## 2 Literature Review
### Smart Environment
#### 2.3.2 Countermeasures  
Although prevention and monitoring techniques may lower the risk of cyber assaults, the residual risk in vital infrastructures or services might still be unacceptable. Resilience, or a system's capacity to survive evil occurrences while retaining adequate operation, is a crucial attribute of such systems. While numerous resilience indicators have previously been proposed, there are limited experimental data on the cyber security of CPSs. This study [73] aims to provide a model-free, quantitative, and general-purpose assessment approach for extracting resilience indices from data sources such as system logs and process logs. The authors evaluate four resilience indices from a broad range using an actual wastewater treatment plant model and modeling assaults that interfere with a vital feedback control loop. The findings reveal that although the selected indexes varied in their behavior and susceptibility to certain assaults, they can all summarize and extract valuable information from large system logs. The proposed method includes deriving performance indicators from observable data without understanding the system dynamics. Waste management and recycling are critical to remaining sustainable and clean in contemporary metropolitan settings. Solid waste management, disposal, and recycling are all difficulties in many major cities across the globe. Combining IoTs with deep learning provides a modular approach for data classification and real-time analysis. This article [74] depicts an effective smart waste management and classification system based on IoT and deep learning. The study proposes a microchip-based trash can with a fast waste collection system. IoT provides real-time data control in the recommended data-monitoring system. Smart trash management and categorization include a convolutional neural-network-based algorithm. This waste-collection facility will use trash classification to increase recycling. This system offers waste collection, management, and categorization. Modern technologies enable water distribution systems (WDSs) to provide improved water supply, storage, distribution, and recycling services. They help with real-time monitoring, automation, and management. However, the limitations of these technologies expose the WDS to cyber-physical threats. The primary aim of cyber-physical assaults is to interrupt regular operations and tamper with crucial data, negatively influencing the WDS. As a result, it is critical to design and deploys solutions to improve WDS security by detecting and mitigating cyber-physical assaults. The authors of this research [75] thoroughly investigate typical cyber-physical assaults and common detection strategies for the WDS. They contrast assaults and detection approaches, focusing on concepts, methodologies, evaluation findings, benefits, limits, etc. The increasing number of successful and attempted attacks on critical infrastructures, such as power grids and water treatment plants, has resulted in an urgent need to develop and implement methods for detecting such attacks, which state actors or insiders in the targeted organization frequently carry out. This research [76] aims to provide a case study of an infected wastewater treatment plant (WTP) that used a live memory dump acquisition Imager. The forensic carving procedure is removed in bulk, and features are extracted from the memory dump. In addition, this study [77] emphasizes one method to identify assaults that compromise one or more actuators and sensors in a plant by successfully infiltrating the plant's communication network or accessing the plant computers directly. This technique, known as distributed attack detection (DAD), may detect assaults in real time by spotting irregularities in the behavior of the physical process in the plant. The use of monitors that are actual implementations of the invariants obtained from the plant architecture is how anomalies are discovered.