Autors: Pamukov, M. E., Poulkov, V. K., Shterev, V. A.
Title: NSNN Algorithm Performance with Different Neural Network Architectures
Keywords: Intrusion Detection System; IoT; Negative Selection; Neural Networks

Abstract: Internet of Things (IoT) development and the addition of billions of computationally limited devices prohibit the use of classical security measures such as Intrusion Detection Systems (IDS). In this paper, we study the influence of the implementation of different feed-forward type of Neural Networks (NNs) on the detection Rate of the Negative Selection Neural Network (NSNN) algorithm. Feed-forward and cascade forward NN structures with different number of neurons and different number of hidden layers are tested. For training and testing the NSNN algorithm the labeled KDD NSL dataset is applied. The detection rates provided by the algorithm with several NN structures to determine the optimal solution are calculated and compared. The results show how these different feed-forward based NN architectures impact the performance of the NSNN algorithm.

References

    Issue

    in Proceedings of International Conference on Telecommunications and Signal Processing (TSP), 7-9 July 2020, pp. 280-284, 2020, Italy, DOI 10.1109/TSP49548.2020.9163469

    Copyright IEEE

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science