BITS Faculty Publications
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Item Unsupervised machine learning framework for discriminating major variants of concern during COVID-19(ARXIV, 2022-10) Agarwal, VintiDue to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from different dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for a particular variant (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We conclude that the proposed framework can effectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future.Item PACE: Platform for Android Malware Classification and Performance Evaluation(IEEE, 2019) Agarwal, VintiAndroid malware has become the topmost threat for ubiquitous and useful Android eco-system. Multiple solutions leveraging big data and machine learning capabilities to detect android malware are being constantly developed. Too often, many of these solutions are either limited to the research output or remain isolated and unable to reach to end-users or malware researchers. In this paper, we propose, PACE, a unified solution to offer open and easy implementation access to several machine learning-based Android malware detection techniques that make most of the research in this domain reproducible. The benefits of PACE are offered using three interfaces i.e. through REST API, Web Interface and ADB interface. Multiple interfaces enable users with different expertise such as IT administrator, security practitioners, malware researcher, etc. to avail its offered services. A community-accepted dataset is used for testing of all the techniques to provide a better comparison of performance. A prototype of the proposed platform is introduced and our vision is that it will help malware analysts to tackle challenges and reduce the amount of manual work.Item PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting(MDPI, 2020-01) Agarwal, VintiAndroid malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated and incapable of reaching end users or malware researchers. An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based Android malware detection techniques, that makes most of the research reproducible in this domain. The benefits of PACE are offered through three interfaces: Representational State Transfer (REST) Application Programming Interface (API), Web Interface, and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different expertise such as IT administrators, security practitioners, malware researchers, etc. to use their offered services. In this paper, we propose PACER (Platform for Android Malware Classification, Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence and reporting functionality for the end-user device through the ADB interface. A prototype of the proposed platform is introduced, and our vision is that it will help malware analysts and end users to tackle challenges and reduce the amount of manual workItem Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering(Wiley, 2015-04) Agarwal, VintiThe tremendous amount of content generated on online social networks has led to a radical paradigm shift in the interest of people to group friends dynamically and share content selectively. At large, social networking sites (e.g. Google+, Facebook, Twitter, etc.) offer users with various controls over categorizing their family members, friends, colleagues, etc. into one or more ‘circles’ that they want to share content with. However, it is typically impossible to design social circles in large networks and update their number and size, whenever networks grow. Aiming at predicting the dynamics of formation and evolution of social circles, we performed several experiments on ground-truth data, and found that studying patterns of network and profile features at individual level rather than studying circle as a whole can greatly enhance the understanding of social circles development in online social networks. In this review, we first present a comprehensive study of the structural behavior of circles, and then build an observation that within every circle there exist some key elements, termed as ‘Node of Creations (NoCs)’, which play an important role in the development, survival, and evolvability of circle structures. We, therefore, propose a Genetic Algorithm–based framework to determine these key elements (NoCs) and differentiate Ego networks into non-overlapping, hierarchically nested as well as overlapping circles by leveraging knowledge from the identified patterns in order to assist K-means clustering. We have performed our experiments using Facebook and Twitter datasets and the experimental results clearly demonstrate the effectiveness of our scheme. WIREs Data Mining Knowl Discov 2015, 5:113–141. doi: 10.1002/widm.1150