Faculty Publications
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Item Quality and business offer driven selection of web services for compositions(2009) D’Mello, D.A.; Ananthanarayana, V.S.The service composition makes use of the existing services to produce a new value added service to execute the complex business process. The service discovery finds the suitable services (candidates) for the various tasks of the composition based on the functionality. The service selection in composition assigns the best candidate for each tasks of the pre-structured composition plan based on the non-functional properties. In this paper, we propose the broker based architecture for the QoS and business offer aware Web service compositions. The broker architecture facilitates the registration of a new composite service into three different registries. The broker publishes service information into the service registry and QoS into the QoS registry. The business offers of the composite Web service are published into a separate repository called business offer (BO) registry. The broker employs the mechanism for the optimal assignment of the Web services to the individual tasks of the composition. The assignment is based on the composite service providers's (CSP) variety of requirements defined on the QoS and business offers. The broker also computes the QoS of resulting composition and provides the useful information for the CSP to publish thier business offers. © 2009 Springer Berlin Heidelberg.Item Web sessions clustering using hybrid sequence alignment measure (HSAM)(Springer-Verlag Wien michaela.bolli@springer.at, 2013) Poornalatha, G.; Raghavendra, S.R.Web usage mining inspects the navigation patterns in web access logs and extracts previously unknown and useful information. This may lead to strategies for various web-oriented applications like web site restructure, recommender system, web page prediction and so on. The current work demonstrates clustering of user sessions of uneven lengths to discover the access patterns by proposing a distance method to group user sessions. The proposed hybrid distance measure uses the access path information to find the distance between any two sessions without altering the order in which web pages are visited. R2 is used to make a decision regarding the number of clusters to be constructed. Jaccard Index and Davies–Bouldin validity index are employed to assess the clustering done. The results obtained by these two standard statistic measures are encouraging and illustrate the goodness of the clusters created. © 2012, Springer-Verlag.Item Enhanced lbs discovery in a decentralized registry based web services environment(Rinton Press Inc. sales@rintonpress.com, 2014) D'Souza, M.; Ananthanarayana, V.S.Location Based Services (LBS) is the most happening thing in the mobile industry today. Everybody is trying to generate revenue from location based services. Mobile phone manufacturers are developing new smart phones every day and network providers are offering high speed data connections. Several LBS providers and applications are available in the market but the major problem is service provider dependency. This paper gives an overview of a decentralized registry based architecture using web services technology which facilitates dynamic discovery, interoperability and provider independence. The web services technology uses UDDI registry service for publishing and discovering services but the discovery results obtained are not reliable as the service discovery considers only static service description. This paper contributes to enhancing LBS discovery by considering service dynamics and expanding LBS discovery process to neighboring locations. © Rinton Press.Item A bio-inspired, incremental clustering algorithm for semantics-based web service discovery(Inderscience Enterprises Ltd., 2015) Kamath S?, S.; Ananthanarayana, V.S.Web service discovery is a challenging task due to the widespread availability of published services on the web. In this paper, a service crawler-based web service discovery framework is proposed, that employs information retrieval techniques to effectively retrieve available, published service descriptions. Their functional semantics is extracted for similarity computation and tag generation using natural language processing techniques. The framework is inherently dynamic in nature as new service descriptions may be continually added during periodic crawler runs or existing ones may be removed if service is unavailable. To deal with these issues, a dynamic, incremental clustering approach based on bird flocking behaviour is proposed. Experimental results show that semantic analysis and automatic tagging captured the services' functional semantics in a meaningful way. The algorithm effectively handled the dynamic requirements of the proposed framework by eliminating cluster recomputation overhead and achieved a speed-up factor of 61.8% when compared to hierarchical clustering. © 2015 Inderscience Enterprises Ltd.Item Semantic similarity based context-aware web service discovery using NLP techniques(Rinton Press Inc. sales@rintonpress.com, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.Due to the high availability and also the distributed nature of published web services on the Web, efficient discovery and retrieval of relevant services that meet user requirements can be a challenging task. In this paper, we present a semantics based web service retrieval framework that uses natural language processing techniques to extract a service’s functional information. The extracted information is used to compute the similarity between any given service pair, for generating additional metadata for each service and for classifying the services based on their functional similarity. The framework also adds natural language querying capabilities for supporting exact and approximate matching of relevant services to a given user query. We present experimental results that show that the semantic analysis & automatic tagging effectively captured the inherent functional details of a service and also the similarity between different services. Also, a significant improvement in precision and recall was observed during Web service retrieval when compared to simple keyword matching search, using the natural language querying interface provided by the proposed framework. © Rinton Press.Item Semantics-based Web service classification using morphological analysis and ensemble learning techniques(Springer Science and Business Media Deutschland GmbH, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach. © 2016, Springer International Publishing Switzerland.Item Discovering composable web services using functional semantics and service dependencies based on natural language requests(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Kamath S?, S.; Ananthanarayana, V.S.The processes of service discovery, selection and composition are crucial tasks in web service based application development. Most web service-driven applications are complex and are composed of more than one service, so, it becomes important for application designers to identify the best service to perform the next task in the intended application’s workflow. In this paper, a framework for discovering composable service sets as per user’s complex requirements is proposed. The proposed approach uses natural language processing and semantics based techniques to extract the functional semantics of the service dataset and also to understand user context. In case of simple queries, basic services may be enough to satisfy the user request, however, in case of complex queries, several basic services may have to be identified to serve all the requirements, in the correct sequence. For this, the service dependencies of all the services are used for constructing a service interface graph for finding suitable composable services. Experiments showed that the proposed approach was effective towards finding relevant services for simple & complex queries and achieved an average accuracy rate of 75.09 % in finding correct composable service templates. © 2017, Springer Science+Business Media New York.Item Jail-Phish: An improved search engine based phishing detection system(Elsevier Ltd, 2019) Rao, R.S.; Pais, A.R.Stealing of sensitive information (username, password, credit card information and social security number, etc.) using a fake webpage that imitates trusted website is termed as phishing. Recent techniques use search engine based approach to counter the phishing attacks as it achieves promising detection accuracy. But, the limitation of this approach is that it fails when phishing page is hosted on compromised server. Moreover, it also results in low true negative rate when newly registered or non-popular domains are encountered. Hence, in this paper, we propose an application named as Jail-Phish, which improves the accuracy of the search engine based techniques with an ability to detect the Phishing Sites Hosted on Compromised Servers (PSHCS) and also detection of newly registered legitimate sites. Jail-Phish compares the suspicious site and matched domain in the search results for calculating the similarity score between them. There exists some degree of similarity such as logos, favicons, images, scripts, styles, and anchorlinks within the pages of the same website whereas on the other side, the dissimilarity within the pages is very high in PSHCS. Hence, we use the similarity score between the suspicious site and matched domain as a parameter to detect the PSHCS. From the experimental results, it is observed that Jail-Phish achieved an accuracy of 98.61%, true positive rate of 97.77% and false positive rate less than 0.64%. © 2019 Elsevier LtdItem Detection of phishing websites using an efficient feature-based machine learning framework(Springer London, 2019) Rao, R.S.; Pais, A.R.Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number etc. Attackers fool the Internet users by masking webpage as a trustworthy or legitimate page to retrieve personal information. There are many anti-phishing solutions such as blacklist or whitelist, heuristic and visual similarity-based methods proposed to date, but online users are still getting trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques. Our model has been evaluated using eight different machine learning algorithms and out of which, the Random Forest (RF) algorithm performed the best with an accuracy of 99.31%. The experiments were repeated with different (orthogonal and oblique) random forest classifiers to find the best classifier for the phishing website detection. Principal component analysis Random Forest (PCA-RF) performed the best out of all oblique Random Forests (oRFs) with an accuracy of 99.55%. We have also tested our model with the third-party-based features and without third-party-based features to determine the effectiveness of third-party services in the classification of suspicious websites. We also compared our results with the baseline models (CANTINA and CANTINA+). Our proposed technique outperformed these methods and also detected zero-day phishing attacks. © 2018, The Natural Computing Applications Forum.Item Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers(Elsevier Ltd, 2019) Srinivasa, S.; Santhi Thilagam, P.In the current era of internet, information related to crime is scattered across many sources namely news media, social networks, blogs, and video repositories, etc. Crime reports published in online newspapers are often considered as reliable compared to crowdsourced data like social media and contain crime information not only in the form of unstructured text but also in the form of images. Given the volume and availability of crime-related information present in online newspapers, gathering and integrating crime entities from multiple modalities and representing them as a knowledge base in machine-readable form will be useful for any law enforcement agencies to analyze and prevent criminal activities. Extant research works to generate the crime knowledge base, does not address extraction of all non-redundant entities from text and image data present in multiple newspapers. Hence, this work proposes Crime Base, an entity relationship based system to extract and integrate crime related text and image data from online newspapers with a focus towards reducing duplicity and loss of information in the knowledge base. The proposed system uses a rule-based approach to extract the entities from text and image captions. The entities extracted from text data are correlated using contextual as-well-as semantic similarity measures and image entities are correlated using low-level and high-level image features. The proposed system also presents an integrated view of these entities and their relations in the form of a knowledge base using OWL. The system is tested for a collection of crime related articles from popular Indian online newspapers. © 2019 Elsevier Ltd