Abstract
Even in its infancy, the internet of things (IoT) has enticed most of the modern industrial areas like smart cities, automobiles, medical technology. Since IoT connects everything together, it is vulnerable to a variety of devastating intrusion attacks. Being the internet of different devices makes it easy for attackers to launch their attacks. Thus, to combat all these attacks, an attack analysis is presented in this article using the basic principles of Artificial Neural Networks. Internet packet traces are used to train to the supervised ANN (Multilevel Perceptron) and evaluated after the training to decline the DDoS Attacks. This research article mainly focuses on the categorization of traffic patterns into legitimate traffic and attack traffic patterns in IoT network. The ANN processes are evaluated and tested in a simulated IoT network. The experimental results show a greater accuracy in detection of various DDoS attacks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computers Communications & Control
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
