## 2 Background and Related Work
### Deception Technology  
Deception technology is a powerful tool for enhancing cybersecurity within smart health ecosystems by deploying decoys, false information, and traps to proactively identify and mitigate cyber threats. This approach utilizes various tactics, such as honeypots, deceptive files, fake credentials, misleading network traffic, and counterfeit emails, to mislead attackers and trigger alerts when these fabricated assets are accessed or interacted with [10,11]. By detecting threats early, healthcare organizations can gain valuable insights into attackers' tactics and intentions, thereby improving their overall cybersecurity posture and enhancing incident response capabilities. However, careful implementation is crucial to ensure that the deceptive elements do not disrupt legitimate healthcare operations or introduce new vulnerabilities [27, 28, 29]. In addition to reducing false positives, deception technology engages attackers, revealing more sophisticated strategies and contributing significantly to threat intelligence. It remains an effective, legal, and ethical method for gathering cyber threat intelligence, enabling healthcare organizations to detect, understand, and mitigate threats at their early stages [10, 11, 27, 28, 29].  
Within smart health ecosystems, deception technology can be particularly effective in detecting and managing cyber-attacks. By deploying decoys and fabricated assets that replicate critical components of healthcare systems, such as IoT medical devices, patient data storage servers, sensors, and control systems, organizations can divert attackers away from real assets [10, 11]. For example, setting up a honeypot that mimics IoT device data or simulates patient monitoring systems can attract attackers and trigger alerts when they interact with these deceptive elements. Any unauthorized engagement with these  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **Survey (S) or** \\ **Research (R)** \\ \end{tabular} & \begin{tabular}{c} **Employs Deception** \\ **Technology or Not** \\ **(Yes—/, No—X)** \\ \end{tabular} & \begin{tabular}{c} **Scope of the Study** \\ **(Yes—/, No—X)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} ([PERSON] et al., 2021), [26] \\ \end{tabular} & R & X & \begin{tabular}{c} The authors present a security architecture for integrated healthcare solutions that uses Logistic and Hyperchaotic equations, as well as DNA encoding, to protect medical data. The framework also uses a Lossless Computational Secret Image Sharing (CSIS) approach to encrypt and save medical pictures in cloud-based systems, ensuring great security against attacks while being resource-economical. \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} ([PERSON] et al., 2017), [27] \\ \end{tabular} & R & \begin{tabular}{c} \(\checkmark\) \\ \end{tabular} & \begin{tabular}{c} The authors offer a honeypot that simulates a ZigBee gateway to record and analyze attack traffic, revealing that although bulk attacks were standard DDoS and bot malware, individual attackers deliberately targeted the ZigBee simulation. \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} ([PERSON] et al., 2019), [29] \\ \end{tabular} & R & X &
\begin{tabular}{c} This study introduces HealthGuard, a machine learning-based security framework designed to detect malicious activities in Smart Healthcare Systems (SHS) with high accuracy, using various detection techniques and achieving a 91\% accuracy and 90\% F1 score in evaluations. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._decoys can provide early warning of cyber threats targeting smart health infrastructure. This approach not only protects critical assets but also offers healthcare organizations and relevant stakeholders valuable insights into attackers' methods, thereby strengthening the security of smart health ecosystems.