Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Trust Management

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    HTmRPL++ : A Trust-Aware RPL Routing Protocol for Fog Enabled Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2020) Subramanian, N.; Mitra, S.; Martin, J.P.; Chandrasekaran, K.
    With the proliferation of Fog computing, computation is moved to edge devices and is not based on a purely centralized approach. In a Fog computing network, the network topology is dynamic. New nodes will join and leave. One of the major issues in Fog computing is trust. Trust is the level of assurance that an object will behave in a satisfactory manner. The Routing Protocol for Low Power and Lossy Networks (RPL) is a protocol used for routing in IoT networks. RPL provides meager protection against routing or other forms of attacks. The resource-constrained nature of Fog nodes prevents the use of heavyweight cryptographic algorithms to achieve secured communication. A lightweight mechanism is thus essential to impart security in Fog-IoT networks. Trust analysis provides a behavior-based analysis of entities in the system with the power to predict future behavior. In this paper, a lightweight Recommendation based Trust Mechanism is proposed to impart security to RPL. © 2020 IEEE.
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    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.