Proposed Attack Strategy Research Articles

Optimal innovation-based attacks against remote state estimation with side information and historical data

This work investigates the attack strategy design problem of the false data injection attack against the cyber-physical system. Distinct from the relevant results which utilise the current intercepted data or additionally consider side information, a more universal attack model is proposed which combines historical data and side information with the current intercepted data to synergistically deteriorate the system estimation performance. In order to quantify the impact resulting from the proposed attack strategy, the optimisation objective is characterised by deriving the error covariance matrix under the attacks, which becomes more intricate since the proposed attack model introduces more decision variables and coupled terms. Take the stealthiness which is characterised by Kullback-Leibler divergence as the constraints, the problem investigated in this work is equivalently transformed into the constrained multi-variable non-convex optimisation problems, which are not able to be solved directly by the methods in the relevant results. By utilising the Lagrange multiplier method, the structural characteristic of the optimal mean and the optimal covariance which only related to the Lagrange multiplier are derived, such that the optimal distribution of the modified innovation is able to be obtained by a simple search procedure. Following that, the design of the optimal attack strategy is completed by using semi-definite programming to derive the optimal attack matrices. Finally, the simulation examples are given such that the validity of the results is verified.

Optical attenuator-oriented quantum hacking on continuous-variable quantum key distribution with local local oscillator

Continuous-variable quantum key distribution with local local oscillator (LLO-CVQKD) eliminates the security loophole caused by transmitting high power local oscillator (LO). However, in order to establish a common phase frame between the sender and receiver, a low power phase reference needs to be transmitted with quantum signals, which may bring in new security problems for eavesdroppers to obtain information illegally. Especially in trusted phase noise model, phase noise associated with detectors is regarded as trusted noise in order to obtain a higher secret key rate and transmission distance, which further provides opportunities for eavesdroppers. To this end, we propose an optical attenuator-oriented attack strategy against the LLO-CVQKD system under the trusted phase noise model, where Eve can inject suitable light into the optical attenuator in reference light path to reduce the attenuation level of it, thereby controlling the intensity of the reference pulses and resulting in an underestimated excess noise. Simulation results show that the proposed attack strategy can render the estimated secret key rate much higher than the actual values. When the transmission distance exceeds 25 km, the eavesdropper can obtain approximately 90% of the key information by adjusting the intensity of the reference pulses to twice the original value.

A powerful adversary model and corresponding OTP time slot allocation scheme in RIS-assisted physical layer key generation

Physical layer key generation (PLKG) is a technique of information-theoretic security to tackle the problem of key distribution between resource-constrained legitimate users and is a promising candidate for the one time pad (OTP) technique. However, in quasi-static, the key rate is greatly limited due to low entropy. Reconfigurable intelligent surface (RIS) is introduced to adaptively reconfigure the radio environment. However, how to allocate time slots in the OTP to counter the increasingly powerful adversary model is an urgent problem to be solved. In this paper, we propose a very powerful adversary model and give an attack strategy called eavesdropping channel search, which allows Eve to use its search and eavesdropping capabilities to maximize the probability of successful attacks. Meanwhile, we propose a time slot allocation algorithm in the OTP to ensure the security of the key. Simulations validate that our proposed attack strategy is more powerful than any existing adversary model and our proposed time slot allocation algorithm does not have any security loss.

Trajectory tracking attack for vehicular ad‐hoc networks

AbstractMaintaining user privacy and security is a critical concern in vehicular ad hoc networks (VANETs). However, prior research has neglected the study of matrix recovery attack methods in VANETs and the challenge of reducing the number of roadside units (RSUs). In this article, we formulate a path recovery strategy using matrix recovery techniques from an adversarial view. Subsequently, the challenge of minimizing RSUs while monitoring all user vehicles in a region is converted into a set cover problem. We introduce a heuristic algorithm that utilizes clustering to address this issue. To minimize matrix recovery errors, a Kalman filter based method is integrated to enhance the performance. This paper also presents an initial deployment of path recovery attacks, maintaining effectiveness even with certain defense mechanisms in place. Furthermore, we conduct simulation experiments to evaluate the effectiveness of the proposed attack strategy. The simulation results demonstrate the performance across various dimensions. Finally, the results show that the success rate of our proposed counter‐defense strategy in overcoming user defenses surpasses 50%.

An optimal cyber–physical attack strategy on DC microgrids

This paper proposes an optimal attack strategy against Direct Current MGs (DCMGs). In the proposed strategy, attackers first eavesdrop on the data communicated between a target converter and the MG Control Center (MGCC) to model (i) the converter by its state-space representation using system identification techniques, and (ii) the rest of the system by its Thevenin equivalent circuit using the least-square method. Afterwards, the obtained model is used in an optimal control framework to design a False Data Injection Attack (FDIA) that controls the converter’s output to achieve the attackers’ objective, e.g., triggering the overcurrent or under-voltage protection of the converter. The proposed framework fulfills the objective while minimizing the rate of change of the converter’s voltage and the required control effort during the FDIA (to keep the attack stealthy) in the presence of the attack constraints. Such an attack strategy provides cyber-security experts with insight into potential attacks and their impacts on the operation and stability of DCMGs, and helps them to design and test their cyber-security measures more effectively. Simulation results in MATLAB/Simulink corroborates the effectiveness of the proposed attack strategy.

Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

With the continuous expansion of the power grid nodes scale, traditional centralized state estimation method shows certain limitations in estimation efficiency and accuracy. Recently, some power grids adopt a distributed state estimation method, in which each partition independently estimates the partial state information by partitioning the entire power system. However, the deviation of the state estimation in certain partition will result in the deviation of the estimation results in the entire power grid system. In this paper, we propose the attack strategy against the distributed state estimation in smart grids from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Moreover, the theoretical analysis of the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations are formalized. The effectiveness of the proposed attack strategy is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of a distributed system can indirectly affect the state estimation results of other partitions and attacking against the measurement value of coordination center can directly threaten the state estimation results of the entire power grid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes two attack strategies against the distributed state estimation of power grid from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Most of the previous works fail to formalize the state estimation deviation of both partial and entire state estimation results of power grid after the attacker launches the attack against the distributed state estimation. We formalize the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations. The effectiveness of the proposed attack strategy against the state estimation of power grid is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of power grid can indirectly cause the deviation in the state estimation results of entire power grid and attacking against the measurement value of the coordination center can directly threaten the state estimation results of the entire power grid. In conclusion, the proposed attack strategies are helpful for the research community to design detection strategies in a targeted manner and can be conveniently applied to the real-world security management system of smart grid.

On Information Fusion in Optimal Linear FDI Attacks Against Remote State Estimation

This paper studies the problem of false data injection (FDI) attacks against remote state estimation. The scenario that malicious attackers can intercept original data packets and also eavesdrop on some side information of system states with extra sensors is considered. To clarify a counter-intuitive issue in existing work, a different innovation-based linear attack policy fusing all available information is proposed. First, the evolution of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimation error covariance with FDI attacks is derived. Then, explicit solutions of optimal stealthy attack coefficients are obtained without solving optimization problems numerically. The condition under which there exist multiple optimal attacks is analyzed. Additionally, an easy-to-check criterion for comparing two information fusion methods in scalar systems is given. Simulation results show that, compared with existing work, the proposed attack strategy can completely deceive the anomaly detector and cause more severe performance degradation in remote state estimation.

Research on the Attack Strategy of Multifunctional Market Trading Oriented to Price

In the context of energy transformation and power market system reform, it is crucial to address the network risks associated with enhancing the integration of the “Energy–Information–Market” paradigm. This necessitates research on multi-energy market trading modes and the corresponding offensive and defensive technologies. This paper proposes a novel approach centered around a node-local Energy Hub (EH) that represents large industrial users with diverse energy demands. To facilitate multi-energy two-way trading, a price-oriented Transactive Energy (TE) market clearing strategy is developed. Building upon this transaction network framework, a data-driven attack strategy targeting the state estimator of the Transmission System Operator (TSO) is introduced and implemented in two stages, encompassing real-time topology estimation and False Data Injection attacks. By leveraging Matrix Transfer Entropy (MTE), the optimal attack target is identified to disrupt the economic stability of the system and the profit of the attacker increases significantly. The proposed attack strategy is validated through simulations conducted on a 30-node system, yielding conclusive evidence of its effectiveness while offering vital insights for system defense.

An Optimal DoS Attack Strategy With Pause and Restart Rules Under Energy Constraints

This study investigates a denial-of-service (DoS) attack strategy against cyber-physical systems (CPS) under energy constraints. A DoS attacker with eavesdropping ability under a limited energy budget was considered. By scheduling the attack behavior based on historical attack results, the attacker aims to maximize the state estimation error of the CPS. A novel DoS attack strategy with pause and restart rules was proposed, in which the attacker pauses the attack for a while to restore energy after a failed attack attempt. The restart rule was described based on Markov chain modeling of the attacking period. The transition matrix and stationary distribution of the Markov chain model were also presented. To maximize the attack effect, the optimal restart rule of the proposed strategy was investigated. Finally, it was proven that the proposed attack strategy in this study is more destructive than the periodic attack strategy, and simulations were provided to verify our theoretical results.

Revisiting the transferability of adversarial examples via source-agnostic adversarial feature inducing method

Though deep neural networks (DNNs) have revealed their extraordinary performance in the fields of computer vision, it is evident that the vulnerability of DNNs to adversarial attacks with crafted human-imperceptible perturbations. Most existing adversarial attacks draw their attention to invading target deep task models by enhancing input-diagnostic features via image rotation, warp, or transformation to improve adversarial transferability. Such manners pay close concentration to operation on original inputs regardless of the properties from different source information. Research has inspired us to consider utilizing source-agnostic information and integrating generated features with raw inputs to enrich adversarial properties. For such needs, we propose a simple and flexible adversarial attack method with source-agnostic Feature Inducing Method (FIM) for improving the transferability of adversarial examples (AEs). FIM first focuses on generating perturbed features by imitating diverse patterns from multi-domain sources. Instead of exploiting the original inputs’ diversity, such proposed work gains the various properties by random feature imitation referring to different source distributions. By optimizing the generated features with norm bounds, FIM then integrates original inputs with imitative features. Such manner can diverse row positive class-general features, which reduce the capability of class-specific patterns on cross-model transferability. Based on the crafted property, FIM employs the adaptive gradient-based strategy on such information to generate perturbations, which helps to decrease probability dropping into local optimal when searching for the decision boundary of source and target models. We conduct detailed experiments to evaluate the performance of our proposed approach with existing baselines on three public datasets. The experimental results reveal the better performance of the proposed works on fooling source and target task models leading to a considerable margin in most adversarial scenarios. We further investigate adversarial attacks on adversarial defense models (with adversarial training and trades). Such a proposed attack strategy achieves better attack quality by a margin over 3.00% on CIFAR10 and reduces the robust accuracy of adversarially trained models by a large margin near 9.00% on MNIST. Furthermore, we exploit the performance of the proposed attack strategy applied to feature-level adversarial domains and conduct evaluations to demonstrate its adversarial feasibility in integrating with various attack mechanisms, which gains better adversarial effectiveness over 20.00% than the base attacks on studied deep task models.

Optimal Stealthy Attack on Cyber-Physical Systems and Its Application to a Networked PMSM System

This paper investigates the stealthy attack on cyber-physical systems (CPSs) under the stochastic communication protocol (SCP), which is used to avoid data collisions. The attack stealthiness is characterized by the Kullback-Leibler divergence (KLD) and the attack performance is measured by the corrupted error covariance. This paper aims to maximize the attack performance for a certain stealthy level. Different from the existing results, our proposed attack strategy takes the SCP-introduced effects into account and exploits incomplete measurement outputs at each time instant to construct attack model. Then, a non-convex optimization problem is formulated to achieve the desired attack performance for a given stealthy level. By applying the monotonic optimization technique, the non-convex problem is converted to two optimization problems with equality constraints. Then, the analytical expressions of the attack parameters are derived. Finally, simulations on a three-tank system and experiments on a networked permanent magnet synchronous machine (PMSM) prototype system are conducted to validate our proposed theoretical results.

Stealthy Noise Injection Attacks Against State Estimation in Interconnected Systems

This brief investigates the stealthy attack design problem for interconnected systems, where the aim of the attacker is to maximize the estimation error covariance of the centralized state estimator by injecting elaborately designed Gaussian noises into the compromised subsystem. The proposed attack strategy can achieve complete stealthiness for local detectors and keep a certain level of stealthiness for the centralized one simultaneously, which differs from the existing attack strategies for interconnected systems. Due to the nature of the attack model and interconnected systems, the stealthiness constraint is so complex that it is difficult to directly solve the formulated optimization problem to obtain the detailed attack strategy. To transform the stealthiness constraint, the eigenvalues of matrices are used to characterize the upper and lower bounds of the corresponding determinant, and then the closed-form optimal attack strategy is derived. Finally, a 1D network simulation example is provided to verify the developed results.

Stochastic Data-Based Denial-of-Service Attack Strategy Design Against Remote State Estimation in Interval Type-2 T–S Fuzzy Systems

This article focuses on designing a novel stochastic data-based denial-of-service (DoS) attack strategy that can intercept measurements and reduce the quality of remote state estimation in interval type-2 (IT2) T–S fuzzy systems. The new proposed DoS attack strategy has two main characteristics. First different from most of the existing DoS models, it is data-based, which mainly attacks the packets playing important roles in the system performance. Therefore, compared with the existing indiscriminate DoS attack approaches, the data-based DoS attack is more intelligent and can cause larger disruptions. Second, the proposed attack strategy is random, which has better concealment than the periodic or consecutive DoS attack models. Then, by using a stochastic method, the relationship among attack effect, attack probability, and attack parameter are analyzed and shown in Theorem <xref ref-type="theorem" rid="theorem9" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> . Furthermore, if the attacks are energy-constrained, the relation between the attack rate threshold and the upper bound of the attack parameter is expressed analytically, which is shown in Theorem <xref ref-type="theorem" rid="theorem12" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</xref> . Simulation results demonstrate that, compared with some existing DoS models, the proposed stochastic data-based DoS attack strategy brings about greater destructiveness to the estimation quality of the systems, which demonstrates the effectiveness of the proposed attack strategy from the attacker’s perspective.

Completely Stealthy FDI Attack Against State Estimation in Networked Control Systems

An effective false data injection attack should cause enough damage to the system while avoiding being spotted by detection methods. This brief proposes an extremely stealthy attack strategy, which can make the residual almost always unchanged to invalidate both the widely used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> detector and the advanced summation detector and can eventually cause unbound state deviations. Compared with the existing attack methods, the proposed attack strategy can reduce the sum of cumulative residual increments by 85.39% while causing 9817001 times of the state deviations in the numerical simulations.

Multiplicative Attacks with Essential Stealthiness in Sensor and Actuator Loops against Cyber-Physical Systems.

Stealthy attacks in sensor and actuator loops are the research priorities in the security of cyber-physical systems. Existing attacks define the stealthiness conditions against the Chi-square or Kullback-Leibler divergence detectors and parameterize the attack model based on additive signals. Such conditions ignore the potential anomalies of the vulnerable outputs in the control layer, and the attack sequences need to be generated online, increasing the hardware and software costs. This paper investigates a type of multiplicative attack with essential stealthiness where the employed model is a novel form. The advantage is that the parameters can be designed in a constant form without having to be generated online. An essential stealthiness condition is proposed for the first time and complements the existing ones. Two sufficient conditions for the existence of constant attack matrices are given in the form of theorems, where two methods for decoupling the unknown variables are particularly considered. A quadruple-tank process, an experimental platform for attack and defense, is developed to verify the theoretical results. The experiments indicate that the proposed attack strategy can fulfill both the attack performance and stealthiness conditions.

Load Oscillating Attacks of Smart Grids: Vulnerability Analysis

We investigate the vulnerability of a power transmission grid to load oscillation attacks. We demonstrate that an adversary with a capability to oscillate a small fraction of load and monitor some tie line flows can launch a successful load oscillation attack to destabilize the grid. The adversary is assumed to be able to compromise smart meters at a subset of load buses and control their switches. In the reconnaissance stage of the proposed attack, the adversary estimates the line flow sensitivity factors (LFSFs) associated with the monitored tie lines by perturbing a small amount of load at compromised buses and observing how the monitored line flow changes. The learned LFSF values are used to select a target line and optimize the oscillation attack magnitudes at the compromised load buses to cause the target line to trip. In the execution stage, the oscillation attack frequency is adjusted in real time, based on the monitored target line flow measurements, in order to maximize the attack impact under the system condition at the attack time. We experimentally demonstrate that the proposed attack strategy can result in a much more severe consequence than benchmark oscillation attacks, using the COSMIC time-domain simulator with two test cases, the IEEE RTS-96 and Polish 2383-Bus Systems. The proposed attack strategy succeeded in causing a full blackout by oscillating only 8% of the load in the IEEE RTS-96 test system, and 1.7% of the load in the Polish test system.

Stealthy switching attacks on sensors against state estimation in cyber‐physical systems

AbstractThis article focuses on designing sensor attacks to deteriorate the state estimation in cyber‐physical systems. The scenario that the malicious attack signals can be injected into different but limited number of sensor communication channels is considered. The state estimation error variations and innovation variations are adopted to measure attack performance and attack stealthiness, respectively. A switching attack strategy is proposed, under which the estimation error variations are driven to the predesigned target value and the norm of innovation variations remains at a small level. The switching attack design problem is formulated as a discrete switched optimal control problem which can be solved by dynamic programming, while the computational burden is heavy. To overcome this difficulty, by using pruning technique to remove the redundant matrices generated in dynamic programming, the quadratic optimization problem becomes numerically tractable. In this way, the suboptimal attack signal sequence and switching sequence can be acquired. Finally, a simulation example is provided to illustrate the effectiveness of the proposed attack strategy.

Profit-Oriented False Data Injection Attack Against Wind Farms and Countermeasures

Wind farm industrial control systems (ICSs) play an important role in the operation of wind turbines (WTs). To increase revenues, a corrupt wind power producer tends to compromise the ICSs of the opponents’ wind farms by launching false data injection (FDI) attacks. In this article, we investigate a novel profit-oriented FDI (POFDI) attack against wind farms and study corresponding attack detection schemes. Specifically, we proposed a potential FDI attack model against ICSs of wind farms considering the power distribution of wind farms. Based on the characteristics of the proposed attack model, a profit-oriented attack strategy is discussed as a composition of attack detection. Considering the characteristics of the compromised data and those of normal data, we develop countermeasures to detect the POFDI attack, through the relationship between the characteristics of WTs in wind farms. A relation-based detection network is used to evaluate the similarity between the data to be tested and the historical data. Simulation results show the impact of the proposed attack strategy and the effectiveness of the attack detection scheme.

Practical attacks on Login CSRF in OAuth

OAuth 2.0 is an important and well studied protocol. However, despite the presence of guidelines and best practices, the current implementations are still vulnerable and error-prone. This research mainly focused on the Cross-Site Request Forgery (CSRF) attack. This attack is one of the dangerous vulnerabilities in OAuth protocol, which has been mitigated through state parameter. However, despite the presence of this parameter in the OAuth deployment, many websites are still vulnerable to the OAuth-CSRF (OCSRF) attack. We studied one of the most recurrent type of OCSRF attack through a variety range of novel attack strategies based on different possible implementation weaknesses and the state of the victim’s browser at the time of the attack. In order to validate them, we designed a repeatable methodology and conducted a large-scale analysis on 395 high-ranked sites to assess the prevalence of OCSRF vulnerabilities. Our automated crawler discovered about 36% of targeted sites are still vulnerable and detected about 20% more well-hidden vulnerable sites utilizing the novel attack strategies. Based on our experiment, there was a significant rise in the number of OCSRF protection compared to the past scale analyses and yet over 25% of sites are exploitable to at least one proposed attack strategy. Despite a standard countermeasure exists to mitigate the OCSRF, our study shows that lack of awareness about implementation mistakes is an important reason for a significant number of vulnerable sites.

DoS Attack Allocation Against Remote State Estimation Over Packet-Dropping Networks

We consider the security problem of remote state estimation in cyber-physical systems. The sensor sends data under round-robin protocol to the remote side while denial-of-service (DoS) attacker could interfere this process. Due to the energy-constrained DoS attack, it needs to decide whether to launch attacks to the wireless channel. In this work, two different cases for multi-sensor running Kalman filter are studied, i.e., sensors have limited or sufficient computational abilities. Furthermore, to capture the unreliability of the communication network, the occurrence of packet loss in the absence of attack is considered. Based on the evolution process of estimation error covariance, we first propose the sufficient condition to guarantee the optimality of the attack allocation strategy which maximally degrades the system performance. Then, the optimal attack sequence is given in a closed-form and shown to be monotonic with respect to time. Finally, a simulation example is given to illustrate the effectiveness of our proposed attack strategy.