Dissertation and Thesis

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Subject: search.filters.subject.Feature Extraction

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    Dimensionality Reduction for Data Visualization and Classification
    (Indian Statistical Institute, Kolkata, 2023-07) Das, Suchismita
    In this thesis, we identify a few gaps in the existing methods of dimensionality reduction for data visualization and classification and propose some solutions to those as summarized below. Most of the data visualization methods do not learn any explicit function to project high dimensional data to a lower dimension. To overcome the difficulty associated with the absence of an explicit map, in Chapter 2, we propose a framework to estimate explicit maps for data visualization in a supervised setting. The quality of output of any regression-type system depends on the quality of the target data. However, even for simple data, sometimes the target data for visualization may be severely distorted. We present a framework that can significantly correct such distortions in the output for data visualization. For any supervised data visualization method the availability of target data is indispensable, which limits the applicability of such methods. Another problem with most of the methods is that they always produce some output given any input, even when the test input is far from the “sampling window” of the training data. In Chapter 3, using a fuzzy rule-based system (FRBS), we propose an unsupervised approach to learn explicit maps for data visualization that addresses the previously mentioned issues. The proposed method can project out-of-sample instances in a straightforward manner. It can also refuse to project an out-of-sample instance when it is far away from the sampling window of the training data. We have demonstrated the generality of the proposed framework using different objective functions for learning the FRBS. When a data set has significant differences between its class and cluster structure, features selected considering only the discrimination between classes would lead to poor clustering performance. Similarly, features selected considering only the preservation of cluster structures would lead to poor classification performance. To address this issue, in Chapter 4, we propose a neural network-based feature selection method that focuses both on class discrimination and structure preservation. For large datasets, to reduce the computational overhead we propose an effective sample-based method. When a data set has class-specific characteristics, selecting a single feature subset for the entire data set may not characterize the data correctly, although the classifier performance may be satisfactory. To address this, in Chapter 5, we have proposed class-specific feature selection (CSFS) schemes using feature modulators embedded in a fuzzy rule-based classifier. The parameters of the modulators are tuned by minimizing a loss function comprising classification error and a regularizer to make the modulators completely select or reject features in a class-specific manner. Our method is free from the hazards of most of the existing CSFS methods, which suffer due to the use of onevs- all strategy. We have extended the CSFS scheme so that it can monitor class-specific redundancy between selected features. We note here that data from a particular class may have multiple clusters and different clusters may be effectively defined by different subsets of features. To address this, finally, our CSFS framework is generalized to a rule-specific feature selection framework.
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    Feature Extraction And Detection of Malicious URLs Using Deep Learning Approach
    (Indian Statistical Institute,Kolkata, 2019-07) Kushwaha, Rajni
    Phishing Attack is one of the cyber bullying activity over the internet. Most of the phishing websites try to look similar to legitimate websites, their web content and URL features memic the legitimate URL. Due to emerging new techniques, detecting and analyzing these malicious URL is very costly due to their complexities. Traditionally, black and white listing is used for detection, but these technique was not good for real time.To address this, recent years have witnessed several e orts to perform Malicious URL Detection using Machine Learning. The most popular and scalable approaches use lexical properties of the URL string by extracting Bag-of-words like features, followed by applying machine learning models such as SVMs, Randon Forest etc. Various machine learning and deep learning techniques are used to improve generalization of malicious URLs.These approaches su er from several limitations: (i) Inability to e ectively capture semantic meaning and sequential patterns in URL strings; (ii) Requiring substantial manual feature engineering; and (iii) Inability to handle unseen features and generalize to test data. To address these Limitation, In this dissertation work, we are focused to built the real time and language independent phishing detection model by analyzing the anatomy of the URLs using deep learning techniques. To achieve this, we rstly try to nd static and dynamic features manually using some previous work. After getting the featured valued data set, we tried to nd the lexical features of Url using CNN which has both characters and words of the URL String to learn the URL embedding. After that we merge features which we manually selected and features learned from CNN and applied on Bi-LSTM Model to keeps the sequence information of URL. A hybrid model of CNN (convolution neural network model) and Bi-directional LSTM(Long Short Term Memory) are to achieve the goal. Our model analyze the URL without accessing the web content of websites. It eliminates the time latency.