As billions of devices are now connected to the Internet, the Internet of Things has become an integral part of modern life. However, hackers are constantly developing new methods of exploiting IoT devices, leaving them open to security breaches. There are many applications for artificial neural networks (ANNs) in the detection and analysis of IoT attacks. The structure and operation of ANNs are inspired by the human brain. This enables them to mine data for hidden patterns. This makes them ideal for discovering IoT attacks, which frequently involve convoluted data patterns. To learn how to identify patterns in IoT traffic data, a deep neural network is employed. We put our method through its paces using real-world IoT traffic data. Our findings demonstrate that our approach is highly effective at detecting IoT attacks.


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    Titel :

    IoT Attack Detection Using Artificial Neural Network


    Beteiligte:


    Erscheinungsdatum :

    22.11.2023


    Format / Umfang :

    284492 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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