Faculty Publications
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Publications by NITK Faculty
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Item Mitigating Neighborship Attack In Underwater Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Deshmukh, A.; Deo, S.; Chandavarkar, B.R.Transmission of information through Underwater Wireless Sensor Networks(UWSN) across the ocean is one of the enabling technologies for underwater communication. These advances trigger security concerns of the underlying UWSN. Due to the Sack of predictability of the movement of the nodes in such a system, secure neighbour discovery for successful information exchange is a challenge. A neighborship attack is the one which hinders neighbour discovery amongst the various nodes within the network. The wormhole attack and the Sybil attack being the prominent attacks in this category, lead to various issues if not mitigated. The consequences of these attacks can quickly scale from reduced throughput to loss of confidentiality. Moreover, conventional cryptographic algorithms are not possible to implement in a UWSN due to restrictions on the open acoustic channel and severe underwater conditions. In this paper, we propose a true-neighbour algorithm for mitigating neighborship attack in UWSN. Furthermore, the performance of this algorithm is demonstrated in UnetStack with reference to end to end packet delay, with and without implementation of the algorithm. © 2021 IEEE.Item User Interest Drift Identification Using Contextual Factors in Implicit Feedback-Based Recommender Systems(Springer Science and Business Media Deutschland GmbH, 2023) Chaitanya, V.S.; Deo, S.; Santhi Thilagam, P.S.The modeling of appropriate recommendations using the session interactions in the implicit feedback-based recommender systems necessitates the identification of user interest drift. But this identification is challenging due to the presence of unintentional interactions (noise) made by the user. Most of the existing literature focused on understanding the correlation between ongoing session interactions but did not explore the contextual factors, such as the time of occurrence of the session and the item’s popularity, that led the user to perform that specific interaction. This has resulted in the wrongful categorization of interactions between user interest drift and noise. To overcome these limitations, this work proposes a deep learning-based approach that uses both ongoing session information and contextual information. Depending on availability, this work also considers the user’s previous interactions to generate personalized recommendations. In comparison with the existing works, this work effectively identifies the user interest drift and generates the appropriate recommendations for the users. The proposed approach demonstrates superior performance over state-of-the-art baselines in terms of Recall and MRR, as evidenced by experimental results on benchmark datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.