In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networ More
In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networks and the Internet of Things have made SDN-IoT security one of the most important concerns. On the other hand, the use of intelligent algorithms has been an opportunity that these algorithms have been able to make significant progress in various cases such as image processing and disease diagnosis. Of course, intrusion detection systems for SDN-IoT environment still face the problem of high false alarm rate and low accuracy.
In this article, a new hybrid method based on intelligent algorithms is proposed. The proposed method integrates the monitoring algorithms of frequent return gate and unsupervised k-means classifier in order to obtain suitable results in the field of intrusion detection. The simulation results show that the proposed method, by using the advantages of each of the integrated algorithms and covering each other's disadvantages, has more accuracy and a lower false alarm rate than other methods such as the Hamza method. Also, the proposed method has been able to reduce the false alarm rate to 1.1% and maintain the accuracy at around 99%.
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