# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 1 Introduction  
Industry 4.0 (I4.0) is a highly appealing paradigm established originally in Germany and spread throughout Europe. It is characterized as the merger of the Internet and adaptable items (\"smart\" [1] machinery and goods) with existing digitized industry. Access to the Internet through Cyber-Physical Production Systems (CPPS) is one of the primary facilitators of I4.0, and this combination is commonly referred to as the Industrial Internet of Things (IIoT). The use of the Internet of Things (IoT) in I4.0 enables devices to be more flexible in how they interface and behave, as well as the integration of significantly more complex architectures in smart and autonomic industrial scenarios, such as components that process massive amounts of data (Big Data), and distributed computation in the Cloud/Edge layer. This synergy among technologies enables CPPS components in I4.0 to be autonomic since components can organize themselves in a network based on the context in which they are inserted, can monitor their environment, assess potential problems, and adapt, resulting in a decentralized system [1]. As time passes, CPPS tend to have more and more devices linked in a network, greater Internet access, and less human involvement [2], as CPPS is often viewed as a vital element in the notion of IIoT and the Fourth Industrial Revolution. At their foundation, CPPS are a combination of computing and physical processes that are aligned with communication networks [3]. While typical embedded systems integrate computing with real-world interactions, CPPS place a particular emphasis on inter-device communication, according to [PERSON] [3]. They are frequently integrated into feedback loops in which the cyber component (control, communication and computation in general) and physical component (the physical process, measurements by sensors and actuator impacts) are tightly coupled, just as with embedded systems, but with the added component of subsystem interactions due to their ability to communicate with each other. This increases the complexity and dynamism of such systems, which are frequently challenging to manage in centralized and prescribed techniques. A CPPS is thought to be a safety-critical system in many of its application scenarios, where a variation in conventional behavior might result in substantial economic losses and material damage, if not human lives. Over the years, several attacks have tackled Industrial Control Systems (ICS), which had a considerable impact on the physical processes. Some known examples are the _Stuxnet worm_[4], the _Ukraine SCADA attack_[5], the _Mirai attack_[6], Maroochy Water Services in Australia (2000), German Steel Mill Thyssenkrupp (2014), and Norsk Hydro Aluminum (2019) [7]. To address this problem, designing robust CPPS is critical, mainly when under equipment faults, caused by attacks or not. Furthermore, due to the rise of automation and system dynamics, increased complexity makes assaults and naturally occurring errors more difficult to detect, analyze, and resolve by a human operator. This motivates the need for CPPS to identify such anomalies and take relevant action. Furthermore, since the reaction to mitigate the impact of anomalies should be fast, CPPS have real-time needs [8]. This work proposes a method for implementing anomaly detection as a tool for detecting anomalies. The underlying physical process at the shop floor is the focus of anomaly detection, as it is frequently disregarded in typical anomaly detection problems, which consider the computer security domain [9; 10]. These objectives are further narrowed down with a solution proposal in the field of Artificial Immune Systems (AIS). The field of AIS delivers immunology-based solutions and demonstrates potential qualities such as adaptivity, scalability, and lightweight solutions. It is, however, mostly ignored by the technical community. As a result, it is critical to evaluate its suitability for addressing the difficulties outlined in anomaly detection problems.