This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,th...This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.展开更多
ARIA is a new block cipher designed as the block cipher standard of South Korea. The current version is 1.0, which is an improvement of version 0.8 with the security using four kinds of S-boxes instead of two and an a...ARIA is a new block cipher designed as the block cipher standard of South Korea. The current version is 1.0, which is an improvement of version 0.8 with the security using four kinds of S-boxes instead of two and an additional two rounds of encryptions. These improvements are designed to prevent the dedicated linear attack on ARIA version 0.8 by the four different kinds of S-boxes. This paper presents 12 linear approximations of a single round function that succeeds in attacking ARIA version 1.0 on 7, 9, or 9 rounds for key sizes of 128, 192, or 256 bits using any of these approximations. The corresponding data complexities are 2^87, 2^119, and 2^119, the counting complexities are 1.5×2^88, 2^119, and 2^119, the memory required for each attack on all three key versions is 2^64 bits and there are 12 weak key classes. These results are similar to the dedicated linear attack on ARIA version 0.8 and show that the improved version can also not effectively resist this type of attack.展开更多
A linearization attack on the Key Stream Generator （KSG） of the modified Eo algorithm proposed by Hermelin [Proceedings of ICISC＇99, Springer LNCS 1787, 2000, 17-29] is given in this paper. The initial value can be...A linearization attack on the Key Stream Generator （KSG） of the modified Eo algorithm proposed by Hermelin [Proceedings of ICISC＇99, Springer LNCS 1787, 2000, 17-29] is given in this paper. The initial value can be recovered by a linearization attack with O（2^60.52） operations by solving a System of Linear Equations （SLE） with at most 2^20.538 unknowns. Frederik Armknecht [Cryptology ePrint Archive, 2002/191] proposed a linearization attack on the KSG olEo algorithm with O（2^70.341） operations by solving an SLE with at most 2^24.056 unknowns, so the modification proposed by Hermelin reduces the ability or E0 to resist the linearization attack by comparing with the results ofFrederik Armknecht.展开更多
A structure iterated by the unbalanced Feistel networks is introduced. It is showed that this structure is provable resistant against linear attack. The main result of this paper is that the upper bound of r-round （r...A structure iterated by the unbalanced Feistel networks is introduced. It is showed that this structure is provable resistant against linear attack. The main result of this paper is that the upper bound of r-round （r≥2m） linear hull probabilities are bounded by q^2 when around function F is bijective and the maximal linear hull probabilities of round function F is q. Application of this structure to block cipher designs brings out the provable security against linear attack with the upper bounds of probabilities.展开更多
A novel integrated guidance and control (IGC) design method is proposed to solve problems of low control accuracy for a suicide unmanned combat aerial vehicle (UCAV) in the terminal attack stage. First of all, the IGC...A novel integrated guidance and control (IGC) design method is proposed to solve problems of low control accuracy for a suicide unmanned combat aerial vehicle (UCAV) in the terminal attack stage. First of all, the IGC system model of the UCAV is built based on the three-channel independent design idea, which reduces the difficulties of designing the controller. Then, IGC control laws are designed using the trajectory linearization control (TLC). A nonlinear disturbance observer (NDO) is introduced to the IGC controller to reject various uncertainties, such as the aerodynamic parameter perturbation and the measurement error interference. The stability of the closed-loop system is proven by using the Lyapunov theorem. The performance of the proposed IGC design method is verified in a terminal attack mission of the suicide UCAV. Finally, simulation results demonstrate the superiority and effectiveness in the aspects of guidance accuracy and system robustness.展开更多
Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extract...Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.展开更多
文摘This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.
基金Supported by the National Key Basic Research and Development(973) Program of China(No.2007CB807902)the National NaturalScience Foundation of China(Nos.90604036 and 60525201)
文摘ARIA is a new block cipher designed as the block cipher standard of South Korea. The current version is 1.0, which is an improvement of version 0.8 with the security using four kinds of S-boxes instead of two and an additional two rounds of encryptions. These improvements are designed to prevent the dedicated linear attack on ARIA version 0.8 by the four different kinds of S-boxes. This paper presents 12 linear approximations of a single round function that succeeds in attacking ARIA version 1.0 on 7, 9, or 9 rounds for key sizes of 128, 192, or 256 bits using any of these approximations. The corresponding data complexities are 2^87, 2^119, and 2^119, the counting complexities are 1.5×2^88, 2^119, and 2^119, the memory required for each attack on all three key versions is 2^64 bits and there are 12 weak key classes. These results are similar to the dedicated linear attack on ARIA version 0.8 and show that the improved version can also not effectively resist this type of attack.
文摘A linearization attack on the Key Stream Generator （KSG） of the modified Eo algorithm proposed by Hermelin [Proceedings of ICISC＇99, Springer LNCS 1787, 2000, 17-29] is given in this paper. The initial value can be recovered by a linearization attack with O（2^60.52） operations by solving a System of Linear Equations （SLE） with at most 2^20.538 unknowns. Frederik Armknecht [Cryptology ePrint Archive, 2002/191] proposed a linearization attack on the KSG olEo algorithm with O（2^70.341） operations by solving an SLE with at most 2^24.056 unknowns, so the modification proposed by Hermelin reduces the ability or E0 to resist the linearization attack by comparing with the results ofFrederik Armknecht.
基金Supported by the fund of National Laboratory for Modern Communications （5143603ZDS0601）,the outstanding youth science foundation of Henan （0312001800）.
文摘A structure iterated by the unbalanced Feistel networks is introduced. It is showed that this structure is provable resistant against linear attack. The main result of this paper is that the upper bound of r-round （r≥2m） linear hull probabilities are bounded by q^2 when around function F is bijective and the maximal linear hull probabilities of round function F is q. Application of this structure to block cipher designs brings out the provable security against linear attack with the upper bounds of probabilities.
基金supported by the National Natural Science Foundation of China(6160150571501184)the National Aviation Science Foundation of China(20155196022)
文摘A novel integrated guidance and control (IGC) design method is proposed to solve problems of low control accuracy for a suicide unmanned combat aerial vehicle (UCAV) in the terminal attack stage. First of all, the IGC system model of the UCAV is built based on the three-channel independent design idea, which reduces the difficulties of designing the controller. Then, IGC control laws are designed using the trajectory linearization control (TLC). A nonlinear disturbance observer (NDO) is introduced to the IGC controller to reject various uncertainties, such as the aerodynamic parameter perturbation and the measurement error interference. The stability of the closed-loop system is proven by using the Lyapunov theorem. The performance of the proposed IGC design method is verified in a terminal attack mission of the suicide UCAV. Finally, simulation results demonstrate the superiority and effectiveness in the aspects of guidance accuracy and system robustness.
基金supported by the National Natural Science Foundation of China [Nos. 61772452, 61379116]the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [No.2019L0847]the Natural Science Foundation of Hebei Province, China [No. F2015203046]
文摘Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.
