Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
| Autor: | Pedro H. J. Nardelli, Dick Carrillo Melgarejo, Robson V. Mendonca, Demostenes Zegarra Rodriguez, Muhammad Saadi, Arthur A. M. Teodoro, Renata Lopes Rosa |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Artificial neural network Computer science Tree-CNN General Engineering deep learning 020206 networking & telecommunications Denial-of-service attack 02 engineering and technology Intrusion detection system computer.software_genre Convolutional neural network Hierarchical database model Activation function TK1-9971 Support vector machine Identification (information) Statistical classification 0202 electrical engineering electronic engineering information engineering intrusion detection system 020201 artificial intelligence & image processing General Materials Science Data mining Electrical engineering. Electronics. Nuclear engineering computer |
| Zdroj: | IEEE Access, Vol 9, Pp 61024-61034 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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