# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
Additionally, it is important to clarify the processing logic and how trust metrics influence trust evaluations [31]. In this study, we made two key contributions to improve the explainability of our model. First, we conducted a semi-formal analysis to identify potential modifications to the CA algorithm's processing logic that could enhance its performance. Then, we carried out a series of simulation experiments to demonstrate the effectiveness of these improvements. **User Acceptance**. The acceptance of a trust model depends on factors such as Quality of Service (QoS), quality of experience (QoE), and individual user preferences [31]. It can be assessed by gathering user feedback through questionnaires. However, the user acceptance of the CA model has not yet been evaluated. Overall, the CA model satisfies decentralization, subjectivity, integrity, and transparency, but several aspects require further enhancement and evaluation. Future research should focus on scalability, as the model's performance in large-scale environments with a high number of nodes remains untested. Additionally, while trust data are stored locally, their impact on system overhead and storage efficiency needs further investigation. Accuracy assessment using standard metrics like Precision, Recall, and the F1-score is also necessary to validate its reliability. In terms of robustness, although the model resists false recommendations, its resilience against insider attacks requires deeper analysis. Privacy preservation remains an open challenge, particularly in safeguarding trustor identity and feedback transmission, which could benefit from encryption or blockchain-based solutions. Finally, user acceptance has yet to be assessed, making it essential to evaluate the model's adoption based on Quality of Service (QoS) and quality of experience (QoE). Addressing these challenges will strengthen the CA model's effectiveness and applicability in dynamic environments. To the best of our knowledge, no other models have undergone such a comprehensive evaluation. ## 8 Trust-Related Attacks

In this section, we examine the CA model's resilience against the most common trust-related attacks identified in various research studies [2, 16, 21, 22, 23, 25, 31, 34, 35, 36, 41, 42]. A malevolent node is typically defined as a socially uncooperative entity that consistently seeks to disrupt system operations [36]. Its primary objective is to provide low-quality services to conserve its own resources while still benefiting from the services offeredby other nodes in the system. Malicious nodes can employ various trust-related attack strategies, each designed to evade detection through different deceptive tactics [25]. **Malicious with Everyone (ME). ** In this attack, a node consistently provides low-quality services or misleading recommendations, regardless of the requester. This is one of the most fundamental types of attacks. To counter the ME attack, the CA approach could incorporate a contract-theoretic incentive mechanism. This mechanism would reward honest service providers with utility gains while penalizing dishonest service providers with utility losses, similarly to the approach in [19], by awarding or deducting credit coins. **Bad-mouthing Attack (BMA). ** In this attack, one or more malicious nodes deliberately provide bad recommendations to damage the reputation of a well-behaved node, reducing its chances of being selected as service provider. However, in the CA approach, agents do not exchange trust information through recommendations. Since nodes cannot act as recommenders, our approach is inherently immune to BMA. Most studies assume that service requesters are honest, but as noted in [17], service requesters can also be \"ill-intended\" or \"dishonest\", deliberately giving low ratings to a service provider despite receiving good service. Additionally, some service requesters may be \"amateur\" and incapable of accurately assessing service quality. This represents a specific type of bad-mouthing attack, against which our approach is also resilient.