## 2 Literature Review
### Related Work on Trust Mechanisms  
A unique method for identifying and mitigating blackhole attacks in 6 LoWPAN RPL-based wireless sensor networks (WSNs) is presented by [PERSON] et al. [31], which is referred to as Blackhole detection in RPL-based CPS (BCPS-RPL) in this paper. By preventing malicious nodes from interfering with network communication, the proposed method improves the security of CPS. A lightweight trust-enabled routing technique has been proposed by [PERSON] et al. [32] to reduce the impact of Sybil attacks in RPL-based IoT networks. The suggested method efficiently detects and prevents Sybil attacks, and hence, enhances the security and dependability of IoT networks. A taxonomy of several network attacks on CPSs, including denial of service (DoS), data manipulation, and injection attacks, has been provided by [PERSON] et al. [33]. [PERSON] and [PERSON] [34] suggest utilizing the Gini index to identify and isolate Sybil attacks in IoT. Methods for detecting and preventing greyhole attacks, such as trust-based procedures and routing protocols, have been elaborated in [PERSON] and [PERSON] [35]. The study explains the problems that may arise from greyhole attacks and suggests ways to fix them. An exhaustive taxonomy of attacks, including their characteristics, impacts, and detection/prevention methods, is presented by [PERSON] et al. [36]. To detect and prevent attacks in RPL-based networks, [PERSON] et al. [37] describe a unique IDS architecture that integrates different anomaly-detection approaches. The contribution is a workable IDS solution modified to fit the specific features and needs of IoT networks based on RPL. To improve the safety of IoT networks, [PERSON] and [PERSON] [38] present a novel trust model developed for the IoT. A new method for localization is presented by [PERSON] et al. [39] that includes features for the early detection of Sybil and wormhole attacks in IoT networks. Using RPL control messages, [PERSON] et al. [40] provide a complete overview of various routing attacks and countermeasures. Sybil attacks in WSNs with a cluster topology are detected using a lightweight trust-based framework given by [PERSON] et al. [41]. A behavioral intrusion-detection framework for WSNs is proposed by [PERSON] et al. [42]. The prospective applications and benefits of various technologies have been highlighted by [PERSON] and [PERSON] [43]. [PERSON] et al. [44] carefully examine multiple attack vectors across multiple layers of the IoT architecture, propose mitigation strategies, and highlight research gaps. In order to identify and neutralize potential security risks in sensor networks, [PERSON] et al. [45] analyze the actions of nodes at the device layer. [PERSON] and [PERSON] [46] present a strategy that combines neighbor monitoring with trust-based routing to identify and isolate blackhole attacks. [PERSON] et al. [47] offer a novel strategy that examines network traffic patterns and behavior to find probable blackhole nodes. In order to identify and remove blackhole nodes from the network, [PERSON] et al. [48] develop an enhanced approach that examines the behavior of nearby nodes. An innovative method presented by [PERSON] et al. [49] optimizes the energy usage of security measures while assuring efficient threat detection and mitigation. [PERSON] et al. [50] present the FedLearnSP framework, which enables model training without disclosing personal information. An innovative fuzzy logic-based intrusion-detection system is presented by [PERSON] et al. [51] as their contribution. The categorization and classification of different attacks according to how they affect the CPS's sensors, actuators, and communication channels are among the contributions made by [PERSON] et al. [52]. The taxonomy offers a methodical framework for comprehending and evaluating various attack types in CPSs. In their study on network congestion in WSNs, [PERSON] and [PERSON] [53] analyze the effects of various security threats, such as blackhole, wormhole, and flooding attacks. [PERSON] et al.