A credit-based comprehensive worm detection algorithm

作者：Yang Hong Yu^*; Mi Yun Feng

来源：Journal of the University of Electronic Science and Technology of China, 2013, 42(4): 571-576.

DOI：10.3969/j.issn.1001-0548.2013.04.008

• Comprehensive analysis
• Credit
• Dataset
• Detection experiments
• Different frequency
• False positive rates
• Sequential hypothesis testing
• Worm scan

This paper presents a new comprehensive worm detection algorithm based on credit. Firstly, the parameters of connection success probability are optimized and set through detection contrast experiments. Secondly, the response strategy of the credit-based connection rate limiting algorithm is redesigned, and consequently the event response process of the algorithm is improved. On the basis of the above study, the new credit-based comprehensive worm detection (CBCWD) algorithm is designed to monitor network traffics, execute response strategy for monitoring events, and give the detection results through attack condition judge and comprehensive analysis. Five different frequencies attacks detection experiments on the NUST 2011 open dataset were conducted. The experiment results demonstrate that proposed algorithm has a higher detection rate and lower false positive rate.

• 出版日期2013