You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems 
Chapter Title:  IV. DISTRIBUTED SECURITY CONTROL AND FILTERING OF INDUSTRIAL CPSS 
1. Abstract of Cited Paper (BIB001):  Distributed estimation of a deterministic mean-shift parameter in additive zero-mean noise is studied when using quantized data in the presence of Byzantine attacks. Several subsets of sensors are assumed to be tampered with by adversaries using different attacks such that the compromised sensors transmit fictitious data. First, we consider the task of identifying and categorizing the attacked sensors into different groups according to distinct types of attacks. It is shown that increasing the number $K$ of time samples at each sensor and enlarging the size $N$ of the sensor network can both ameliorate the identification and categorization, but to different extents. As $K\rightarrow\infty$ , the attacked sensors can be perfectly identified and categorized, while with finite but sufficiently large $K$ , as $N\rightarrow\infty$ , it can be shown that the fusion center can also ascertain the number of attacks and obtain an approximate categorization with a sufficiently small percentage of sensors that are misclassified. Next, in order to improve the estimation performance by utilizing the attacked observations, we consider joint estimation of the statistical description of the attacks and the parameter to be estimated after the sensors have been well categorized. When using the same quantization approach successfully employed without attacks, it can be shown that the corresponding Fisher Information Matrix (FIM) is singular. To overcome this, a time-variant quantization approach is proposed, which will provide a nonsingular FIM, provided that $K\geq 2$ . Furthermore, the FIM is employed to provide necessary and sufficient conditions under which utilizing the compromised sensors in the proposed fashion will lead to better estimation performance when compared to approaches where the compromised sensors are ignored. 
2. Abstract of Cited Paper (BIB002):  Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm. 
3. Abstract of Cited Paper (BIB003):  Distributed generators (DGs) have been developing rapidly in power systems. Motivated by their intrinsic distributed nature, distributed cooperative control based upon local communication recently emerge as a preferred strategy. For instance, a cooperative power control strategy can regulate the active power from a cluster of DGs at a certain ratio of its maximal available power according to a dispatch command. However, such a networked control system is susceptible to both communication failure and cyber-attack, e.g., denial-of-service attack and deceptive attack. To address this potential problem, an attack-resilient cooperative control strategy is proposed in this paper. With a properly designed observation network, each DG can monitor the behaviors of all its in-neighbors, and gradually isolate the misbehaving DGs (when present) from the network as long as they do not collude with each other. Consequently, even certain DGs misbehave, the rest of them can together accomplish the control objective provided that the remaining communication network is still connected. Simulations of the IEEE standard 34-bus test feeder demonstrate effectiveness of the proposed strategy. 
4. Abstract of Cited Paper (BIB004):  Distributed energy management algorithms are being developed for the smart grid to efficiently and economically allocate electric power among connected distributed generation units and loads. The use of such algorithms provides flexibility, robustness, and scalability, while it also increases the vulnerability of smart grid to unexpected faults and adversaries. The potential consequences of compromising the power system can be devastating to public safety and economy. Thus, it is important to maintain the acceptable performance of distributed energy management algorithms in a smart grid environment under malicious cyber-attacks. In this paper, a neighborhood-watch-based distributed energy management algorithm is proposed to guarantee the accurate control computation in solving the economic dispatch problem in the presence of compromised generation units. The proposed method achieves the system resilience by performing a reliable distributed control without a central coordinator and allowing all the well-behaving generation units to reach the optimal operating point asymptotically. The effectiveness of the proposed method is demonstrated through case studies under several different adversary scenarios. 
5. Abstract of Cited Paper (BIB005):  This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders’ decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems. 
6. Abstract of Cited Paper (BIB006):  This paper is concerned with the distributed recursive filtering problem for a class of discrete time-delayed stochastic systems subject to both uniform quantization and deception attack effects on the measurement outputs. The target plant is disturbed by the multiplicative as well as additive white noises. A novel distributed filter is designed where the available innovations are from not only the individual sensor but also its neighboring ones according to the given topology. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of time-delays, deception attacks and uniform quantization effects, an upper bound for the filtering error covariance is guaranteed and subsequently minimized by properly designing the filter parameters via a gradient-based method at each sampling instant. Furthermore, by utilizing the mathematical induction, a sufficient condition is established to ensure the asymptotic boundedness of the sequence of the error covariance. Finally, a simulation example is utilized to illustrate the usefulness of the proposed design scheme of distributed filters. 
7. Abstract of Cited Paper (BIB007):  The recent years have seen a surge of security issues of cyber-physical systems (CPS). In this paper, denial-of-service (DoS) attack scheduling is investigated in depth. Specifically, we consider a system where a remote estimator receives the data packet sent by a sensor over a wireless network at each time instant, and an energy-constrained attacker that cannot launch DoS attacks all the time designs the optimal DoS attack scheduling to maximize the attacking effect on the remote estimation performance. Most of the existing works concerning DoS attacks focus on the ideal scenario in which data packets can be received successfully if there is no DoS attack. To capture the unreliability nature of practical networks, we study the packet-dropping network in which packet dropouts may occur even in the absence of attack. We derive the optimal attack scheduling scheme that maximizes the average expected estimation error, and the one which maximizes the expected terminal estimation error over packet-dropping networks. We also present some countermeasures against DoS attacks, and discuss the optimal defense strategy, and how the optimal attack schedule can serve for more effective and resource-saving countermeasures. We further investigate the optimal attack schedule with multiple sensors. The optimality of the theoretical results is demonstrated by numerical simulations. 
8. Abstract of Cited Paper (BIB008):  In this paper, online detection of false data injection attacks and denial of service attacks in the smart grid is studied. The system is modeled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized cumulative sum algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over existing methods. 
9. Abstract of Cited Paper (BIB009):  This paper investigates and addresses the vulnerability of the distributed DC optimal power flow (DC-OPF) algorithm to data integrity attacks. In particular, we first show that a compromised distributed controller on a single bus could manipulate the power dispatch result by sharing false information to neighboring buses. Two malicious scenarios of launching the data integrity attack are considered, namely economic-driven and infeasibility-driven attacks, respectively. These vulnerabilities demonstrate a growing need for anomaly detection and mitigation mechanisms that fit into the distributed power system framework to counteract highly skilled malicious cyber attackers. We then introduce a resilient distributed DC-OPF algorithm with an embedded attack-resilient control mechanism. It performs two major functions in a fully distributed way: 1) verifying the correctness of the shared information from neighboring buses while protecting each other’s privacy and 2) identifying the compromised distributed controllers and recovering the optimal power dispatch result from the impact of data integrity attacks. The effectiveness of the proposed attack-resilient mechanism is illustrated through case studies in the IEEE 14-bus system. 
10. Abstract of Cited Paper (BIB010):  As one of the widely used applications in wireless sensor networks, target tracking has attracted considerable attention. Although many tracking techniques have been developed, it is still a challenging problem if the network is under cyber attacks. Inaccurate or false information is maliciously broadcast by the compromised nodes to their neighbors. They are likely to threaten the security of the system and result in performance deterioration. In this paper, a distributed Kalman filtering technique with trust-based dynamic combination strategy is developed to improve resilience against cyber attacks. Furthermore, it is efficient in terms of communication load, only local instantaneous estimates are exchanged with the neighboring nodes. Numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks.

Literature Review:
There is no doubt that the increased interaction between physical realms and cyber will result in unavoidable security vulnerabilities. In real-word CPSs, typical cyber-attacks include denial of service attacks, replay attacks, deception attacks, and so forth. Any successful attacks against industrial CPSs may cause the serious impact on the human society and national economy BIB008 . As such, the security of industrial CPSs to defend against these attacks is an urgent research concern BIB005 - BIB002 . It is worth mentioning that, compared with relatively mature techniques under traditional distributed frameworks, distributed control and filtering with security perspective still remain at an infant stage. Recently, from the perspective of defenders, the effect on state estimation from attacks was investigated (see BIB006 , BIB001 , and the references therein). Usually, the security can be enhanced by effectively utilizing the identification information of cyber-attacks or necessary attack information (i.e., attack intensity or attack frequency). In light of the Kalman filtering theory with generalized cumulative sum algorithms, a distributed state estimator is designed in BIB009 in an attempt to realize a quick attack mitigation and system recovery. In comparison with the existing results based on least square approaches, the proposed scheme can not only successfully recognize bad data but also identify structured false data injection attacks. Besides, according to the well-known gradient descent, a recursive algorithm is designed in BIB007 to handle a distributed filtering issue over sensor networks subject to deception attacks and a sufficient condition is derived to check the stability of proposed algorithm. A distributed filter owning the capabilities of attack detection and state estimation is designed and tested via the wide-area monitoring of a power network in BIB003 , where a hybrid Bernoulli random set is introduced to describe the joint information on the attack's presence or not. Different from distributed filtering algorithms in the mean square sense BIB011 , the paradigm of Kullback-Leibler fusion is utilized to handle the challenge from probability density function. It should be pointed out that the obtained algorithm only has limited robustness to attacks arising from the assumption on attack intensity. In the context of industrial CPS security, the performance indexes mainly include security and stability as well as resilience where the tradeoff between security and stability receives growing attention in the literature  . Similar to the distributed filtering of industrial CPSs discussed above, it is also urgent to mitigate the threat from various cyber-attacks via designed control strategies. In view of the core of critical infrastructures, the resulting successful attack on industrial CPSs is generally more serious in contrast to attacks on traditional networked control systems. In addition, according to system recovery, two schemes of attack-resilient cooperative control for industrial CPSs consisting of distributed agents are investigated in BIB004 via the identification and isolation of the misbehaving cyber elements and the cascade protection design of lossless systems. For operator-vehicle networks remotely maneuvered by an operator, a distributed resilient algorithm is developed in  to steer vehicles to the desired formation against attackers. The designed control algorithm can be effectively performed under a distributed form by resorting to the introduction of an auxiliary receding-horizon control. A cooperative resilient control strategy is introduced in BIB004 to regulate the active power from a cluster of DGs at a certain ratio of their maximal available power. Such a strategy combined with a proper observation networks can effectively monitor the behaviors of all neighbors and isolate the misbehaving DGs coming from noncolluding cyber-attacks. Additionally, a resilient distributed algorithm is proposed in BIB010 to solve a dc optimal power flow issue against data integrity attacks. In these two papers, the consensus conception is utilized to estimate the utilization ratios of DGs in BIB004 by introducing a virtual power plant and estimate the bus power imbalance in BIB010 by adopting the primal-dual decomposition method. It should be pointed out that almost all results on security in the framework of control theory are based on an assumption that system dynamics need to be simple, which leads to a gap between theoretical results and practical engineering applications.