Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Cryptanalysis of a remote user authentication protocol using smart cards(IEEE Computer Society help@computer.org, 2014) Madhusudhan, R.; Kumar, R.S.Remote user authentication using smart cards is a method of verifying the legitimacy of remote users accessing the server through insecure channel, by using smart cards to increase the efficiency of the system. During last couple of years many protocols to authenticate remote users using smart cards have been proposed. But unfortunately, most of them are proved to be unsecure against various attacks. Recently this year, Yung-Cheng Lee improved Shin et al.'s protocol and claimed that their protocol is more secure. In this article, we have shown that Yung-Cheng-Lee's protocol too has defects. It does not provide user anonymity; it is vulnerable to Denial-of-Service attack, Session key reveal, user impersonation attack, Server impersonation attack and insider attacks. Further it is not efficient in password change phase since it requires communication with server and uses verification table. © 2014 IEEE.Item Prediction of Remaining Useful Life in MEMS Devices Using a Stacking Model Approach(Institute of Electrical and Electronics Engineers Inc., 2025) Sanjay, M.; Kumar, R.S.Accurate estimation of the Remaining Useful Life (RUL) of Micro-Electro-Mechanical Systems (MEMS) devices is essential for enhancing predictive maintenance in industrial settings. This paper proposes a robust stacking ensemble model for Remaining Useful Life (RUL) prediction of MEMS devices, integrating sensor and mechanical component data. Publicly available RUL datasets were augmented using Generative Adversarial Networks (GANs), ensuring diverse input data. Key algorithms-Bayesian Ridge, Random Forest, and XGBoost-were identified using Lazy Predict and combined with a Support Vector Regressor (SVR) as the meta-learner. Hyperparameter optimization was performed using the Deep Deterministic Policy Gradient (DDPG) algorithm, offering enhanced efficiency for large datasets and dynamic retraining. The model demonstrated superior performance based on RMSE and R2 metrics compared to traditional methods. Deployment was achieved via a Flask web application integrated with a CI/CD pipeline using GitHub Actions and Docker, ensuring scalability and reproducibility. This research contributes a scalable and efficient framework for advancing predictive maintenance in industrial automation. © 2025 IEEE.