Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item This paper presents a design & implementation methodology for FPGA based MIL-STD-1553 Remote Terminal Sub-System using DDC's BU-61580 device. The interesting part of this paper is that it presents a design of non-processor remote terminal sub-system. The glue logic is put on one FPGA of 176 pins which initializes the BU-61580 in RT mode at power-on. The FPGA design was done using Viewlogic & Actel packages. The simulation has shown the correct results which was then followed by the implementation. In this paper, the design is presented for 9 sensors among which 5 are analog and 4 are digital. For the purpose of testing the circuit, the analog & digital sensors are simulated through the computer.(ISRO Satellite Centre, Design and implementation of FPGA based non-processor MIL-STD-1553 remote terminal sub-system using DDC's BU-61580) Bhagyalakshmi, K.; Ramachandra, G.; Agrawal, V.K.; Subbanna Bhat, P.; Philar, S.R.1999Item Deep learning for network security: a novel GNN-LSTM-based intrusion detection model(Inderscience Publishers, 2025) Agrawal, V.K.; Rudra, B.The rise in the use of IoT devices in daily life has led to an increase in attacks, making it crucial to protect our devices and information. Intrusion detection system (IDS) is vital in preventing potential attacks. This paper presents a novel IDS architecture using a hybrid GNN-LSTM-based approach. Graph neural network (GNN) is used to extract information from graph-based data, while long short-term memory networks (LSTM) helps learn patterns in the extracted embeddings due to its ability to learn from long-term dependencies in data. We introduce a new mechanism for edge-classification using GNN, eliminating the need for node feature aggregation, followed by edge embedding classification using the LSTM model. We also provide a detailed comparison of our proposed model with state-of-the-art machine learning (ML) and deep learning (DL) algorithms for intrusion detection, demonstrating high accuracy. © © 2025 Inderscience Enterprises Ltd.