Thejas Gubbi Sadashiva
School of Computing and Information Sciences
Thejas Gubbi Sadashiva is a Ph.D. candidate at the School of Computing and Information Sciences (SCIS), Florida International University (FIU). He is working under the supervision of Dr. S.S. Iyengar and external member Dr. N. R. Sunitha in the Discovery Lab. He received his Bachelor’s degree and Master’s degree in Computer Science and Engineering (M.Tech) from India. He worked as a trainee for one year at Defence Research and Development Organization/Electronic and RADAR Development Establishment (DRDO/LRDE), India. His areas of research include Machine Learning, Cybersecurity, Human-Computer Interaction (HCI), and Performance Optimization using parallel computing. Thejas worked as an Assistant Professor for five years at Siddaganga Institute of Technology (SIT) before commencing his Ph.D. at FIU, and he also served as a Memorandum of Understanding (MOU) activities coordinator between SIT and FIU. Thejas is a recipient of Dissertation Year Fellowship Award, FIU School of Computing and Information Sciences travel Awards, and FIU Graduate & Professional Student Committee (GPSC) travel grant. Thejas’s publications include one book chapter, 19 research papers in top conferences and journals and has secured an Indian Patent. He has authored the book entitled “Dr. S. S. Iyengar’s four decades of contribution towards research and educational enhancement between USA & India”. Thejas has served as a mentor, and resource person in National Science Foundation (NSF) supported program Research Experience for Teachers (RET) 2017-19 at FIU. He has also served as a co-ordinator in Science without Borders summer program 2017-19 at Discovery Lab, FIU.
Thanks and Regards,
Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness — while existing feature selection techniques address this issue, they have their own performance limitations.
By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches.