• S MERCY SHALINIE

      Articles written in Sadhana

    • FuRL: fuzzy RBM learning framework to detect and mitigate network anomalies in Information Centric Network

      P VIMALA RANI S MERCY SHALINIE

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      Information Centric Network (ICN) is a promising next-generation internet architecture in which the network focuses on retrieving the content by employing open in-network caching scheme to provide an efficient content distribution to users. However, such open in-network caching is vulnerable to networkanomalies. In particular, cache pollution attack disrupts the smooth working of in-network caching by flooding unpopular contents. Hence, the in-network caching malfunctions and legitimate consumer requests are dropped. To address this problem, a secure framework based on Fuzzy Restricted Boltzmann Machine has been proposed to detect the anomalies and defend against such pollution attacks in ICN. Further, a reward-based cache replacement (ReBac) algorithm that is capable of avoiding cache pollution attack has also been proposed. Theexperimental results obtained while testing the proposed framework show better detection rate compared with the state-of-art solution and the proposed framework shows better cache rate

    • QeCSO: Design of hybrid Cuckoo Search based Query expansion model for efficient information retrieval

      J FELICIA LILIAN K SUNDARAKANTHAM S MERCY SHALINIE

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      The web contains lots of information that gets updated every second. Searching for a relevant document from the web needs an efficient scrutinization. As the user’s need varies based on location, intention and purpose the retrieval of an efficient response is a challenge. To address this challenge an informationretrieval technique has been put forth along with the advent of the machine learning and deep learning models. We have proposed a QeCSO algorithm to perform an efficient retrieval of relevant response. The AttentionbasedBi-directional LSTM (ATT-BLSTM) helps to improve the retrieval of the relevant document based on its feature that correlates the semantics between the query and the content. On further expanding the query we can observe a steep improvement in retrieving the response. To perform this, the output from ATT-BLSTM is given as an input to the meta-heuristic algorithm called cuckoo search. It helps us to retrieve the exact term to expand the query and make our search to move closer to an optimal solution. The performance of our approach iscompared to those of other models based on the evaluation metrics such as accuracy and F-measure. It is evaluated by applying the model over the SQUAD 1.1 dataset. By analyzing the results it is verified that our proposed algorithm achieves an accuracy of 95.8% for an efficient information retrieval of relevant response with an increase in F-measure.

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