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 of Physics-Based Attack Detection in Cyber-Physical Systems 
Chapter Title:  General Shortcomings 
1. Abstract of Cited Paper (BIB001):  Off-the-shelf intrusion detection systems prove an ill fit for protecting industrial control systems, as they do not take their process semantics into account. Specifically, current systems fail to detect recent process control attacks that manifest as unauthorized changes to the configuration of a plant's programmable logic controllers (PLCs). In this work we present a detector that continuously tracks updates to corresponding process variables to then derive variable-specific prediction models as the basis for assessing future activity. Taking a specification-agnostic approach, we passively monitor plant activity by extracting variable updates from the devices' network communication. We evaluate the capabilities of our detection approach with traffic recorded at two operational water treatment plants serving a total of about one million people in two urban areas. We show that the proposed approach can detect direct attacks on process control, and we further explore its potential to identify more sophisticated indirect attacks on field device measurements as well. 
2. Abstract of Cited Paper (BIB002):  Advanced false data injection attack in targeted malware intrusion is becoming an emerging severe threat to the Supervisory Control And Data Acquisition SCADA system. Several intrusion detection schemes have been proposed previously [1, 2]. However, designing an effective real-time detection system for a resource-constraint device is still an open problem for the research community. In this paper, we propose a new relation-graph-based detection scheme to defeat false data injection attacks at the SCADA system, even when injected data may seemly fall within a valid/normal range. To balance effectiveness and efficiency, we design a novel detection model, alternation vectors with state relation graph. Furthermore, we propose a new inference algorithm to infer the injection points, i.e., the attack origin, in the system. We evaluate SRID with a real-world power plant simulator. The experiment results show that SRID can detect various false data injection attacks with a low false positive rate at 0.0125%. Meanwhile, SRID can dramatically reduce the search space of attack origins and accurately locate most of attack origins.

Literature Review:
(1) Lack of Trust Models. Most papers do not describe their trust models with enough precision. Information exchanged between field devices (sensor to controller and controller to actuator in Figure 1 ) is communicated through a different channel from information that is exchanged between controllers or between controller and the supervisory control center. Papers that monitor network packets in the supervisory control network BIB002 implicitly assume that the controller (PLC) they monitor is trusted, otherwise the PLC could fabricate false packets that the monitor expects to see, while at the same time sending malicious data to actuators (what Stuxnet did). Thus, we need to monitor the communication between field devices to identify compromised PLCs in addition to monitoring supervisory control channels to identify compromised sensors or actuators. (2) No Consistent Evaluation. There is no common evaluation metric used across multiple papers. Some papers BIB001 BIB003 measure the accuracy of their anomaly detector by looking at the trade-off between the false-alarm rate and the true positive rate (metrics that are commonly used in machine-learning, fault-detection, and some intrusion detection papers), while others BIB002 argue that measuring the true positive rate is misleading in a field that has not enough attack samples, so they focus only on measuring the fidelity of their models (i.e., minimizing the false alarms). In addition, most papers focusing on false data injection for state estimation in the power grid and most papers in control theory tend to focus on developing new undetected attacks, and ignore completely the number of false alarms. Another concern is that several papers do not consider the delay in detecting an attack, and as we can see in our table, we need more research focusing on improving the time-to-detect metrics, so that attacks cannot cause irreparable damage by the time they are detected. It is important to note that most of the papers that consider stateful detection mechanisms like the CUSUM are actually using statistical tools designed precisely to minimize the time to detect an attack. As such they are implicitly including the time-to-detect as a metric.