Change trend of averaged Hurst parameter of traffic under DDOS flood attacks

被引:66
作者
Li, Ming [1 ]
机构
[1] E China Normal Univ, Sch Informat Sci & Technol, Shanghai 200026, Peoples R China
基金
中国国家自然科学基金;
关键词
Hurst parameter; traffic; time series; distributed denial-of-service flood attacks; anomaly detection;
D O I
10.1016/j.cose.2005.11.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Distributed denial-of-service (DDOS) flood attacks remain great threats to the Internet though various approaches and systems have been proposed. Because arrival traffic pattern under DDOS flood attacks varies significantly away from the pattern of normal traffic (i.e., attack free traffic) at the protected site, anomaly detection plays a rote in the detection of DDOS flood attacks. Hence, quantitatively studying statistics of traffic under DDOS flood attacks (abnormal traffic for short) are essential to anomaly detections of DDOS flood attacks. References regarding qualitative descriptions of abnormal traffic are quite rich, but quantitative descriptions of its statistics are seldom seen. Though statistics of normal traffic are affluent, where the Hurst parameter H of traffic plays a key role, how H of traffic varies under DDOS flood attacks is rarely reported. As a supplementary to our early work, this paper shows that averaged H of abnormal traffic usually tends to be significantly smaller than that of normal one at the protected site. This abnormality of abnormal traffic is demonstrated with test data provided by MIT Lincoln Laboratory and explained from a view of Fourier analysis. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:213 / 220
页数:8
相关论文
共 56 条
[1]   Traffic models in broadband networks [J].
Adas, A .
IEEE COMMUNICATIONS MAGAZINE, 1997, 35 (07) :82-89
[2]  
[Anonymous], 2003, DECIPHERING DETECT 3
[3]  
[Anonymous], 1958, Introduction to Fourier Analysis and Generalised Functions
[4]  
BENCSATH B, 2004, INT S COLL TECHN SYS, P22
[5]  
Bendat JS., 2011, RANDOM DATA ANAL MEA
[6]   LONG-RANGE DEPENDENCE IN VARIABLE-BIT-RATE VIDEO TRAFFIC [J].
BERAN, J ;
SHERMAN, R ;
TAQQU, MS ;
WILLINGER, W .
IEEE TRANSACTIONS ON COMMUNICATIONS, 1995, 43 (2-4) :1566-1579
[7]  
Beran J., 1994, STAT LONG MEMORY PRO
[8]  
BETTATI R, 1999, P 1 USENIX WORKSH IN
[9]   Analyzing exact fractal time series: evaluating dispersional analysis and rescaled range methods [J].
Caccia, DC ;
Percival, D ;
Cannon, MJ ;
Raymond, G ;
Bassingthwaighte, JB .
PHYSICA A, 1997, 246 (3-4) :609-632
[10]  
CARMONA R, 1999, PRACTICAL TIME FREQU, P244