of pedagogical challenges and the integrated application of big da...This paper investigates the integration of ideological and political education within cybersecurity postgraduate curricula.Through systematic analysis of pedagogical challenges and the integrated application of big data analytics,AI-enhanced pedagogy,and data mining methodologies,we propose an innovative framework to synergize technical training with ethical cultivation.This approach aims to develop high-quality cybersecurity professionals equipped with moral integrity,technical expertise,and strategic compliance awareness,thereby advancing the cybersecurity industry’s sustainable development.展开更多
Taking the cooperation between China and Pakistan as an example,this paper expounds on the current situation,governance concept,obstacles to cooperation,and differentiated policies of Western countries in the areas of...Taking the cooperation between China and Pakistan as an example,this paper expounds on the current situation,governance concept,obstacles to cooperation,and differentiated policies of Western countries in the areas of cybersecurity,the role of new e-commerce platforms,and digital sovereignty of BRICS countries.It aims to promote inter-governmental cooperation through civil dialogue and lead information technology cooperation among developing countries through the BRICS mechanism,as well as to collaborate to establish guidelines for global cybersecurity,new e-commerce platforms,and digital sovereignty.展开更多
The European Standardization Organizations(ESOs),CEN,CENELEC and ETSI,joined forces with ENISA,the EU Agency for Cybersecurity,to host the 9th Cybersecurity Standardization Conference on March 20 in Brussels,Belgium.
Strengthening cybersecurity education for college students holds significant importance in achieving the strategic goal of building China into a cyber power.This article begins by discussing the significance and neces...Strengthening cybersecurity education for college students holds significant importance in achieving the strategic goal of building China into a cyber power.This article begins by discussing the significance and necessity of implementing cybersecurity education for university students.Drawing on disciplinary characteristics and student learning analysis,it presents a comprehensive construction process and countermeasures for a general cybersecurity education course,covering aspects such as teaching content development,teaching resource creation,and pedagogical approaches.The aim is to provide reference and guidance for other universities in developing general cybersecurity education courses.展开更多
This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems,integrating immutable machine learning(ML)with distributed ledger technology.Our contribution focused on three...This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems,integrating immutable machine learning(ML)with distributed ledger technology.Our contribution focused on three factors,Quantum-resistant feature engineering using theUNSW-NB15 dataset adapted for solar infrastructure anomalies.An enhanced Light Gradient Boosting Machine(LightGBM)classifier with blockchain-validated decision thresholds,and A cryptographic proof-of-threat(PoT)consensus mechanism for cyber attack verification.The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision,recall and F1-score,outperforming conventional intrusion detection systems(IDSs)by 12.7% in false positive reduction.The blockchain layer demonstrates a 2.4-s average block confirmation time with 256-bit SHA-3 hashing,enabling real-time threat logging in photovoltaic networks.Experimental results improve in attack traceability compared to centralized security systems,establishing new benchmarks for trustworthy anomaly detection in smart grid infrastructures.This study also compared traditional and hybrid ML based blockchian driven IDSs and attained better classification results.The proposed framework not only delivers a resilient,adaptable threat mitigation system(TMS)for Industry 4.0 solar powered infrastructure but also attains high explainability,scalability with tamper-proof logs,and remarkably exceptional ability of endurance to cyber attacks.展开更多
With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it ...With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it also poses significant cybersecurity challenges to the financial sector. This study comprehensively analyzes the current state, challenges, and protective measures of cybersecurity in the financial sector, aiming to provide important references for financial institutions in formulating cybersecurity strategies and enhancing risk management.展开更多
The proliferation of smart communities in Foshan has led to increasingly diverse and prevalent cybersecurity risks for residents.This trend has rendered traditional cybersecurity education models inadequate in address...The proliferation of smart communities in Foshan has led to increasingly diverse and prevalent cybersecurity risks for residents.This trend has rendered traditional cybersecurity education models inadequate in addressing the challenges of the digital era.Guided by the theory of collaborative governance and the framework of digital transformation,this paper examines the multi-stakeholder collaborative mechanism involving the government,businesses,community organizations,universities,and residents.It subsequently proposes a series of implementation strategies such as digitizing educational content,intellectualizing platforms,contextualizing delivery methods,and refining management precision.Studies demonstrate that this model enables effective resource integration,improves educational precision,and boosts resident engagement.It represents a fundamental shift from unilateral dissemination to multi-party interaction and from decentralized management to collaborative synergy,offering a replicable“Foshan Model”for digital governance at the community level.展开更多
文摘The ability of technology to profoundly affect our lives is exem- plified by the digital transformation that is occurring in many aspects of our lives and being played out in the virtual world of cyberspace. Cyberspace provides unparalleled connectivity and glo- bal reach, and is central to societal and economic well-being.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
文摘Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
文摘The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University.
文摘Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by the Korea Internet&Security Agency(KISA)through the Information Security Specialized University Support Project(50%).
文摘As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.
文摘The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver operational improvements, the heightened interconnectivity introduces significant cybersecurity challenges, particularly within military logistics, where mission-critical operations and life-safety concerns are paramount. This paper examines these unique cybersecurity requirements, focusing on advanced persistent threats, supply chain poisoning, and data breaches that could compromise sensitive operations. The study proposes a hybrid cybersecurity framework tailored to military logistics, integrating resilience, redundancy, and cross-jurisdictional security measures. Real-world applicability is validated through simulations, offering strategies for securing supply chains while balancing security, efficiency, and flexibility.
文摘This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.
文摘The digital transformation in Cameroon presents critical cybersecurity challenges that demand immediate attention and strategic intervention. This comprehensive analysis examines the evolving cybersecurity landscape in Cameroon from 2020 to 2023, during which cyber-attacks increased by 156% and financial losses from digital fraud exceeded $45 million. This research identifies significant vulnerabilities in Cameroon’s cybersecurity ecosystem through a rigorous assessment of national infrastructure, policy frameworks, and institutional capacities. Recent data indicates that while digital service adoption has grown exponentially, with internet penetration reaching 35.2% in 2023, cybersecurity measures have lagged significantly behind international standards. This analysis draws on comprehensive data from multiple sectors, including financial services, government institutions, and telecommunications, incorporating findings from the National Cybersecurity Assessment Program and the Digital Infrastructure Security Report. The research reveals that 73% of organizations lack dedicated security teams, while response times to cyber incidents average 72 hours—three times than the global standard. Based on these findings, this paper proposes evidence-based solutions for enhancing digital resilience, including policy modernization, capacity-building initiatives, and technical infrastructure development. The recommendations encompass short-term tactical responses, medium-term strategic improvements, and long-term structural changes, providing a comprehensive roadmap for strengthening Cameroon’s national cybersecurity frameworks.
文摘The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber risks. Although comprehensive, the complexity of the NIST CSF can be overwhelming, especially for those lacking extensive cybersecurity resources. Current implementation tools often cater to larger companies, neglecting the specific needs of SMEs, which can be vulnerable to cyber threats. To address this gap, our research proposes a user-friendly, open-source web platform designed to simplify the implementation of the NIST CSF. This platform enables organizations to assess their risk exposure and continuously monitor their cybersecurity maturity through tailored recommendations based on their unique profiles. Our methodology includes a literature review of existing tools and standards, followed by a description of the platform’s design and architecture. Initial tests with SMEs in Burkina Faso reveal a concerning cybersecurity maturity level, indicating the urgent need for improved strategies based on our findings. By offering an intuitive interface and cross-platform accessibility, this solution aims to empower organizations to enhance their cybersecurity resilience in an evolving threat landscape. The article concludes with discussions on the practical implications and future enhancements of the tool.
文摘Small and Medium-sized Enterprises (SMEs) are considered the backbone of global economy, but they often face cyberthreats which threaten their financial stability and operational continuity. This work aims to offer a proactive cybersecurity approach to safeguard SMEs against these threats. Furthermore, to mitigate these risks, we propose a comprehensive framework of practical and scalable cybersecurity measurements/protocols specifically for SMEs. These measures encompass a spectrum of solutions, from technological fortifications to employee training initiatives and regulatory compliance strategies, in an effort to cultivate resilience and awareness among SMEs. Additionally, we introduce a specially designed a Java-based questionnaire software tool in order to provide an initial framework for essential cybersecurity measures and evaluation for SMEs. This tool covers crucial topics such as social engineering and phishing attempts, implementing antimalware and ransomware defense mechanisms, secure data management and backup strategies and methods for preventing insider threats. By incorporating globally recognized frameworks and standards like ISO/IEC 27001 and NIST guidelines, this questionnaire offers a roadmap for establishing and enhancing cybersecurity measures.
文摘The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.
文摘This paper investigates the integration of ideological and political education within cybersecurity postgraduate curricula.Through systematic analysis of pedagogical challenges and the integrated application of big data analytics,AI-enhanced pedagogy,and data mining methodologies,we propose an innovative framework to synergize technical training with ethical cultivation.This approach aims to develop high-quality cybersecurity professionals equipped with moral integrity,technical expertise,and strategic compliance awareness,thereby advancing the cybersecurity industry’s sustainable development.
文摘Taking the cooperation between China and Pakistan as an example,this paper expounds on the current situation,governance concept,obstacles to cooperation,and differentiated policies of Western countries in the areas of cybersecurity,the role of new e-commerce platforms,and digital sovereignty of BRICS countries.It aims to promote inter-governmental cooperation through civil dialogue and lead information technology cooperation among developing countries through the BRICS mechanism,as well as to collaborate to establish guidelines for global cybersecurity,new e-commerce platforms,and digital sovereignty.
文摘The European Standardization Organizations(ESOs),CEN,CENELEC and ETSI,joined forces with ENISA,the EU Agency for Cybersecurity,to host the 9th Cybersecurity Standardization Conference on March 20 in Brussels,Belgium.
基金supported in part by the 2024 Core General Education Course Construction Project of Beijing Union University,titled“Cybersecurity:Exploring the World of White Hat Hackers”the 2025 Educational Science Research Project of Beijing Union University(JK202514)+1 种基金the General Project of Science and Technology Program of Beijing Municipal Education Commission under Grant KM201911417011the Academic Research Projects of Beijing Union University(ZK30202407).
文摘Strengthening cybersecurity education for college students holds significant importance in achieving the strategic goal of building China into a cyber power.This article begins by discussing the significance and necessity of implementing cybersecurity education for university students.Drawing on disciplinary characteristics and student learning analysis,it presents a comprehensive construction process and countermeasures for a general cybersecurity education course,covering aspects such as teaching content development,teaching resource creation,and pedagogical approaches.The aim is to provide reference and guidance for other universities in developing general cybersecurity education courses.
文摘This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems,integrating immutable machine learning(ML)with distributed ledger technology.Our contribution focused on three factors,Quantum-resistant feature engineering using theUNSW-NB15 dataset adapted for solar infrastructure anomalies.An enhanced Light Gradient Boosting Machine(LightGBM)classifier with blockchain-validated decision thresholds,and A cryptographic proof-of-threat(PoT)consensus mechanism for cyber attack verification.The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision,recall and F1-score,outperforming conventional intrusion detection systems(IDSs)by 12.7% in false positive reduction.The blockchain layer demonstrates a 2.4-s average block confirmation time with 256-bit SHA-3 hashing,enabling real-time threat logging in photovoltaic networks.Experimental results improve in attack traceability compared to centralized security systems,establishing new benchmarks for trustworthy anomaly detection in smart grid infrastructures.This study also compared traditional and hybrid ML based blockchian driven IDSs and attained better classification results.The proposed framework not only delivers a resilient,adaptable threat mitigation system(TMS)for Industry 4.0 solar powered infrastructure but also attains high explainability,scalability with tamper-proof logs,and remarkably exceptional ability of endurance to cyber attacks.
文摘With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it also poses significant cybersecurity challenges to the financial sector. This study comprehensively analyzes the current state, challenges, and protective measures of cybersecurity in the financial sector, aiming to provide important references for financial institutions in formulating cybersecurity strategies and enhancing risk management.
基金2025 Foshan Social Science Planning Project,“Research on Pathways for Enhancing Cybersecurity Awareness Among Foshan Community Residents Empowered by Digital and Intelligent Technologies”(Project No.:2025-GJ091)。
文摘The proliferation of smart communities in Foshan has led to increasingly diverse and prevalent cybersecurity risks for residents.This trend has rendered traditional cybersecurity education models inadequate in addressing the challenges of the digital era.Guided by the theory of collaborative governance and the framework of digital transformation,this paper examines the multi-stakeholder collaborative mechanism involving the government,businesses,community organizations,universities,and residents.It subsequently proposes a series of implementation strategies such as digitizing educational content,intellectualizing platforms,contextualizing delivery methods,and refining management precision.Studies demonstrate that this model enables effective resource integration,improves educational precision,and boosts resident engagement.It represents a fundamental shift from unilateral dissemination to multi-party interaction and from decentralized management to collaborative synergy,offering a replicable“Foshan Model”for digital governance at the community level.
