Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Differential evolution

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    Network anomaly detection using artificial neural networks optimised with PSO-DE hybrid
    (Springer Verlag service@springer.de, 2019) Rithesh, K.; Gautham, A.V.; Chandra Sekaran, K.
    Anomaly Detection is an important field of research in the present age of ubiquitous computing. Increased importance in Network Monitoring and Security due to the growing Internet is the driving force for coming up with new techniques for detecting anomalies in network behaviour. In this paper, Artificial Neural Network (ANN) model optimised with a hybrid of Particle Swarm Optimiser (PSO) and Differential Evolution (DE) is proposed to monitor the behaviour of the network and detect any anomaly in it. We have considered two subsets of 2000 and 10000 dataset size of the NSL KDD dataset for training and testing our model and the results from this model is compared with the traditional ANN-PSO algorithm, and one of the existing variants of PSO-DE algorithm. The performance measures used for the analysis of results are the training time, precision, recall and f1-score. © Springer Nature Singapore Pte Ltd. 2019.
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    A novel meta-heuristic differential evolution algorithm for optimal target coverage in wireless sensor networks
    (Springer Verlag service@springer.de, 2019) Naik, C.; Shetty D, D.
    A wireless sensor network (WSN) faces various issues one of which includes coverage of the given set of targets under limited energy. There is a need to monitor different targets in the sensor field for effective information transmission to the base station from each sensor node which covers the target. The problem of maximizing the network lifetime while satisfying the coverage and energy parameters or connectivity constraints is known as the Target Coverage Problem in WSN. As the sensor nodes are battery driven and have limited energy, the primary challenge is to maximize the coverage in order to prolong network lifetime. The problem of assigning a subset of sensors, such that all targets are monitored is proved to be NP-complete. The Objective of this paper is to assign an optimal number of sensors to targets to extend the lifetime of the network. In the last few decades, many meta-heuristic algorithms have been proposed to solve clustering problems in WSN. In this paper, we have introduced a novel meta-heuristic based differential evolution algorithm to solve target coverage in WSN. The simulation result shows that the proposed meta-heuristic method outperforms the random assignment technique. © 2019, Springer Nature Switzerland AG.