With the advent of the smart industry, Industrial Control Systems (ICS) moved from isolated environments to connected platforms to meet Industry 4.0 targets. The inherent connectivity in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems (IDS) empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research IDSs due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their IDSs in a controlled environment. Although various ICS testbeds have been developed, researchers’ access to a low-cost, extendable, and customizable testbed that can accurately simulate ICSs and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds in which various cyber threats and network attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. Simulated components are deployable on actual hardware such as Raspberry Pis, containerized environments like Docker, and simulation environments such as GNS-3. ICSSIM also offers physical process modeling using software and hardware in the loop simulation. This framework reduces the time for developing ICS components and aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. We demonstrate ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied.

Context: Speech Emotion Recognition (SER) is a valuable technology that identifies human emotions from spoken language, enabling the development of context-aware and personalized intelligent systems. To protect user privacy, Federated Learning (FL) has been introduced, enabling local training of models on user devices. However, FL raises concerns about the potential exposure of sensitive information from local model parameters, which is especially critical in applications like SER that involve personal voice data. Local Differential Privacy (LDP) has prevented privacy leaks in image and video data. However, it encounters notable accuracy degradation when applied to speech data, especially in the presence of high noise levels. In this paper, we propose an approach called LDP-FL with CSS, which combines LDP with a novel client selection strategy (CSS). By leveraging CSS, we aim to improve the representatives of updates and mitigate the adverse effects of noise on SER accuracy while ensuring client privacy through LDP. Furthermore, we conducted model inversion attacks to evaluate the robustness of LDP-FL in preserving privacy. These attacks involved an adversary attempting to reconstruct individuals' voice samples using the output labels provided by the SER model. The evaluation results reveal that LDP-FL with CSS achieved an accuracy of 65-70%, which is 4% lower than the initial SER model accuracy. Furthermore, LDP-FL demonstrated exceptional resilience against model inversion attacks, outperforming the non-LDP method by a factor of 10. Overall, our analysis emphasizes the importance of achieving a balance between privacy and accuracy in accordance with the requirements of the SER application.