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
Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the correspond...Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.展开更多
Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attracti...Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attractive victims for themalicious actions of adversaries.Within the spectrumof existing threats,Side-ChannelAttacks(SCAs)have established themselves as an effective way to compromise cryptographic implementations.These attacks exploit unintended,unintended physical leakage that occurs during the cryptographic execution of devices,bypassing the theoretical strength of the crypto design.In recent times,the advancement of deep learning has provided SCAs with a powerful ally.Well-trained deep-learningmodels demonstrate an exceptional capacity to identify correlations between side-channel measurements and sensitive data,thereby significantly enhancing such attacks.To further understand the security threats posed by deep-learning SCAs and to aid in formulating robust countermeasures in the future,this paper undertakes an exhaustive investigation of leading-edge SCAs targeting Advanced Encryption Standard(AES)implementations.The study specifically focuses on attacks that exploit power consumption and electromagnetic(EM)emissions as primary leakage sources,systematically evaluating the extent to which diverse deep learning techniques enhance SCAs acrossmultiple critical dimensions.These dimensions include:(i)the characteristics of publicly available datasets derived from various hardware and software platforms;(ii)the formalization of leakage models tailored to different attack scenarios;(iii)the architectural suitability and performance of state-of-the-art deep learning models.Furthermore,the survey provides a systematic synthesis of current research findings,identifies significant unresolved issues in the existing literature and suggests promising directions for future work,including cross-device attack transferability and the impact of quantum-classical hybrid computing on side-channel security.展开更多
Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that man...Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication r...In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication resources more effectively,a novel adaptive event-triggered(AET) mechanism is introduced,whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states.Differing from existing event-triggered(ET) mechanisms,the proposed one demonstrates exceptional relevance and flexibility.It is closely related to attack probability,and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack.To leverage attacker information more effectively,a switching-like sliding mode security controller is designed,which can autonomously select different controller gains based on the sliding function representing the attack situation.Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface.Compared with existing time-invariant control strategies within the triggered interval,more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy.Finally,a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.展开更多
An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many ...An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many existing distributed NE seeking works,it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks.An attack-resilient NE seeking algorithm that is distributed(i.e.,independent of global information on the graph's algebraic connectivity,Lipschitz and monotone constants of pseudo-gradients,or number of players),is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically.Another key characteristic is that FDI attacks are allowed to be unknown and unbounded.By exploiting nonsmooth analysis and stability theory,the global asymptotic convergence of the developed algorithm to the NE is ensured.Moreover,we extend this design to further consider the attack-resilient NE seeking of double-integrator players.Lastly,numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resi...Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.展开更多
Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective ...Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)cryptanalysis.Specifically,we present an automated search for the DL distinguishers of Ballet based on MILP/MIQCP.For the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest distinguishers.In addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL distinguishers.As a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/256.The data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.展开更多
Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de...Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.展开更多
Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networ...Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.展开更多
This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional m...This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.展开更多
This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments,built upon a novel,syntheticmulti-vector dataset g...This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments,built upon a novel,syntheticmulti-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling.The proposed model is based on the XGBoost algorithm,optimized with Principal Component Analysis for dimensionality reduction,utilizing lightweight flowlevel features extracted from Open Flow statistics to classify attacks across critical IoT protocols including TCP,UDP,HTTP,MQTT,and CoAP.The model employs lightweight flow-level features extracted from Open Flow statistics to ensure low computational overhead and fast processing.Performance was rigorously evaluated using key metrics,including Accuracy,Precision,Recall,F1-Score,False Alarm Rate,AUC-ROC,and Detection Time.Experimental results demonstrate the model’s high performance,achieving an accuracy of 98.93%and a low FAR of 0.86%,with a rapid median detection time of 1.02 s.This efficiency validates its superiority in meeting critical Key Performance Indicators,such as Latency and high Throughput,necessary for time-sensitive SDN-IoT systems.Furthermore,the model’s robustness and statistically significant outperformance against baseline models such as Random Forest,k-Nearest Neighbors,and Gradient Boosting Machine,validating through statistical tests using Wilcoxon signed-rank test and confirmed via successful deployment in a real SDN testbed for live traffic detection and mitigation.展开更多
A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effect...A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effective solutions.The primary challenge lies in selecting the best among several DDoS detection models.This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making(MCDM)techniques to compare and select the most effective models.The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion(FWZIC)and MultiAttribute Boundary Approximation Area Comparison(MABAC)methodologies.FWZIC assigns weights to evaluate criteria,while MABAC compares detection models based on the assessed criteria.The results indicate that the FWZIC approach assigns weights to criteria reliably,with time complexity receiving the highest weight(0.2585)and F1 score receiving the lowest weight(0.14644).Among the models evaluated using the MABAC approach,the Support Vector Machine(SVM)ranked first with a score of 0.0444,making it the most suitable for this work.In contrast,Naive Bayes(NB)ranked lowest with a score of 0.0018.Objective validation and sensitivity analysis proved the reliability of the framework.This study provides a practical approach and insights for cybersecurity practitioners and researchers to evaluate DDoS detection models.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This surve...The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.展开更多
Vehicular Ad Hoc Networks(VANETs)are central to Intelligent Transportation Systems(ITS),especially for real-time communication involving emergency vehicles.Yet,Distributed Denial of Service(DDoS)attacks can disrupt sa...Vehicular Ad Hoc Networks(VANETs)are central to Intelligent Transportation Systems(ITS),especially for real-time communication involving emergency vehicles.Yet,Distributed Denial of Service(DDoS)attacks can disrupt safety-critical channels and undermine reliability.This paper presents a robust,scalable framework for detecting DDoS attacks in highway VANETs.We construct a new dataset with Network Simulator 3(NS-3)and Simulation of Urban Mobility(SUMO),enriched with real mobility traces from Germany’s A81 highway(OpenStreetMap).Three traffic classes are modeled:DDoS,Voice over IP(VoIP),and Transmission Control Protocol Based(TCP-based)video streaming(VideoTCP).The pipeline includes normalization,feature selection with SHapley Additive exPlanations(SHAP),and class balancing via Synthetic Minority Over-sampling Technique(SMOTE).Eleven classifiers are benchmarked—including eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),Adaptive Boosting(AdaBoost),Gradient Boosting(GB),and an Artificial Neural Network(ANN)—using stratified 5-fold cross-validation.XGBoost,GB,CatBoost and ANN achieve the highest performance(weighted F1-score=97%).To assess robustness under non-ideal conditions,we introduce an adversarial evaluation with packet-loss and traffic-jitter(small-sample deformation);the top models retain strong performance,supporting real-time applicability.Collectively,these results demonstrate that the proposed highway-focused framework is accurate,resilient,and well-suited for deployment in VANET security for emergency communications.展开更多
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supported by the National Natural Science Foundation of China(62303353,62273030,62573320)。
文摘Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.
基金The Key R&D Program of Hunan Province(Grant No.2025AQ2024)of the Department of Science and Technology of Hunan Province.Distinguished Young Scientists Fund(Grant No.24B0446)of Hunan Education Department.
文摘Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attractive victims for themalicious actions of adversaries.Within the spectrumof existing threats,Side-ChannelAttacks(SCAs)have established themselves as an effective way to compromise cryptographic implementations.These attacks exploit unintended,unintended physical leakage that occurs during the cryptographic execution of devices,bypassing the theoretical strength of the crypto design.In recent times,the advancement of deep learning has provided SCAs with a powerful ally.Well-trained deep-learningmodels demonstrate an exceptional capacity to identify correlations between side-channel measurements and sensitive data,thereby significantly enhancing such attacks.To further understand the security threats posed by deep-learning SCAs and to aid in formulating robust countermeasures in the future,this paper undertakes an exhaustive investigation of leading-edge SCAs targeting Advanced Encryption Standard(AES)implementations.The study specifically focuses on attacks that exploit power consumption and electromagnetic(EM)emissions as primary leakage sources,systematically evaluating the extent to which diverse deep learning techniques enhance SCAs acrossmultiple critical dimensions.These dimensions include:(i)the characteristics of publicly available datasets derived from various hardware and software platforms;(ii)the formalization of leakage models tailored to different attack scenarios;(iii)the architectural suitability and performance of state-of-the-art deep learning models.Furthermore,the survey provides a systematic synthesis of current research findings,identifies significant unresolved issues in the existing literature and suggests promising directions for future work,including cross-device attack transferability and the impact of quantum-classical hybrid computing on side-channel security.
基金supported by 2023 Higher Education Scientific Research Planning Project of China Society of Higher Education(No.23PG0408)2023 Philosophy and Social Science Research Programs in Jiangsu Province(No.2023SJSZ0993)+2 种基金Nantong Science and Technology Project(No.JC2023070)Key Project of Jiangsu Province Education Science 14th Five-Year Plan(Grant No.B-b/2024/02/41)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202407).
文摘Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金supported in part by Shanghai Natural Science Foundation(24ZR1454700)the National Natural Science Foundation of China(62503331,62533016,62573279,62173231,62203288)Shanghai Pujiang Program(23PJD033)。
文摘In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication resources more effectively,a novel adaptive event-triggered(AET) mechanism is introduced,whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states.Differing from existing event-triggered(ET) mechanisms,the proposed one demonstrates exceptional relevance and flexibility.It is closely related to attack probability,and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack.To leverage attacker information more effectively,a switching-like sliding mode security controller is designed,which can autonomously select different controller gains based on the sliding function representing the attack situation.Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface.Compared with existing time-invariant control strategies within the triggered interval,more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy.Finally,a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.
基金supported in part by the National Natural Science Foundation of China(62373022,U2241217,62141604)Beijing Natural Science Foundation(4252043,JQ23019)+4 种基金the Fundamental Research Funds for the Central Universities(JKF-2025037448805,JKF-2025086098295)the Aeronautical Science Fund(2023Z034051001)the Academic Excellence Foundation of BUAA for Ph.D. Studentsthe Science and Technology Innovation2030—Key Project of New Generation Artificial Intelligence(2020AAA0108200)the National Key Research and Development Program of China(2022YFB3305600)。
文摘An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many existing distributed NE seeking works,it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks.An attack-resilient NE seeking algorithm that is distributed(i.e.,independent of global information on the graph's algebraic connectivity,Lipschitz and monotone constants of pseudo-gradients,or number of players),is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically.Another key characteristic is that FDI attacks are allowed to be unknown and unbounded.By exploiting nonsmooth analysis and stability theory,the global asymptotic convergence of the developed algorithm to the NE is ensured.Moreover,we extend this design to further consider the attack-resilient NE seeking of double-integrator players.Lastly,numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
文摘Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.
基金National Natural Science Foundation of China(62272147,12471492,62072161,12401687)Shandong Provincial Natural Science Foundation(ZR2024QA205)+1 种基金Science and Technology on Communication Security Laboratory Foundation(6142103012207)Innovation Group Project of the Natural Science Foundation of Hubei Province of China(2023AFA021)。
文摘Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)cryptanalysis.Specifically,we present an automated search for the DL distinguishers of Ballet based on MILP/MIQCP.For the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest distinguishers.In addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL distinguishers.As a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/256.The data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.
基金supported in part by the National Science Foundation of China(62373240,62273224,U24A20259).
文摘Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.
基金supported by the National Natural Science Foundation of China(61433003,60904003,11602019).
文摘Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.
基金supported in part by Shanghai Rising-Star Program,China under grant 22QA1409400in part by National Natural Science Foundation of China under grant 62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under grant 2021SHZDZX0100.
文摘This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.
文摘This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments,built upon a novel,syntheticmulti-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling.The proposed model is based on the XGBoost algorithm,optimized with Principal Component Analysis for dimensionality reduction,utilizing lightweight flowlevel features extracted from Open Flow statistics to classify attacks across critical IoT protocols including TCP,UDP,HTTP,MQTT,and CoAP.The model employs lightweight flow-level features extracted from Open Flow statistics to ensure low computational overhead and fast processing.Performance was rigorously evaluated using key metrics,including Accuracy,Precision,Recall,F1-Score,False Alarm Rate,AUC-ROC,and Detection Time.Experimental results demonstrate the model’s high performance,achieving an accuracy of 98.93%and a low FAR of 0.86%,with a rapid median detection time of 1.02 s.This efficiency validates its superiority in meeting critical Key Performance Indicators,such as Latency and high Throughput,necessary for time-sensitive SDN-IoT systems.Furthermore,the model’s robustness and statistically significant outperformance against baseline models such as Random Forest,k-Nearest Neighbors,and Gradient Boosting Machine,validating through statistical tests using Wilcoxon signed-rank test and confirmed via successful deployment in a real SDN testbed for live traffic detection and mitigation.
文摘A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effective solutions.The primary challenge lies in selecting the best among several DDoS detection models.This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making(MCDM)techniques to compare and select the most effective models.The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion(FWZIC)and MultiAttribute Boundary Approximation Area Comparison(MABAC)methodologies.FWZIC assigns weights to evaluate criteria,while MABAC compares detection models based on the assessed criteria.The results indicate that the FWZIC approach assigns weights to criteria reliably,with time complexity receiving the highest weight(0.2585)and F1 score receiving the lowest weight(0.14644).Among the models evaluated using the MABAC approach,the Support Vector Machine(SVM)ranked first with a score of 0.0444,making it the most suitable for this work.In contrast,Naive Bayes(NB)ranked lowest with a score of 0.0018.Objective validation and sensitivity analysis proved the reliability of the framework.This study provides a practical approach and insights for cybersecurity practitioners and researchers to evaluate DDoS detection models.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
文摘The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.
文摘Vehicular Ad Hoc Networks(VANETs)are central to Intelligent Transportation Systems(ITS),especially for real-time communication involving emergency vehicles.Yet,Distributed Denial of Service(DDoS)attacks can disrupt safety-critical channels and undermine reliability.This paper presents a robust,scalable framework for detecting DDoS attacks in highway VANETs.We construct a new dataset with Network Simulator 3(NS-3)and Simulation of Urban Mobility(SUMO),enriched with real mobility traces from Germany’s A81 highway(OpenStreetMap).Three traffic classes are modeled:DDoS,Voice over IP(VoIP),and Transmission Control Protocol Based(TCP-based)video streaming(VideoTCP).The pipeline includes normalization,feature selection with SHapley Additive exPlanations(SHAP),and class balancing via Synthetic Minority Over-sampling Technique(SMOTE).Eleven classifiers are benchmarked—including eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),Adaptive Boosting(AdaBoost),Gradient Boosting(GB),and an Artificial Neural Network(ANN)—using stratified 5-fold cross-validation.XGBoost,GB,CatBoost and ANN achieve the highest performance(weighted F1-score=97%).To assess robustness under non-ideal conditions,we introduce an adversarial evaluation with packet-loss and traffic-jitter(small-sample deformation);the top models retain strong performance,supporting real-time applicability.Collectively,these results demonstrate that the proposed highway-focused framework is accurate,resilient,and well-suited for deployment in VANET security for emergency communications.
