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N. R. Sunitha
Researcher at Siddaganga Institute of Technology
Publications - 61
Citations - 306
N. R. Sunitha is an academic researcher from Siddaganga Institute of Technology. The author has contributed to research in topics: Digital signature & Proxy (statistics). The author has an hindex of 8, co-authored 54 publications receiving 210 citations.
Papers
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Proceedings ArticleDOI
Deep Learning-based Model to Fight Against Ad Click Fraud
G. S. Thejas,Kianoosh G. Boroojeni,Kshitij Chandna,Isha Bhatia,S. Sitharama Iyengar,N. R. Sunitha +5 more
TL;DR: A deep-learning based model is proposed that is a hybrid of artificial neural network (ANN), autoencoder and semi-supervised generative adversarial network (GAN) and triumphs excellent accuracy than other models towards click fraud detection.
Journal ArticleDOI
Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method
TL;DR: This work proposes a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods, based on a two phase process where the features are ranked and the best subset of features are chosen based on the ranking.
Journal ArticleDOI
A hybrid and effective learning approach for Click Fraud detection
TL;DR: The proposed model, CFXGB (Cascaded Forest and XGBoost), is a combination of two learning models used for feature transformation and classification that showcases its superior performance compared to other related models, and makes a comparison with multiple click fraud datasets with varying sizes.
Journal ArticleDOI
Key pre-distribution scheme with join leave support for SCADA systems
TL;DR: A matrix- based key pre-distribution scheme for SCADA systems that supports device join, leave and key update operations with less communication cost and is compared with existing schemes through simulation results and analyzing the findings.
Proceedings ArticleDOI
A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset
G. S. Thejas,Jayesh Soni,Kianoosh G. Boroojeni,S. Sitharama Iyengar,Kanishk Srivastava,Prajwal Badrinath,N. R. Sunitha,Nagarajan Prabakar,Himanshu Upadhyay +8 more
TL;DR: The objective of the proposed work is to model multi-time-scale time series data on AR/MA by relying only on time and the label without the need of too many attributes and to model different time scales separately on Auto-regression (AR) and Moving Average (MA) models.