# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 4 Test Architecture and System Implementation
The proposed solution, in Figure 2, is built on a distributed architecture, consisting of multiple nodes for data processing and collection. In the following sections, each component is analyzed and briefly explained to clarify its functionality and role within the system. * Network Traffic Monitoring Sensors: Strategically placed sensors that capture and analyze network data packets, providing insights into the protocols used, IP addresses involved, source and destination ports, and other relevant attributes. * Data Preprocessing: This component prepares the collected data for analysis, performing tasks such as normalization, handling of missing values, and selection of relevant attributes. * DT System: A virtual replica of the network system that receives preprocessed data, enabling the analysis and identification of suspicious behaviors and intrusions. * Intrusion Detection Algorithms: Algorithms such as SVM and RF, implemented within the DT system, trained on labeled datasets to recognize patterns and detect abnormal behaviors associated with intrusions. * Integration with the Real Network System: The DT is connected with the real network to continuously monitor and detect suspicious activities in real time, allowing for automated responses and notifications in case of detected intrusions. * System Administrators: System administrators receive alerts and information about detected intrusions, enabling them to respond promptly to protect the network system. The operational model of the DT system involves collecting network data, preprocessing it, and subsequently analyzing it using machine learning algorithms to detect abnormal behavior, anticipate failures, and optimize performance. The system employs a combination of machine learning algorithms, including SVM and RF, for data analysis and the identification of malicious activities. These algorithms are essential for the operation of the IDS integrated into the DT, enabling the prompt and accurate detection of potential security threats. To implement the illustrated model, we will analyze three benchmark datasets for intrusion detection in network traffic: CICIDS2017 [26], 4 SICS 2015 [27], and CIC Modbus [28]. The CICIDS2017 dataset, developed by the Canadian Institute for Cybersecurity, is one of the most comprehensive and widely recognized benchmarks for intrusion detection systems (IDSs), due to its extensive representation and the diversity of simulated scenarios. It includes both benign traffic and multiple types of modern cyberattacks, such as DDoS, port scanning, and web attacks, providing a dynamic and realistic testing environment for anomaly detection algorithms [29]. Additionally, the 4 SICS 2015 dataset was selected to extend the analysis to industrial control system (ICS/SCADA) security, offering a specialized dataset for industrial networks. This dataset contains real-world traffic samples and anomalies from industrial environments, making it essential for assessing IDS performance in critical infrastructure security contexts. Additionally, we incorporate the CIC Modbus dataset, which focuses on detecting cyber threats in industrial networks using the Modbus protocol, a widely used communication standard in ICS environments. This dataset is particularly relevant for evaluating IDS models in the context of Industry 4.0, as it enables testing against attacks specifically targeting industrial communication protocols. By leveraging these three datasets, we aim to assess IDS models across a diverse range of scenarios, covering both conventional IT networks and industrial networks, thus ensuring a comprehensive evaluation within the Industry 4.0 ecosystem. Like any model in this category, this one also presents several limitations that merit consideration. The entire system critically depends on the correctness and quality of data collected from traffic monitoring sensors. Inaccurate or incomplete data can lead to errors in intrusion detection, impacting the reliability of alerts and final notifications.