Visible to the public NSNN Algorithm Performance with Different Neural Network Architectures

TitleNSNN Algorithm Performance with Different Neural Network Architectures
Publication TypeConference Paper
Year of Publication2020
AuthorsPamukov, Marin, Poulkov, Vladimir, Shterev, Vasil
Conference Name2020 43rd International Conference on Telecommunications and Signal Processing (TSP)
KeywordsArtificial neural networks, Biological neural networks, Classification algorithms, Computer architecture, Cyber-physical systems, Immune system, Intrusion detection, intrusion detection system, IoT, Metrics, Negative Selection, Neural Network Security, Neural networks, Neurons, policy-based governance, pubcrawl, Resiliency
AbstractInternet 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.
Citation Keypamukov_nsnn_2020