SYN Flood Attack Detection Usiing AR Model | ||||
Menoufia Journal of Electronic Engineering Research | ||||
Article 5, Volume 28, Issue 1, January 2019, Page 85-92 PDF (417.16 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/mjeer.2019.62725 | ||||
View on SCiNiTO | ||||
Authors | ||||
Rania Ghazy* 1; el-sayed El-Rabaie1; Moawad Dessouky1; Nawal El-Fishawy2; Fathi Abd El-Samie1 | ||||
1Dept. of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University. | ||||
2Computer Science and Eng., Faculty of Electronic Eng., Menoufia University. | ||||
Abstract | ||||
Due to the sophisticated characteristics of auto-regressive (AR) modeling approach, it finds applications in most anomaly detection processes. This paper extends the concept of AR modeling to create models for the estimated auto-correlation between data and control planes packet counts of the network traffic. These models are fed with the anomaly traffic containing SYN flood attack. The estimated residuals in these scenarios are used as indicators for the attacks. Simulation results revealed the success of attack detection using the proposed approach. | ||||
Keywords | ||||
auto-regressive; control planes and network traffic | ||||
References | ||||
h: 0px; "> [1] M. Manna and A. Amphawan “Review Of Syn-Flooding Attack Detection Mechanism,” International Journal of Distributed and Parallel Systems (IJDPS), Vol.3, No.1, January 2012. [2] https://en.wikipedia.org/wiki/SYN_flood (Access date Dec. 3, 2017) [3] B. AsSadhan, H. Kim, J. Moura, and X. Wang, “Network Traffic Behavior Analysis by Decomposition into Control and Data Planes," InternationalWorkshop on Security in Systems and Networks (SSN) with conjunction of IEEE International Parallel and Distributed Processing Symposium (IPDPS), Miami, FL, USA, Apr. 18, 2008. [4] http://en.wikipedia.org/wiki/Cross-correlation (Access date Dec. 3, 2017. [5] A. Aibinu and J. Salami, A. Shafie , A. Najeeb “Comparing Autoregressive Moving Average (ARMA) coefficients determination using Artificial Neural Networks with other techniques” World Academy of Science, Engineering and Technology, 18, 2008. [6] http://en.wikipedia.org/wiki/Autoregressive_model (Access date Dec. 3, 2017). | ||||
Statistics Article View: 96 PDF Download: 229 |
||||