IoT Secure Communication using ANN Classification Algorithms
Tamer S. Fatayer, Mohammed N. Azara · 2019
Internet of Things (IoT) is state of the art technology of internet network that enables devices in our life (e.g., camera, and cars) to connect internet network and exchange data between them. Data communication between these devices exposed to different attacks (e.g., Denial of Service (DoS), buffer overflow, and probing attack). So we need secure and good performance techniques to detect these attacks. In this paper, we use Artificial Neural Networks (ANN) classification algorithms to detect several attacks which IoT communication is exposed. These algorithms are all learning Vectors Quantization (LVQ's) versions, Radial basis function (RBN) and Multilayer Perceptron (MLP). In this paper, to conduct our experimental results, we use KDD CUP 99 Dataset that contains 494020 instances for different attacks. Our results showed that LVQ2.1 archives best classification accuracy (97.44%) than other versions during 4.56 second. On the other hand, MLP achieves best accuracy (99.86%) than LVQ2.1 and RBF. Unfortunately, time taken by MLP is significantly low which takes 28811 seconds.