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 Home Energy Management 
Chapter Title:  B. COUNTER-MEASURES 
1. Abstract of Cited Paper (BIB001):  Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5 Bus test system, we can show the effectiveness and properties of the studied game. 
2. Abstract of Cited Paper (BIB002):  Vulnerability analysis of a power grid, especially in its static status, is often performed through solving a bi-level optimization problem, which, if solved to optimality, yields the most destructive interdiction plan with the worst loss. As one of the most effective operations to mitigate deliberate outages or attacks, transmission line switching recently has been included and modeled by a binary variable in the lower level decision model. Because this bi-level (or an equivalent min-max) problem is a challenging nonconvex discrete optimization problem, no exact algorithm has been developed, and only a few recent heuristic procedures are available. In this paper, we present an equivalent single-level reformulation of this problem, and describe a column-and-constraint generation algorithm to derive the global optimal solution. Numerical study confirms the quality of solutions and the computational efficiency of the proposed algorithm. Discussion and analysis of the mitigation effect of line switching are presented. 
3. Abstract of Cited Paper (BIB003):  Ensuring security of systems based on supervisory control and data acquisition is a major challenge. The goal of this paper is to develop the model-based techniques capable of detecting integrity attacks on the sensors of a control system. In this paper, the effect of integrity attacks on the control systems is analyzed and countermeasures capable of exposing such attacks are proposed. The main contributions of this paper, beyond the novelty of the problem formulation, lies in enumerating the conditions of the feasibility of the replay attack, and suggesting countermeasures that optimize the probability of detection by conceding control performance. The methodologies are shown and the theoretical results are validated using several sets of simulations. 
4. Abstract of Cited Paper (BIB004):  We propose new game theoretic approaches to estimate a binary random variable based on sensor measurements that may have been corrupted by a cyber-attacker. The estimation problem is formulated as a zero-sum partial information game in which a detector attempts to minimize the probability of an estimation error and an attacker attempts to maximize this probability. While this problem can be solved exactly by reducing it to the computation of the value of a matrix, this approach is computationally feasible only for a small number of sensors. The two key results of this paper provide complementary computationally efficient solutions to the construction of the optimal detector. The first result provides an explicit formula for the optimal detector but it is only valid when the number of sensors is roughly smaller than two over the probability of sensor errors. In contrast, the detector provided by the second result is valid for an arbitrary number of sensor. While it may result in a probability of estimation error that is ϵ above the minimum achievable, we show that this error ϵ is small when the number of sensors is large, which is precisely the case for which the first result does not apply. 
5. Abstract of Cited Paper (BIB005):  By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the $\chi^{2}$ -detector or the proposed Euclidean detector. The $\chi^{2}$ -detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The $\chi^{2}$ -detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the $\chi^{2}$ -detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack. 
6. Abstract of Cited Paper (BIB006):  Cyberphysical systems (CPSs) refer to the embedding of widespread sensing, networking, computation, and control into physical spaces with the goal of making them safer, more efficient, and reliable. Driven by the miniaturization and integration of sensing, communication, and computation in cost-efficient devices, CPSs are bound to transform industries such as aerospace, transportation, built environments, energy, health care, and manufacturing, to name a few. This great opportunity, unfortunately, is matched by even greater challenges. Taming the complexity of design and analysis of these systems poses a fundamental problem as a new paradigm is needed to bridge various scientific domains, which, through the years, have developed significantly different formalisms and methodologies. In addition, while the use of dedicated communication networks has so far sheltered systems from the outside world, use of off-the-shelf networking and computing, combined with the unattended operation of a plethora of devices, provides several opportunities for malicious entities to inject attacks on CPSs. A wide variety of motivations exists for launching an attack on CPSs, ranging from economic reasons, such as obtaining a financial gain, all the way to terrorism, for instance, threatening an entire population by manipulating life-critical resources. Any attack on safety-critical CPSs may significantly hamper the economy and lead to the loss of human lives. While the threat of attacks on CPSs tends to be underplayed at times, the Stuxnet worm provided a clear example of the possible future to come. This malware, targeting a uranium enriching facility in Iran, managed to reach the supervisory control and data acquisition (SCADA) system controlling the centrifuges used in the enrichment process. Stuxnet modified the control system, increasing pressure in the centrifuges in a first version of the worm and spinning centrifuges in an erratic fashion in a second version. As a result, Stuxnet caused significant damage to the plant [1]. For details, see "The Stuxnet Attack." 
7. Abstract of Cited Paper (BIB007):  Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework. 
8. Abstract of Cited Paper (BIB008):  The smart grid is arguably one of the most complex cyber-physical systems (CPS). Complex security challenges have been revealed in both the physical and the cyber parts of the smart grid, and an integrative analysis on the cyber-physical (CP) security is emerging. This paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the CP perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. With an in-depth review of the attacks and defences, we then discuss the opportunities and challenges along the smart grid CP security. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS-based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures. 
9. Abstract of Cited Paper (BIB009):  Data injection attacks have recently emerged as a significant threat on the smart power grid. By launching data injection attacks, an adversary can manipulate the real-time locational marginal prices to obtain economic benefits. Despite the surge of existing literature on data injection, most such works assume the presence of a single attacker and assume no cost for attack or defense. In contrast, in this paper, a model for data injection attacks with multiple adversaries and a single smart grid defender is introduced. To study the defender-attackers interaction, two game models are considered. In the first, a Stackelberg game model is used in which the defender acts as a leader that can anticipate the actions of the adversaries, that act as followers, before deciding on which measurements to protect. The existence and properties of the Stackelberg equilibrium of this game are studied. To find the equilibrium, a distributed learning algorithm that operates under limited system information is proposed and shown to converge to the game solution. In the second proposed game model, it is considered that the defender cannot anticipate the actions of the adversaries. To this end, we proposed a hybrid satisfaction equilibrium - Nash equilibrium game and defined its equilibrium concept. A search algorithm is also provided to find the equilibrium of the hybrid game. Numerical results using the IEEE 30-bus system are used to illustrate and analyze the strategic interactions between the attackers and defender. Our results show that by defending a very small set of measurements, the grid operator can achieve an equilibrium through which the optimal attacks have no effect on the system. Moreover, our results show how, at equilibrium, multiple attackers can play a destructive role towards each other, by choosing to carry out attacks that cancel each other out, leaving the system unaffected. 
10. Abstract of Cited Paper (BIB010):  The smart grid (SG) paradigm is the next technological leap of the conventional electrical grid, contributing to the protection of the physical environment and providing multiple advantages such as increased reliability, better service quality, and the efficient utilization of the existing infrastructure and the renewable energy resources. However, despite the fact that it brings beneficial environmental, economic, and social changes, the existence of such a system possesses important security and privacy challenges, since it includes a combination of heterogeneous, co-existing smart, and legacy technologies. Based on the rapid evolution of the cyber-physical systems (CPS), both academia and industry have developed appropriate measures for enhancing the security surface of the SG paradigm using, for example, integrating efficient, lightweight encryption and authorization mechanisms. Nevertheless, these mechanisms may not prevent various security threats, such as denial of service (DoS) attacks that target on the availability of the underlying systems. An efficient countermeasure against several cyberattacks is the intrusion detection and prevention system (IDPS). In this paper, we examine the contribution of the IDPSs in the SG paradigm, providing an analysis of 37 cases. More detailed, these systems can be considered as a secondary defense mechanism, which enhances the cryptographic processes, by timely detecting or/and preventing potential security violations. For instance, if a cyberattack bypasses the essential encryption and authorization mechanisms, then the IDPS systems can act as a secondary protection service, informing the system operator for the presence of the specific attack or enabling appropriate preventive countermeasures. The cases we study focused on the advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA) systems, substations, and synchrophasors. Based on our comparative analysis, the limitations and the shortcomings of the current IDPS systems are identified, whereas appropriate recommendations are provided for future research efforts.

Literature Review:
Counter-measures to the aforementioned attack vectors can be categorised into three distinct classes, namely protection, detection and mitigation BIB009 . Mechanisms implemented for protecting the overall system aim to avoid external attacks before they could occur. They compromise several strategies, such as secure communication channels and protocols, re-configuration of topological information and preservation of critical information, being the latter strictly shared on a ''need-to-known'' basis. When protection mechanisms fail to prevent an attack from occurring, the second defence level is used to detect malicious activities through intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) BIB011 , physical watermarking of control inputs BIB007 , model-based BIB004 , game-theoretic BIB005 , kalman filters BIB006 or machine learning-based techniques, namely ANNs BIB008 . Using information provided by detection mechanisms, mitigation counter-measures are subsequently employed to accommodate ongoing attacks, relying on optimisation-based approaches BIB001 , BIB003 and gametheory techniques BIB010 - BIB002 .