## 2 Literature Review
### Smart Environment
#### 2.3.2 Countermeasures
Each invariant must remain valid during the whole plant operation or while the plant is in a specific condition. A functioning water treatment facility was used in an experiment to evaluate the efficiency of DAD, and it was proven to be successful at detecting sneaky and coordinated assaults. Additionally, this study [78] aims to provide a technique for enhancing operational security in a wastewater treatment plant and to demonstrate how this approach can be used in a particular setting. The motivation stems from the requirement to comprehensively understand security events or attacks on a network and information about the intensity and propagation pattern. In parallel, this study [73] suggests a cyber-security monitoring system that connects time-series event data, visually [74] depicting security occurrences. It provides a predictive prediction of probable circumstances based on established situations. Furthermore, it may assist business choices by identifying or comprehending the link between computer equipment and their business/information technology services. In recent years, the smart energy grid has steadily become the usual development trend in the world's power business. It is an improvement to the old system that incorporates technology and communication into the present grid. This results in a more efficient grid that decreases energy demand peaks and can efficiently integrate renewable resources (at naturally varying levels) into its network. To increase their security, smart grids have included physical control, data encryption, and authentication technologies. However, there is still a shortage of timely and efficient detection tools to keep the grid safe from unwanted breaches. In response to this issue, a machine-learning-based methodology for detecting smart grid DoS assaults was developed in this study [79]. The model initially gathers network data, picks features, uses principal component analysis (PCA) to reduce data dimensionality, and then employs the support vector machine (SVM) algorithm to identify abnormalities. The study exploits the attack vulnerabilities of smart meters and data servers to add data collection and intrusion detection modules between smart meters and data servers, aiming at the design structure of the smart grid. In the smart grid, real-time data capture and detection are possible. When DoS attack activity is recognized, the alarm system is initiated to handle the alert. Cyber security must be a primary priority for electric power providers installing smart meters and smart grid technologies. Despite the well-known benefits of smart meters, how and to what degree cyber assaults might disrupt smart meter functioning and remote data collecting about power use from client locations is unclear. To answer these issues, this study [80] tested a commercial-grade smart meter in a controlled lab and assessed its operational integrity under cyber-attack situations. In addition, the false data injection attack (FDIA) is a way of disrupting the security of the power system based on meter measurement. FDIA detection researchers are now focused on detecting its existence. FDIA location information is also critical for power system security. Finally, in this study [81], identifying the meter's FDIA is seen as a multi-label classification task. Each label denotes the current condition of the respective meter. The multi-label decision tree approach is used as the classifier in the ensemble model to discover the precise position of the FDIA. This approach does not need power topology information or statistical knowledge assumptions. The suggested method's performance is validated by numerical tests using the IEEE-14 bus system.