Approach of detecting low-rate DoS attack based on combined features

Approach of detecting low-rate DoS attack based on combined features

LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2017-05-01
Series:Tongxin xuebao
Subjects:
low-rate denial of service attack
united features
BP neural network
anomaly detection
Online Access:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017075/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.
ISSN:1000-436X

Similar Items