Intrusion Detection Based on Deep Learning | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 43, Volume 28, ICEEM2019-Special Issue, 2019, Pages 369-373 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2019.76787 | ||
| Authors | ||
| Youssef F. Sallam1; Ahmed Sedik2; Rania Ghazy1; Nirmeen Abdelwahab3; HossamEl-din H. Ahmed1; Adel Saleeb1; Ghada M. El Banby4; Ashraf A. M. Khalaf5; Fathi E. Abd El-Samie6 | ||
| 1Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt | ||
| 2The Robotics and Intelligent Machines Department Faculty of Aritificial Intelligence KafrElsheikh University: Kafr ElSheikh, Egypt | ||
| 3Engineering and Computer Science Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt | ||
| 4Industrial Electronics and Control Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt | ||
| 5Electronics and Communications Department Faculty of Engineering Minia University: Minia, Egypt | ||
| 6Communications and Electronics Department Faculty of Electronic Engineering, Menoufia University: Menouf, Egypt | ||
| Abstract | ||
| Information and Communication Technology (ICT) plays an important role in our life. ICT is engaged with the business and individual patterns of human life. The ICT security is one of the normal ICT fields, which attracts researchers’ attention. The objective of security is to discover attacks represented in control and data planes. These attacks include Denial of Service (DoS), and probing attacks. Intrusion Detection System (IDS) is one of the best solutions for observing, and distinguishing these attacks. In this paper, an IDS dependent on Deep Learning (DL) is proposed. This system achieves an accuracy detection level of 100%. | ||
| Keywords | ||
| Intrusion Detection System (IDS); Deep Learning (DL) and Convolutional Neural Network (CNN) | ||
| References | ||
|
[1] White paper, “Intrusion Detection: A Survey,” ch.2, DAAD19-01, NSF, 2002. [2] G. Vigna and R. A. Kemmerer, “Net STAT: A Network based Intrusion Detection Approach,” Proc. 14th An.Comp. Sec. App. Conf., 1998, pp. 25–34. [3] A. Valdes and D. Anderson, “Statistical Methods for Computer Usage Anomaly Detection Using NIDES,” Tech. rep., SRI International, Jan. 1995. [4] Feinstein, D. Schnackenberg, R. Balupari, and D. Kindred. “Statistical approaches to ddos attack detection and response”. DARPA Information Survivability Conference and Expositio, 1:303 – 314, 2003. [5] P. Barford, J. Kline, D. Plonka, and A. Ron. “A signal analysis of network traffic anomalies”.2nd ACM SIGCOMM Workshop on Internet measurement,2002 [6] Kukielka, P.& Kotulski, Z. (2010). Adaptation of The Neural Network- Based IDS to New Attacks Detection.cs.CR.Retrieved from http://arxiv.org/abs/1009.2406. [7] V. Siris and F. Papagalou. “Application of anomaly detection algorithms for detecting syn flooding attacks”. Computer Communications, 29(9):1433–1442, 2006. [8] V. A. Siris, F. Papagalou, “Application of Anomaly Detection Algorithms for Detecting SYN Flooding Attacks”, IEEE GLOBECOM2004, Nov. 2004, pp. 2050-2054. [9] R. B. Blazek, H. Kim, B. Rozovskii, A. Tartakovsky, “A Novel Approach to Detection of Denial-of-Service Attacks via Adaptive Sequential and Batch-Sequential Change-Point Detection Methods”, IEEE Workshop Information Assurance and Security,2001, pp. 220-226. [10] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015 [11] Wickramasinghe, C.S.; Marino, D.L.; Amarasinghe, K.; Manic, M. “Generalization of Deep Learning for Cyber-Physical System Security: A Survey”. In Proceedings of the IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 745–751. [12] Alpaydin, E. (2014). “Introduction to machine learning”. MIT press. [13] Deng, L., Yu, D. “Deep learning: Methods and applications”. Found. Trends Signal Process. 2014, 7, 197–387. [14] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). ''Gradient-based learning applied to document recognition''. Proceedings of the IEEE, 86(11), 2278-2324. [15] Renato, M. A., Huang, F. J., Boureau, Y. L. &LeCun, Y. (2007). “Unsupervised learning of invariant feature hierarchies with applications to object recognition”. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on (pp. 1-8). [16] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014).''Dropout: A simple way to prevent neural networks from over fitting''. The Journal of Machine Learning Research, 15(1), 1929-1958. | ||
|
Statistics Article View: 297 |
||
