## 4 Cybersecurity in WSN  
WSNs represent a crucial technology in various applications ranging from environmental monitoring to healthcare and industrial automation. As these networks become increasingly integral to our interconnected world, the need for robust cybersecurity measures becomes paramount [35]. DDoS attacks in WSNs have emerged as a critical concern [36] due to the unique characteristics of these networks. WSNs are designed to operate in resource-constrained environments where sensor nodes are equipped with limited computational power, energy resources, and communication bandwidth. The cooperative nature of sensor nodes, which collaborate to collect and disseminate information, renders the network susceptible to malicious exploitation. DDoS attacks in WSNs typically involve the coordinated inundation of the network with a high volume of spurious data, thus causing congestion, communication bottlenecks, and resource exhaustion [37]. The consequences of such attacks are far-reaching, as they can disrupt the normal functioning of the WSN, compromise data integrity, and render the sensor nodes ineffective in fulfilling their designated sensing and communication tasks. Given the inherent vulnerabilities of WSNs, addressing the challenges posed by DDoS attacks requires a multidimensional approach. The design of energy-efficient and resilient communication protocols is crucial to ensuring the serviceability of the network under adversarial conditions. Additionally, strategies for secure node authentication, anomaly detection, and adaptive routing algorithms are being explored to fortify WSNs against the evolving landscape of DDoS threats. As WSNs continue to find applications in diverse domains such as environmental monitoring, healthcare, and industrial automation, safeguarding their functionality from malicious disruptions becomes paramount for ensuring the reliability and efficacy of these critical systems. The Enhanced Random Forest Classifier Achieving the Best Execution Time (ERF-ABE) algorithm [38] has emerged as a specialized solution meticulously crafted to safeguard the intricate landscape of the Internet of Medical Things (IoMT) from the menace of DDoS and Delay attacks. Unveiling a nuanced approach, the algorithm introduces the ERF-ABE. This classifier is ingeniously designed to not only identify but also effectively counteract these sophisticated attacks prevalent in healthcare scenarios. The algorithm's design involves careful consideration, as it adeptly navigates the challenging terrain of the IoMT, where data integrity and patient safety are paramount. ERF-ABE demonstrates a remarkable equilibrium between high accuracy and sensitivity, which is critical for discerning subtle attack patterns, while concurrently prioritizing minimized execution time. Notably, the incorporation of Principal Component Analysis (PCA) further enhances the algorithm's prowess by streamlining feature selection, reducing dimensionality, and optimizing overall performance. In its role as a sentinel for IoMT networks, the ERF-ABE algorithm stands as a beacon of resilience against cyber threats, thus ensuring the robustness and reliability of medical data transmission. The research's meticulous delineation of the algorithm's features, coupled with its emphasis on fortifying the IoMT landscape, offers valuable insights for both current and future endeavors in the dynamic realm of medical cybersecurity. As the healthcare sector continues to evolve, the ERF-ABE algorithm paves the way for novel approaches and sets a precedent for advanced, efficient, and secure solutions in the protection of sensitive medical data. On the other hand, there are common security algorithms that are considered competitors to the ERF-ABE. The following are among the algorithms that are considered common in terms of security:  
* Logistic Regression (LR): A statistical technique for binary categorization.