Conference Papers
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Item Human-in-the-Loop Control and Security for Intelligent Cyber-Physical Systems (CPSs) and IoT(Springer, 2023) Jena, S.; Sundarrajan, S.; Meena, A.; Chandavarkar, B.R.In this era of connectivity and digitization, our appliances and equipment are becoming more intelligent and interlinked, creating an Internet of Things (IoT) that can be used to support new kinds of intelligent cyber-physical systems (CPSs). With the massive growth of CPSs, humans have become more involved within this structure. These types of CPSs/IoT systems containing humans are called human-in-the-loop cyber-physical systems (HiTLCPSs)/human-in-the-loop Internet of Things (HiTLIoT). The involvement of humans results in an external and unpredictable element that increases security concerns. This chapter proposes a unique “human-centric†framework to mitigate these issues, enabling humans as crucial components to make the CPS/IoT systems more robust and secure. Through this, the chapter tries to focus on “humans are a solution†rather than “humans are a problem.†Furthermore, the chapter looks into various components that make up the framework, how they are interlinked and how security can be bolstered with a human in the loop combined with this framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Anand, A.; Prateek Janaswamy, L.A.; Sundarrajan, S.; Gupta, M.The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast number of connected devices and the lack of security features. Attacks in edge IIoT can lead to significant damage, including data theft, malfunctioning, and privacy breaches. Federated Learning (FL) is a promising approach to detecting attacks by utilizing edge devices’ collective intelligence. FL allows devices to collaboratively learn from multiple devices’ data without centralized sharing, which preserves data privacy and reduces communication costs. However, FL has vulnerabilities that can compromise model accuracy, privacy, and security. Trusted FL is essential for collaboration among multiple edge IIoT devices while preserving data privacy and security. Trust plays a critical role in the success of FL, as edge IIoT devices must trust that the models are accurately learning and that their data is protected. To address this, we propose an FL framework that uses Federated Averaging (FedAvg) and Convolutional Neural Network (CNN) to detect attacks in edge IIoT. We also propose a mechanism to calculate trust for appropriate edge IIoT device selection by measuring each device’s (a.k.a client’s) performance during model training. The proposed edge IIoT device selection method, client selection, can fairly select clients for model training and improve trust in the entire system. Although the proposed FL approach does not outperform the centralized ResNet-18 CNN model on experimental analysis, improving its performance can be a promising solution for detecting attacks in edge IIoT. © 2023 IEEE.