## 1 Introduction
### Related Work  
Most WSNs are made of basic sensors and hardware components, so attackers, who are knowledgeable about the technology and protocols, can easily attack these devices by accessing the sensors' communication channels [16]. A framework for anomaly problems in Multiple Internet of Things (MIoT) networks provides a standardized approach to study and classify anomalies, which depend on several aspects, for instance, the distances between nodes, the size of the IoT networks, and the degree of centrality and closeness of the anomalous nodes. An example that exhibits the use of such framework in a smart lighting system scenario can be found in [17]. Our proposal's approach is applicable to real problems related to the trust system for monitoring clean and reliable information. The work cited in [18] exposes the industrial data's low quality and proposes a preprocessing step to resolve the corrupted and unlabeled training data. This hybrid framework provides a robust model for process monitoring and model identification, and its efficiency is demonstrated with synthetic examples and real cases of industrial processes. Unfortunately, in industrial systems, the assumption is violated due to the harsh operating environment, as stated in [19]. With the increasing complexity and scale of industrial production, the data for supervisory control and acquisition of the industrial production process are often collected from different machines, stations, or modes of operation, and, as a consequence, the information can be easily compromised. To improve platform failure diagnosis for industrial processes, the authors propose a reconstruction-based approach to isolate the latent source of failure from the sample level to the variable level. Likewise, in [20], the authors highlight the importance of cyber-physical systems, which must function reliably against unforeseen failures and malicious external attacks. These systems, widely used today, require a highly reliable detection and identification of centralized and distributed attacks. Concerning current implementations such as Machine Learning [21], systems must be able to provide real-time solutions that maximize the use of resources in the network, thus increasing the useful life of the network. It is here that WSNs are used in applications, such as biodiversity and ecosystem protection, surveillance, climate change monitoring, and other military applications. Machine learning can play a relevant role in these networks, including intrusion detection. In addition, WSNs can be used for gathering data needed by other support systems, such as the attack detector, which can deliver information about the position, type of intrusion, and area affected. [PERSON] et al. [22] propose a method for preventing the network's disruption under jamming attacks by bypassing the jammed zone and selecting alternative paths based on a maximum link quality criterion. They present a performance analysis of the proposed method with theoretical and simulation-based approaches. The simulation-based study considers the following performance metrics: packet delivery rate, network throughput, transmission energy, node lifetime, and network lifetime. The work by [PERSON] et al. [23] presents a defense mechanism for smart grid monitoring applications based on WSN infrastructure. Their mechanism can detect and isolate various attacks such as the denial of sleep, which is a category of denial of service attack where the network can experience effects similar to the consequences caused by jamming. They study the power consumption of the nodes for networks with cluster-tree topology and subject to jamming-like attacks. The simulated nodes use the energy consumption models of the Chipcon CC2420 radio module and Atmega128L Micro-controller (MCU). The article by [PERSON] et al. [24] introduces a technique for WSN with clustering-based topologies. They propose the use of timestamps to detect the presence of malicious nodes, which generate a jamming attack.