ISSN 0975-3583
 

Journal of Cardiovascular Disease Research



    Deep Ensemble Framework with Supervised Learning for Secure IoT Network


    Trishala Reddy, C. Vaishnavi Reddy, T. Ruchitha, G. Anitha
    JCDR. 2023: 195-207

    Abstract

    Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), recurrent neural network with gated recurrent unit (RNN-GRU) and convolutional neural network (CNN) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details. While producing some malicious users may tamper smart meter to get more bill which can be collected from electricity renewable distributed energy. This attack may cause huge losses to agencies. To detect such attack, this project is employing deep learning models which can detect all possible alterations to predict theft.

    Description

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    Volume & Issue

    Volume 14 Issue 5

    Keywords