N. R. Sunitha

Bio: 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.

Deep Learning-based Model to Fight Against Ad Click Fraud

Abstract: Click fraud is a fast-growing cyber-criminal activity with the aim of deceptively clicking on the advertisements to make the profit to the publisher or cause loss to the advertiser. Due to the popularity of smartphones since the last decade, most of the modern-day advertisement businesses have been shifting their focus toward mobile platforms. Nowadays, in-app advertisement on mobile platforms is the most targeted victim of click fraud. Malicious entities launch attacks by clicking ads to artificially increase the click rates of specific ads without the intention of using them for legitimate purposes. The fraud clicks are supposed to be caught by the ad providers as part of their service to the advertisers; however, there is a lack of research in the current literature for addressing and evaluating different techniques of click fraud detection and prevention. Another challenge toward click fraud detection is that the attack model can itself be an active learning system (smart attacker) with the aim of actively misleading the training process of fraud detection model via polluting the training data. In this paper, we propose a deep-learning based model to address the challenges as mentioned above. The model is a hybrid of artificial neural network (ANN), autoencoder and semi-supervised generative adversarial network (GAN). Our proposed approach triumphs excellent accuracy than other models.

Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Abstract: Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results.

A hybrid and effective learning approach for Click Fraud detection

Abstract: Click Fraud is a fraudulent act of clicking on pay-per-click advertisements to increase the site’s revenue or to drain revenue from the advertiser. This illegal act has been putting commercial industries in a dilemma for quite some time. These industries think twice before advertising their products on websites and mobile-apps, as many parties try to exploit them. To safely promote their products, there must be an efficient system to detect click fraud. To address this problem, we propose a model called CFXGB (Cascaded Forest and XGBoost). The proposed model, classified under supervised machine learning, is a combination of two learning models used for feature transformation and classification. We showcase its superior performance compared to other related models, and make a comparison with multiple click fraud datasets with varying sizes.

A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset

Abstract: Click fraud refers to the practice of generating random clicks on a link in order to extract illegitimate revenue from the advertisers. We present a generalized model for modeling temporal click fraud data in the form of probability or learning based anomaly detection and time series modeling with time scales like minutes and hours. The proposed approach consists of seven stages: Pre-processing, data smoothing, fraudulent pattern identification, homogenizing variance, normalizing auto-correlation, developing the AR and MA models and fine tuning along with evaluation of the models. The objective of the proposed work is to first, 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 secondly, to model different time scales separately on Auto-regression (AR) and Moving Average (MA) models. Then, we evaluate the models by tuning forecasting errors and also by minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) to obtain a best fit model for all time scale data. Through our experiments we also demonstrated that the Probability based model approach is better as compared to the Learning based probabilistic estimator model.

The knowledge complexity of interactive proof-systems

Abstract: Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

Protecting Data Privacy in Private Information Retrieval Schemes.

Abstract: Private information retrieval (PIR) schemes allow a user to retrieve the ith bit of an n-bit data string x, replicated in k?2 databases (in the information-theoretic setting) or in k?1 databases (in the computational setting), while keeping the value of i private. The main cost measure for such a scheme is its communication complexity. In this paper we introduce a model of symmetrically-private information retrieval (SPIR), where the privacy of the data, as well as the privacy of the user, is guaranteed. That is, in every invocation of a SPIR protocol, the user learns only a single physical bit of x and no other information about the data. Previously known PIR schemes severely fail to meet this goal. We show how to transform PIR schemes into SPIR schemes (with information-theoretic privacy), paying a constant factor in communication complexity. To this end, we introduce and utilize a new cryptographic primitive, called conditional disclosure of secrets, which we believe may be a useful building block for the design of other cryptographic protocols. In particular, we get a k-database SPIR scheme of complexity O(n1/(2k?1)) for every constant k?2 and an O(logn)-database SPIR scheme of complexity O(log2n·loglogn). All our schemes require only a single round of interaction, and are resilient to any dishonest behavior of the user. These results also yield the first implementation of a distributed version of (n1)-OT (1-out-of-n oblivious transfer) with information-theoretic security and sublinear communication complexity.

The Concise Encyclopedia of Statistics

Abstract: The Concise Encyclopedia of Statistics presents the essential information about statistical tests, concepts, and analytical methods in language that is accessible to practitioners and students of the vast community using statistics in medicine, engineering, physical science, life science, social science, and business/economics. The reference is alphabetically arranged to provide quick access to the fundamental tools of statistical methodology and biographies of famous statisticians. The more than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions. This reference will be an enduring resource for locating convenient overviews about this essential field of study.