Muhammad Basit Umair, Zeshan Iqbal, Muhammad Ahmad Faraz, Muhammad Attique Khan, Yu-Dong Zhang, Navid Razmjooy, Sefedine Kadry
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A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model.
An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.