Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Has files: NoSubject: search.filters.subject.Block-chain

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Private and Consortium Blockchain-based Authentication Protocol for IoT Devices Using PUF
    (Korean Institute of Communications and Information Sciences, 2024) Cunha, T.B.D.; Manjappa, K.
    In this work, a static random access memory-physical unclonable function (SRAM-PUF) based device security framework is proposed which uses the trending blockchain technology for securing the device credentials. The proposed framework produces a unique fingerprint called PUF key for each device based on its hardware characteristics which will act as an authenticating parameter for the devices during the authentication and re-authentication phase. The proposed work uses both consortium and private blockchains for storing device credentials and authentication, unlike the current trend of using either a secured database or only a public blockchain. The consortium blockchain is used for first-time authentication, while the private blockchain is used for repeated authentication which saves the time incurred in accessing the consortium blockchain during repeated authentication. The proposed protocol also includes mutual authentication between the entities involved and thus provides dual security (device authentication and mutual authentication) to the proposed protocol making the system more secure and robust against attacks. Security analysis of the proposed protocol is done using the Scyther tool and the protocol is also theoretically proven to be stable under various attacks using threat analysis and the real-or-random model (ROR). The performance analysis of the protocol is done by analyzing the computation and communication cost of the proposed protocol against other state-of-the-art protocols. Further, the proposed protocol is also evaluated in the blockchain testbed which includes Raspberry PI and Arduino components. The results conveyed that the introduction of a private blockchain reduces the time incurred in the device re-authentication. © 2024 KICS.
  • No Thumbnail Available
    Item
    Enhancing Money Laundering Detection in Bank Transactions using GAGAN: A Graph-Adapted Generative Adversarial Network Approach
    (Springer Science and Business Media Deutschland GmbH, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.
    The past decade has witnessed profound transformations in the financial sector, driven by the integration of cutting-edge technologies into its core operations. Consequently, banks are increasingly utilizing technologies such as artificial intelligence (AI), blockchain, and big data analytics to offer personalized services, streamline transactions, and improve risk management, enabling the development of new financial products and services that cater to the diverse and evolving needs of customers. Despite these benefits, the banking landscape has also brought about complex challenges, particularly in the fight against money laundering. Money laundering remains a significant threat to the integrity of financial systems, as criminals exploit digital advancements to conceal illicit activities. The growing complexity of digital transactions and the increasing volume of financial data have made detecting and preventing money laundering more challenging than ever. Existing AI-based solutions, while effective to some extent, often grapple with class imbalance issues. This paper addresses the challenge by introducing a novel model named GAGAN (Graph Attention Generative Adversarial Network) and enhances the detection of money laundering activities in bank transactions. The proposed model further addresses the issue of class imbalance, by incorporating Conditional Generative Adversarial Network (cGAN) and Graph Attention Networks (GAT). The GAT classifier is then employed to accurately classify transactions, leveraging attention mechanisms to focus on the most relevant parts of the graph. Empirical results reveal that the proposed model achieves impressive performance metrics, with an accuracy of 98.62%, precision of 98.10%, recall of 98.92%, F1 score of 98.49%, AUC-ROC of 0.99, and a MCC score of 0.991. These results underscore the efficacy of the model in accurately identifying money laundering transactions, showcasing its potential as a robust tool for financial crime detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.