Hybridization of Bottlenose Dolphin Optimization and Artificial Fish Swarm Algorithm with Efficient Classifier for Detecting the Network Intrusion in Internet of Things (IoT)
Keywords:
network intrusion, optimization, convolution neural network, classification, feature selectionAbstract
Due to the current target-oriented assaults aimed at stealing confidential information from a business, Intrusion Detection Systems (IDSs) research is essential in the field of network security. Intrusion classification and detection are difficult yet highly specialised tasks. The accuracy of intrusion detection in network traffic varies for various methods in the current models. The inter domain dispersion disagreement assessment of the current method, unfortunately, has a higher computing complexity as the sample size rises, that might worsen the strategy's capacity in generalise. We suggest a deep transfer learning method based on 1D-CNN for categorising the incursions in order to resolve the issue. Also, a hybrid bottlenose dolphin optimization/artificial fish swarm technique is described for feature selection that can quickly and effectively detect a variety of intrusion behaviours by learning the information associated with typical intrusion characteristics. Using a character that allows the rough set to retain the original dataset's discernibility after reductions, the unique dataset's reductions were computed then utilised to create a neural network for training, improving detection capability. For the purpose of analysis three benchmark dataset such as KDD Cup' 99, NSL-KDD and UNSW-NB15 are used and shows that the suggested Hyb_DOAFS_1D-CNN achieves 96.4% and 99% of accuracy, 92% and 99% of precision, 99% and 97% of recall, 99.4% and 99% of f1-score.
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