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
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
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
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
False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unr...False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.展开更多
Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digita...Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digital rightsmanagement.ABE schemes rely on hard cryptographic assumptions such as pairings and others(pairingfree)to ensure their security against external and internal attacks.Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent.One common internal attack is the attribute collusion attack,in which users with different attribute keys collaborate to decrypt data they could not individually access.This paper focuses on the ciphertext-policy ABE(CP-ABE),a type of ABE where ciphertexts are produced with access policies.Our firstwork is to carry out the attribute collusion attack against several existing pairingfree CP-ABE schemes.As a main contribution,we introduce a novel attack,termed the anonymous key-leakage attack,concerning the context in which users could anonymously publish their secret keys associated with certain attributes on public platforms without the risk of detection.This kind of internal attack has not been defined or investigated in the literature.We then show that several prominent pairing-based CP-ABE schemes are vulnerable to this attack.We believe that this work will contribute to helping the community evaluate suitable CP-ABE schemes for secure deployment in real-life applications.展开更多
Among the four candidate algorithms in the fourth round of NIST standardization,the BIKE(Bit Flipping Key Encapsulation)scheme has a small key size and high efficiency,showing good prospects for application.However,th...Among the four candidate algorithms in the fourth round of NIST standardization,the BIKE(Bit Flipping Key Encapsulation)scheme has a small key size and high efficiency,showing good prospects for application.However,the BIKE scheme based on QC-MDPC(Quasi Cyclic Medium Density Parity Check)codes still faces challenges such as the GJS attack and weak key attacks targeting the decoding failure rate(DFR).This paper analyzes the BGF decoding algorithm of the BIKE scheme,revealing two deep factors that lead to DFR,and proposes a weak key optimization attack method for the BGF decoding algorithm based on these two factors.The proposed method constructs a new weak key set,and experiment results eventually indicate that,considering BIKE’s parameter set targeting 128-bit security,the average decryption failure rate is lowerly bounded by.This result not only highlights a significant vulnerability in the BIKE scheme but also provides valuable insights for future improvements in its design.By addressing these weaknesses,the robustness of QC-MDPC code-based cryptographic systems can be enhanced,paving the way for more secure post-quantum cryptographic solutions.展开更多
In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are qu...In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.展开更多
The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists fac...The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.展开更多
Recent research on adversarial attacks has primarily focused on white-box attack techniques,with limited exploration of black-box attack methods.Furthermore,in many black-box research scenarios,it is assumed that the ...Recent research on adversarial attacks has primarily focused on white-box attack techniques,with limited exploration of black-box attack methods.Furthermore,in many black-box research scenarios,it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts.Unfortunately,this disregard for the real-world practicality of attacks,particularly their potential for human detectability,has left a gap in the research landscape.Considering these limitations,our study focuses on using a similar color attack method,assuming access only to the output label,limiting the number of attack attempts to 100,and subjecting the attacks to human perceptibility testing.Through this approach,we demonstrated the effectiveness of black box attack techniques in deceiving models and achieved a success rate of 82.68%in deceiving humans.This study emphasizes the significance of research that addresses the challenge of deceiving both humans and models,highlighting the importance of real-world applicability.展开更多
In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of site...In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system's normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations(called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design Kcorruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.展开更多
Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving dat...Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.展开更多
The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criter...The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an alternative.In this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios.Consequently,an MCDM approach is essential to address these emerging challenges.While the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the ambiguity.The results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of 0.1946.In contrast,the Hop count is identified as the least significant criterion,with a weighted score of 0.1090.The remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring 0.1472.In terms of ranking,SvN-FDOSM has two approaches:individual and group.Both the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under F-DoSA.The sensitivity analysis provides a high stability in ranking among all 10 scenarios.This approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.展开更多
The sinkhole attack is one of the most damaging threats in the Internet of Things(IoT).It deceptively attracts neighboring nodes and initiates malicious activity,often disrupting the network when combined with other a...The sinkhole attack is one of the most damaging threats in the Internet of Things(IoT).It deceptively attracts neighboring nodes and initiates malicious activity,often disrupting the network when combined with other attacks.This study proposes a novel approach,named NADSA,to detect and isolate sinkhole attacks.NADSA is based on the RPL protocol and consists of two detection phases.In the first phase,the minimum possible hop count between the sender and receiver is calculated and compared with the sender’s reported hop count.The second phase utilizes the number of DIO messages to identify suspicious nodes and then applies a fuzzification process using RSSI,ETX,and distance measurements to confirm the presence of a malicious node.The proposed method is extensively simulated in highly lossy and sparse network environments with varying numbers of nodes.The results demonstrate that NADSA achieves high efficiency,with PDRs of 68%,70%,and 73%;E2EDs of 81,72,and 60 ms;TPRs of 89%,83%,and 80%;and FPRs of 24%,28%,and 33%.NADSA outperforms existing methods in challenging network conditions,where traditional approaches typically degrade in effectiveness.展开更多
Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susce...Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.展开更多
Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and so...Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
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.
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.
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 Sichuan Science and Technology Program(2024YFHZ0015)the Key Laboratory of Data Protection and Intelligent Management,Ministry of Education,Sichuan University(SCUSACXYD202401).
文摘False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.
文摘Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digital rightsmanagement.ABE schemes rely on hard cryptographic assumptions such as pairings and others(pairingfree)to ensure their security against external and internal attacks.Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent.One common internal attack is the attribute collusion attack,in which users with different attribute keys collaborate to decrypt data they could not individually access.This paper focuses on the ciphertext-policy ABE(CP-ABE),a type of ABE where ciphertexts are produced with access policies.Our firstwork is to carry out the attribute collusion attack against several existing pairingfree CP-ABE schemes.As a main contribution,we introduce a novel attack,termed the anonymous key-leakage attack,concerning the context in which users could anonymously publish their secret keys associated with certain attributes on public platforms without the risk of detection.This kind of internal attack has not been defined or investigated in the literature.We then show that several prominent pairing-based CP-ABE schemes are vulnerable to this attack.We believe that this work will contribute to helping the community evaluate suitable CP-ABE schemes for secure deployment in real-life applications.
基金funded by Beijing Institute of Electronic Science and Technology Postgraduate Excellence Demonstration Course Project(20230002Z0452).
文摘Among the four candidate algorithms in the fourth round of NIST standardization,the BIKE(Bit Flipping Key Encapsulation)scheme has a small key size and high efficiency,showing good prospects for application.However,the BIKE scheme based on QC-MDPC(Quasi Cyclic Medium Density Parity Check)codes still faces challenges such as the GJS attack and weak key attacks targeting the decoding failure rate(DFR).This paper analyzes the BGF decoding algorithm of the BIKE scheme,revealing two deep factors that lead to DFR,and proposes a weak key optimization attack method for the BGF decoding algorithm based on these two factors.The proposed method constructs a new weak key set,and experiment results eventually indicate that,considering BIKE’s parameter set targeting 128-bit security,the average decryption failure rate is lowerly bounded by.This result not only highlights a significant vulnerability in the BIKE scheme but also provides valuable insights for future improvements in its design.By addressing these weaknesses,the robustness of QC-MDPC code-based cryptographic systems can be enhanced,paving the way for more secure post-quantum cryptographic solutions.
基金supported in part by the National Natural Science Foundation of China(61933007,62273087,62273088,U21A2019)the Shanghai Pujiang Program of China(22PJ1400400)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Royal Society of U.K.the Alexander von Humboldt Foundation of Germany
文摘In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.
基金supported by NSTC 113-2221-E-155-055NSTC 113-2222-E-155-007,Taiwan.
文摘The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.
基金supported by the Research Resurgence under the Glocal University 30 Project at Gyeongsang National University in 2024.
文摘Recent research on adversarial attacks has primarily focused on white-box attack techniques,with limited exploration of black-box attack methods.Furthermore,in many black-box research scenarios,it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts.Unfortunately,this disregard for the real-world practicality of attacks,particularly their potential for human detectability,has left a gap in the research landscape.Considering these limitations,our study focuses on using a similar color attack method,assuming access only to the output label,limiting the number of attack attempts to 100,and subjecting the attacks to human perceptibility testing.Through this approach,we demonstrated the effectiveness of black box attack techniques in deceiving models and achieved a success rate of 82.68%in deceiving humans.This study emphasizes the significance of research that addresses the challenge of deceiving both humans and models,highlighting the importance of real-world applicability.
基金supported in part by the IN2CCAM project that has received funding from the European Union's Horizon Europe research and innovation programme(101076791)the National Natural Science Foundation of China(62403378)the Natural Science Basic Research Program of Shaanxi Province(2024JC-YBQN-0669)
文摘In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system's normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations(called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design Kcorruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.
文摘Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.
文摘The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an alternative.In this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios.Consequently,an MCDM approach is essential to address these emerging challenges.While the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the ambiguity.The results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of 0.1946.In contrast,the Hop count is identified as the least significant criterion,with a weighted score of 0.1090.The remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring 0.1472.In terms of ranking,SvN-FDOSM has two approaches:individual and group.Both the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under F-DoSA.The sensitivity analysis provides a high stability in ranking among all 10 scenarios.This approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.
文摘The sinkhole attack is one of the most damaging threats in the Internet of Things(IoT).It deceptively attracts neighboring nodes and initiates malicious activity,often disrupting the network when combined with other attacks.This study proposes a novel approach,named NADSA,to detect and isolate sinkhole attacks.NADSA is based on the RPL protocol and consists of two detection phases.In the first phase,the minimum possible hop count between the sender and receiver is calculated and compared with the sender’s reported hop count.The second phase utilizes the number of DIO messages to identify suspicious nodes and then applies a fuzzification process using RSSI,ETX,and distance measurements to confirm the presence of a malicious node.The proposed method is extensively simulated in highly lossy and sparse network environments with varying numbers of nodes.The results demonstrate that NADSA achieves high efficiency,with PDRs of 68%,70%,and 73%;E2EDs of 81,72,and 60 ms;TPRs of 89%,83%,and 80%;and FPRs of 24%,28%,and 33%.NADSA outperforms existing methods in challenging network conditions,where traditional approaches typically degrade in effectiveness.
基金supported in part by the National Natural Science Foundation of China under Grants No.62372087 and No.62072076the Research Fund of State Key Laboratory of Processors under Grant No.CLQ202310the CSC scholarship.
文摘Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.
基金supported by the National Natural Science Foundation of China(Nos.62176122 and 62061146002).
文摘Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
