Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Performance comparison of executing fast transactions in bitcoin network using verifiable code execution(IEEE Computer Society help@computer.org, 2013) Singh, P.; Chandavarkar, B.R.; Arora, S.; Agrawal, N.In this paper, we study Bitcoin network for electronic cash transactions, and compare the extension to the BTCs network which inculcates provision of executing fast transactions with greater security and assurance with the former method of Proof-Of-Work for executing transactions. Above milestones are achieved by introducing the concepts of mutual trust and verifiable code execution between the payer and the payee in the network. Our work proposes a significant modification of the Pioneer model to provide a two-party trust framework for Bitcoin transactions; considerably faster compared to the generic trust platform of Bitcoin networks based on slow proof-of-work. The scheme proposed can promote the use of Bitcoin transactions in real life scenarios, where fast transactions are desirable due time constraints between the payment and the service. © 2013 IEEE.Item Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks using Evolutionary Game Theory(Elsevier B.V., 2016) Arora, S.; Singh, P.; Gupta, A.J.Game theory applies to scenarios wherein multiple players with contrary motives contend with each other. Various solutions based on Game theory have been recently proposed which dealt with security aspects of wireless sensor networks (WSNs). However, the nodes have limited capability of rationality and evolutionary learning which makes it unfavorable to apply conventional game theory in WSNs. Evolutionary Game Theory (EGT) relies on bounded rationality assumption which is in harmony with the wireless sensor networks characteristics. Based on EGT, authors propose an adaptive security model for WSNs for the selection of cryptographic protocols during runtime. The authors formulate this selection in WSNs with the help of an evolutionary game to obtain the evolutionarily stable strategy (ESS) for the system. In this model, the sensor nodes dynamically adapt their defensive strategies to attain the most efficient defense, corresponding to the attackers' varied strategies. Further, the simulations convey that the proposed system converges rapidly to the Evolutionary Stable Strategy. Not only the system converges, but also forms a stable system which was verified by deliberately destabilizing the system. Results show that the nodes quickly return to ESS even after perturbation. © 2016 The Authors.Item Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks using Evolutionary Game Theory(Elsevier B.V., 2016) Arora, S.; Singh, P.; Gupta, A.J.Game theory applies to scenarios wherein multiple players with contrary motives contend with each other. Various solutions based on Game theory have been recently proposed which dealt with security aspects of wireless sensor networks (WSNs). However, the nodes have limited capability of rationality and evolutionary learning which makes it unfavorable to apply conventional game theory in WSNs. Evolutionary Game Theory (EGT) relies on bounded rationality assumption which is in harmony with the wireless sensor networks characteristics. Based on EGT, authors propose an adaptive security model for WSNs for the selection of cryptographic protocols during runtime. The authors formulate this selection in WSNs with the help of an evolutionary game to obtain the evolutionarily stable strategy (ESS) for the system. In this model, the sensor nodes dynamically adapt their defensive strategies to attain the most efficient defense, corresponding to the attackers' varied strategies. Further, the simulations convey that the proposed system converges rapidly to the Evolutionary Stable Strategy. Not only the system converges, but also forms a stable system which was verified by deliberately destabilizing the system. Results show that the nodes quickly return to ESS even after perturbation. © 2016 The Authors.Item Hybrid Approach for Intrusion Detection System(Institute of Electrical and Electronics Engineers Inc., 2018) Singh, P.; Venkatesan, M.In the recent research, Intrusion Detection sys- tem in Machine Learning has been giving good detection and high accuracy on novel attacks. The major purpose of this study is implementing a method that combines Random-Forest classification technique and K-Means clustering Algorithms. In misuse-detection, random-forest algorithm will build a patterns of intrusion over a training data. And in anomaly-detection, intrusions will be identified by the outlier-detection mechanism in the random-forest algorithm. This hybrid-detection system will combine the advantage of anomaly and mis-use detection and improves the performance of detection. This paper mainly focused on evaluating the performance of hybrid approaches namely Gaussian Mixture clustering with Random Forest Classifiers and K-Means clustering with Random Forest Classifiers in-order to detect intrusion. These algorithms were evaluated for the four categories of attacks based on accuracy, false-alarm-rate, and detection-rate. From our experiments conducted, K-Means clustering with Random Forest Classifiers outperformed over the Gaussian Mixture clustering with Random Forest Classifiers. © 2018 IEEE.Item Advanced Microscopic Visualization for Structural Characterization of Cellulose Extracted from Saccharum Spontaneum (Kohua Bon) of Assam, India(Optica Publishing Group (formerly OSA), 2021) Chakraborty, I.; Kalita, R.D.; Singh, P.; Banik, S.; Govindaraju, I.; Mal, S.S.; Zhuo, G.-Y.; Mahato, K.K.; Mazumder, N.Alpha, microcrystalline and nanocrystalline cellulose were sequentially extracted from stems and leaves of Saccharum spontaneum and were subjected to morphological and structural characterization using advanced microscopy techniques, including Scanning electron microscopy and nonlinear optical microscopy. © Optica Publishing Group 2021, © 2021 The Author (s)Item The DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments(International Speech Communication Association, 2023) Baghel, S.; Ramoji, S.; Sidharth; Ranjana, H.; Singh, P.; Jain, S.; Chowdhuri, P.R.; Kulkarni, K.; Padhi, S.; Vijayasenan, D.; Ganapathy, S.In multilingual societies, social conversations often involve code-mixed speech. The current speech technology may not be well equipped to extract information from multi-lingual multispeaker conversations. The DISPLACE challenge entails a first-of-kind task to benchmark speaker and language diarization on the same data, as the data contains multi-speaker conversations in multilingual code-mixed speech. The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing. Further, language diarization (LD) in multi-speaker settings also introduces new challenges, where the system has to disambiguate speaker switches with code switches. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. The systems are evaluated on single-channel far-field recordings. We also release a baseline system and report the highlights of the system submissions. © 2023 International Speech Communication Association. All rights reserved.Item Light-weight Deep Learning Model for Cataract Detection using Novel Activation Function(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, P.; Naveen, B.; Mohapatra, A.R.; Annappa, B.; Dodia, S.In cataracts, the natural lens behind the iris and pupil is cloudy, which causes light passing through it to be distorted or blocked, causing blurry or dim vision. About 50% of all cases of blindness worldwide is caused by cataract, according to WHO and the National Library of Medicine. A timely diagnosis of cataracts can help prevent vision loss and other disease-related complications. Several recent developments in machine learning have significantly impacted medical science. However, most existing approaches for cataract detection are based on traditional machine learning techniques. There have been a few attempts to use deep learning in recent years; the models have delivered decent outcomes but require much computational power. Reducing ophthalmologists' time can improve patient outcomes, increase access to care, lower costs, address workforce shortages, and improve healthcare efficiency. It allows ophthalmologists to see more patients and provide more accurate, timely diagnoses and treatments. Using lightweight deep learning algorithms, this paper proposes a solution that delivers rapid and precise results without requiring high-end hardware. A novel activation function is also proposed that significantly improved the performance. The proposed model is a lightweight one that achieved 95.8% accuracy using only 16,874 parameters. © 2023 IEEE.Item The Second DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments(International Speech Communication Association, 2024) Kalluri, S.B.; Singh, P.; Roy Chowdhuri, P.; Kulkarni, A.; Baghel, S.; Hegde, P.; Sontakke, S.; Deepak, K.T.; Mahadeva Prasanna, S.R.; Vijayasenan, D.; Ganapathy, S.The DIarization of SPeaker and LAnguage in Conversational Environments (DISPLACE) 2024 challenge is the second in the series of DISPLACE challenges, which involves tasks of speaker diarization (SD) and language diarization (LD) on a challenging multilingual conversational speech dataset. In the DISPLACE 2024 challenge, we also introduced the task of automatic speech recognition (ASR) on this dataset. The dataset containing 158 hours of speech, consisting of both supervised and unsupervised mono-channel far-field recordings, was released for LD and SD tracks. Further, 12 hours of close-field mono-channel recordings were provided for the ASR track conducted on 5 Indian languages. The details of the dataset, baseline systems and the leader board results are highlighted in this paper. We have also compared our baseline models and the team's performances on evaluation data of DISPLACE-2023 to emphasize the advancements made in this second version of the challenge. © 2024 International Speech Communication Association. All rights reserved.