RSU Placement Optimization for Securing Vehicle Platoon against False Injection Attacks

In power transmission systems, the false data injection (FDI) attacks against state estimation (SE) have been well studied. However, due to the unique features of power distribution systems including the low x/r ratio, existence of one-and/or two-phase branches, unbalanced load distributions, and unsymmetrical line parameters, the research on FDI attacks against distribution system state estimation (DSSE) is still open. In this paper, we investigate the vulnerability of DSSE to FDI attacks. In particular, we firstly propose a local state-based linear DSSE for multiphase and unbalanced smart distribution systems, which can facilitate the construction of FDI attacks numerically with the least information of system states. Then, the construction of three-phase coupled FDI attacks is introduced. The consideration of the coupling among phases by the three-phase coupled FDI attacks may require the modification of a large number of measurements by the attackers. To reduce the number of required measurements, the perfect three-phase decoupled FDI attacks, which consider the weak couplings among phases, is investigated. The probabilities of successful three-phase decoupled FDI attacks in strongly three-phase coupled systems are also derived numerically. The performance of the proposed FDI attacks against DSSE is evaluated based on IEEE test feeders. The case study results indicate the feasibility of the FDI attacks against DSSE in practical multiphase and unbalanced smart distribution systems. Future research directions including potential countermeasures are also highlighted.

While acquiring precise and intelligent state sensing and control capabilities, the cyber physical power system is constantly exposed to the potential cyber-attack threat. False data injection (FDI) attack attempts to disrupt the normal operation of the power system through the coupling of cyber side and physical side. To deal with the situation that stealthy FDI attack can bypass the bad data detection and thus trigger false commands, a system feature extraction method in state estimation is proposed, and the corresponding FDI attack detection method is presented. Based on the principles of state estimation and stealthy FDI attack, we analyze the impacts of FDI attack on measurement residual. Gaussian fitting method is used to extract the characteristic parameters of residual distribution as the system feature, and attack detection is implemented in a sliding time window by comparison. Simulation results prove that the proposed attack detection method is effectiveness and efficiency.

HighlightsWhat are the main findings?A multisensor multitarget tracking algorithm against the false data injection (FDI) attacks over networks.A detection method for FDI attacks based on Kullback–Leibler divergence (KLD) between labeled multi-Bernoulli densities.What is the implication of the main finding?The proposed algorithm can efficiently detect/defend the FDI attacks and provide reliable tracking performance.This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback–Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks.

Chapter 6 - False data injection attacks

From the perspective of an attacker, this paper studies how to destroy the consensus of distributed multi-agent systems by employing False Data Injection (FDI) attacks. A stealthy FDI attack model is proposed to make the tracking errors diverge while allowing the consensus errors to remain as expected. The proposed model does not rely on real-time node information from the multi-agent systems. Furthermore, the minimum cost of attack edge sets is given, taking into account the limited energy available for the FDI attacks. The corresponding algorithm is further provided. Numerical simulations verify the effectiveness of the proposed FDI attack strategy.

With the enhanced capabilities of the SCADA system, the power system can monitor its operating states in real-time. On the other hand, it also makes the power system more vulnerable to various kinds of attacks. One attack that has serious consequences is the False Data Injection (FDI) attack against the state estimation process. Although some techniques have been proposed to select meters to protect, none of them considers the cost of protecting meters, and thus will not perform well when only a limited number of meters can be protected due to budget limitation. In this paper, we consider a new problem: Given a limited budget, how to select the most critical meters to protect so that the probability of attackers launching successful stealthy FDI attack is minimized? We first formalize this problem which is NP-complete, and then propose heuristic based solutions. The idea is to rank and select meters based on a metric called vulnerability index, which considers two factors: how likely the meter will be targeted by the attacker to launch FDI attacks and how much damage will be caused by compromising the meter in case of a successful stealthy FDI attack. Evaluation results show that our algorithm can significantly reduce the probability of successful attacks, as well as the potential damage caused by FDI attacks.

In power transmission systems, the false data injection (FDI) attacks against state estimation (SE) have been well studied. However, due to the unique features of power distribution systems including: the low x/r ratio, existence of one-and/or two-phase branches, unbalanced load distributions, and unsymmetrical line parameters; the research on FDI attacks against distribution system SE (DSSE) is still open. In this paper, we investigate the vulnerability of DSSE to FDI attacks. In particular, we first propose a local state-based linear DSSE for multiphase and unbalanced smart distribution systems, which can facilitate the construction of FDI attacks numerically with the least information of system states. Then, the construction of three-phase coupled FDI attacks is introduced. The consideration of the coupling among phases by the three-phase coupled FDI attacks may require the modification of a large number of measurements by the attackers. To reduce the number of required measurements, the perfect three-phase decoupled FDI attacks, which consider the weak couplings among phases, is investigated. The probabilities of successful three-phase decoupled FDI attacks in strongly three-phase coupled systems are also derived numerically. The performance of the proposed FDI attacks against DSSE is evaluated based on IEEE test feeders. The case study results indicate the feasibility of the FDI attacks against DSSE in practical multiphase and unbalanced smart distribution systems. Future research directions including potential countermeasures are also highlighted.

The integration of phasor measurement units (PMUs) and phasor data concentrators (PDCs) in smart grids may be exploited by attackers to initiate new and sophisticated false data injection (FDI) attacks. Existing FDI attack mitigation approaches are generally less effective against sophisticated FDI attacks, such as collusive false data injection (CFDI) attacks launched by compromised PDCs (and PMUs) as we demonstrate in this paper. Thus, we propose a secure and resilience-enhanced scheme (SeCDM) to detect and mitigate such cyber threats in smart grids. Specifically, we design a decentralized homomorphic computation paradigm along with a hierarchical knowledge sharing algorithm to facilitate secure ciphertext calculation of state estimation residuals. Following this, a centralized FDI detector is implemented to detect FDI attacks. Findings from the security analysis demonstrate our approach achieves enhanced conventional FDI and CFDI attack resilience, and findings from our performance evaluations on the standard IEEE 14-, 24-, and 39-bus power systems also show that the communication overheads and computational complexity are reasonably “low”.

Recent research has proposed a moving target defense (MTD) approach that actively changes transmission line susceptance to preclude stealthy false data injection (FDI) attacks against the state estimation of a smart grid. However, existing studies were often conducted under a weak adversarial setting, in that they ignore the possibility that alert attackers can also try to detect the activation of MTD before they launch the FDI attacks. We call this new threat as parameter confirming-first (PCF) FDI. To improve the stealthiness of MTD, we propose a hidden MTD approach that cannot be detected by the attackers and prove its equivalence to an MTD that maintains the power flows of the whole grid. Moreover, we analyze the completeness of MTD and show that any hidden MTD is incomplete in that FDI attacks may bypass the hidden MTD opportunistically. This result suggests that the stealthiness and completeness are two conflicting goals in MTD design. Finally, we propose an approach to enhancing the hidden MTD against a class of highly structured FDI attacks. We also discuss the MTD's operational costs under the dc and ac models. We conduct simulations to show the effectiveness of the hidden MTD against PCF-FDI attacks under realistic settings.

Random subspace ensemble-based detection of false data injection attacks in automatic generation control systems

False data injection attack resilient distributed exponential sliding mode consensus protocol for discrete multi-agent system.

In this article, we investigate the resilient distributed secondary control problems of the ac bus frequency and ac main bus voltage regulation, along with active/reactive power sharing among bidirectional interlinking converters (BICs) under false data injection (FDI) attacks in hybrid ac/dc microgrids (MGs). To solve these problems, the distributed iterative observers are first proposed, which incorporate the dynamic characteristics of the previous observation into the present FDI attack observation to achieve the accurate observation of the ac bus frequency and ac main bus voltage, as well as active power, reactive power, and injected FDI attacks of each BIC. Afterward, based on the average FDI attack estimation, a resilient distributed secondary controller is introduced to eliminate the impact of FDI attacks on BICs, achieving accurate recovery of ac bus frequency and ac main bus voltage, as well as the sharing of active/reactive power among BICs under FDI attacks. Compared with existing resilient control strategies in hybrid ac/dc MGs under FDI attacks, the proposed resilient control strategy can enable hybrid ac/dc MGs to exhibit better transient performance during FDI attack injection and disappearance. Finally, the efficacy of the proposed observers and controller is confirmed through a real-time controller-hardware-in-the-loop experiment in OPAL-RT.

Power system state estimation is subject to false data injection (FDI) attacks, because of the integration of advanced computation and communication techniques in power systems. By coordinately tampering the readings of meters, FDI attacks can bypass bad data detectors and stealthily mislead the results of state estimation. In this paper, we propose a joint admittance perturbation and meter protection (JAPMP) strategy to enhance the resilience of state estimation under stealthy FDI attacks, i.e., recover system states from compromised measurements. State recovery conditions in JAPMP are derived, which indicate that the integration of admittance perturbation and meter protection can significantly improve the recoverability of system states. Based on the state recovery conditions, a JAPMP optimization problem is formulated and solved by decomposing the joint optimization problem into two subproblems. Then, a heuristic algorithm is designed, which can greatly reduce the computational complexity in solving the combinatorial problem. Based on the simulation results on a 6-bus, the IEEE 14-bus, the IEEE 57-bus and the IEEE 118-bus test systems, the protection cost and performance of state recovery under stealthy FDI attacks are evaluated.

The largest and the most complex cyber-physical systems, the smart grids, are under constant threat of multi-faceted cyber-attacks. The state estimation (SE) is at the heart of a series of critical control processes in the power transmission system. The false data injection (FDI) attacks against the SE can severely disrupt the power systems operationally and economically. With knowledge of the system topology, a cyber-attacker can formulate and execute stealthy FDI attacks that are very difficult to detect. Statistical, physics-based, and more recently, data-driven machine learning-based approaches have been undertaken to detect the FDI attacks. In this chapter, we employ five supervised machine learning models to detect stealthy FDI attacks. We also use ensembles, where multiple classifiers are used and decisions by individual classifiers are further classified, to find out if ensembles give any better results. We also use feature selection method to reduce the number of features to investigate if it improves detection rate and speed up the testing process. We run experiments using simulated data from the standard IEEE 14-bus system. The simulation results show that the ensemble classifiers do not perform any better than the individual classifiers. However, feature reduction speeds up the training by manyfold without compromising the model performance.KeywordsEnsemble learningFeature reductionSmart gridStealthy false data injection attackSupervised machine learning

Vehicular platooning is a promising technology for improving road safety, increasing vehicle efficiency, and reducing traffic congestion by enabling high-speed vehicles to travel in close formation with minimum inter-vehicular distance. However, a False Data Injection (FDI) attack can destabilise and break up vehicular platoons in several different ways. First, an attacker can inject false leave or split messages leading to a breakup of the vehicular platoon. Another way is by sending fake beacons or tampering information (such as speed, acceleration, distance, location etc) in a beacon. Upon receiving this false data, the platoon will destabilise as the members receives tampered information from the attacker. In this paper, we studied the impact of FDI attacks on the vehicular platoon by modifying significant information in a beacon. We carried out a simulation-based study, where a FDI attacker is modelled in Plexe simulator to attack a platoon. We considered two scenarios for an FDI attack, i.e., the attacker can be present both inside and outside of the platoon. Further, two flavours of FDI attacks are implemented, i.e., (1) Constant FDI: where, the attacker is launching FDI attack constantly throughout it’s journey, and (2) Intelligent On-Off FDI: where the attacker is performing FDI for short period of time and then hides his identity by performing legitimate communication with platoon members. We studied the impact of FDI attacks on vehicular platoons from three significant aspects: environmental (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions), safety (distance), and stability (speed). Our study showed that FDI attacks can have drastic impact on the vehicular platoons.