• U Durga Premchand
• Dr S. Jhansi Rani

With the advent of the Internet of Things, the security of the network layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of the Internet of Things. Therefore, it is extremely urgent to study the intrusion detection system corresponding to today's Internet of Things security. This paper presents an intrusion detection model based on improved Genetic Algorithm and Deep Trust System. Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DTS achieves a high detection rate. Finally, the NSL-KDD dataset was used to simulate and evaluate the model algorithm. Experimental results show that the improved intrusion detection model combined with DTS can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the network

Internet of Things security; Intrusion detection; Deep Trust System; Genetic Algorithm

"An Improved Genetic Algorithm and Deep Trust System for Intrusion Detection Based on IoT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.789-796, June 2019, Available :http://www.jetir.org/papers/JETIR1907418.pdf

"An Improved Genetic Algorithm and Deep Trust System for Intrusion Detection Based on IoT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp789-796, June 2019, Available at : http://www.jetir.org/papers/JETIR1907418.pdf