Conference Papers

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

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Author: search.filters.author.Singh, N.Has files: No

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    Automated versus Manual Approach of Web Application Penetration Testing
    (Institute of Electrical and Electronics Engineers Inc., 2020) Singh, N.; Meherhomji, V.; Chandavarkar, B.R.
    The main aim of this work is to find and explain certain scenarios that can demonstrate the differences in automated and manual approaches for penetration testing. There are some scenarios in which manual testing works better than automatic scripts/vulnerability scanners for finding security issues in web applications. In some other scenarios, the opposite may be true. The concepts of various web application vulnerabilities have been used for testing, including OWASP1Open Web Application Security Project; online community dedicated to web security Top 10, using both manual and automatic approaches. Automation tools and scripts have been used and tested to see what could potentially go wrong if attackers exploit such vulnerabilities. Also, certain scenarios have been used which determine whether one approach is better than the other for finding/detecting security issues in web applications. Finally, the work concludes by providing results in the form of pros-and-cons of both approaches, which it realises after carrying this out. © 2020 IEEE.
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    Hybrid Model of Multifactor Analysis with RNN-LSTM to Predict Stock Price
    (Springer Science and Business Media Deutschland GmbH, 2022) Singh, N.; Mohan, B.R.; Naik, N.
    Prediction on the stock market is one of the most difficult tasks to do in real life. There are so many aspects on which the stock market depends—physical factors versus psychological, rational, and irrational behavior, etc. Proposed research work consists of different aspects on which stock markets are based on. It consists of three models to forecast a stock price on State Bank of India (SBI) stock data. In the current research, we proposed a hybrid model followed by recurrent neural network-long short-term memory (RNN-LSTM) to predict a next-day closing price of SBI. A hybrid model is the combination of two different aspects related to the prediction of stock price. The first technique used other companies’ stock data to predict the target company’s next-day closing price. Other companies lie in the same sector so that they are correlated to each other. For training and testing, we have used multilayer perceptron (MLP) regression model. It is a neural network model in deep learning. The second technique is to predict the stock price of an SBI company using historical data of the target company followed by the auto-regressive integrated moving average—gated recurrent unit (ARIMA-GRU) model. ARIMA-GRU model is a combined model which gives better accuracy for predicting stock price data. In the hybrid model, we take the result of both the models as an input. This paper aims to compare the proposed hybrid model with other two single-aspect models on which stock price depends and proves in terms of accuracy that the hybrid model of all aspects gives better results in comparison to single-aspect models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Experimental Modal Analysis Using Impact Hammer Testing with Random Forest-Based Prediction of Magnetorheological Elastomer Dynamics
    (Institute of Physics, 2025) Shenoy, P.; Kamath, N.; Pawar, K.; Singh, N.; Soundarya; Afnan, S.; Mayya D, S.
    This study presents a novel integration of impact hammer-based experimental modal analysis with Random Forest Regression (RFR) to rapidly characterise the frequency-domain dynamic behaviour of Carbonyl Iron Particle (CIP)-based Magnetorheological Elastomers (MREs) under varying magnetic fields. Using only applied current and excitation frequency as input features, the RFR model predicts FRF amplitude, phase, and coherence with R2 values exceeding 0.96 across both low-frequency (0-70 Hz) and high-frequency (> 70 Hz) regimes. This hybrid experimental-computational framework significantly reduces the number of repeated tests required, enabling faster parametric studies and paving the way for real-time, AI-enhanced tuning of smart vibration isolation systems. © Published under licence by IOP Publishing Ltd.