Research Article  Open Access
Bound Maxima as a Traffic Feature under DDOS Flood Attacks
Abstract
This paper gives a novel traffic feature for identifying abnormal variation of traffic under DDOS flood attacks. It is the histogram of the maxima of the bounded traffic rate on an intervalbyinterval basis. We use it to experiment on the traffic data provided by MIT Lincoln Laboratory under Defense Advanced Research Projects Agency (DARPA) in 1999. The experimental results profitably enhance the evidences that traffic rate under DDOS attacks is statistically higher than that of normal traffic considerably. They show that the pattern of the histogram of the maxima of bounded rate of attackcontained traffic greatly differs from that of attackfree traffic. Besides, the present traffic feature is simple in mathematics and easy to use in practice.
1. Introduction
People nowadays are heavily dependent on the Internet that serves as an infrastructure in the modern society. However, distributed denialofservice (DDOS) flood attackers remain great threats to it. By consuming resources of an attacked site, the victim may be overwhelmed such that it denies services it should offer or its service performances are significantly degraded. Therefore, intrusion detection system (ISD) for detecting DDOS flood attacks has been greatly desired.
There are two categories regarding IDSs. One is misuse detection and the other anomaly detection. Attacking alerts given by misuse detection is primarily based on a library of known signatures to match against network traffic, see, for example, [1–5]. Thus, attacking with unknown signatures from new variants of an attack can escape from being detected by signaturebased IDSs with the probability one, see, for example, [6], making such a category of IDSs at the protected site irrelevant. However, based on anomaly detection, abnormal variations of traffic are identified as potential intrusion so that this category of IDSs are particularly paid attention to for identifying new attacking, see, for example, [7–13]. For the simplicity, in what follows, the term IDS is in the sense of anomaly detection.
Noted that the detection accuracy is a key issue of an anomaly detector, see, for example, [14, 15]. To be effective, IDSs require appropriate features for accurately detecting an attack and distinguishing it from the normal activity as can be seen from [10, Section IV]. Hence, developing new traffic features for anomaly detection is essential.
The reference papers regarding traffic features for IDS use are wealthy. For example, 86 features for clustering normal activities are discussed in [9]. Note that a selected feature is methodologydependent. In this regard, [16] uses packet head data. The paper [17] utilizes autocorrelation function of longrange dependent (LRD) traffic time series in packet size and [18] employs the Hurst parameter. Scherrer et al. adopt scaling properties of LRD traffic [19].
The traffic models used in [17–23] are in the sense of fractal. In general, fractal models might be somewhat complicated in practical application in engineering in comparison with the traffic feature proposed in this paper.
Recall that there are two categories in traffic modeling [24, Section XIV]. One is statistical modeling (e.g., LRD processes). The other bounded modeling, which has particular applications to modeling traffic at connection level, see, for example, [25–30]. Bounded models, in conjunction with a class of service disciplines, are feasible and relatively efficient in applications, such as connection admission control (CAC) in guaranteed qualityofservice (QoS). In addition, such models are simple in mathematics and relatively easy to be used in practice in comparison with fractal models. This paper aims at providing a new traffic feature for anomaly detection based on bounded modeling of traffic. The main contributions in this paper are as follows.(i)We present the histogram of the maxima of bounded traffic rate on an intervalbyinterval basis as a traffic feature for exhibiting abnormal variation of traffic under DDOS flood attacks.(ii)The experimental results exhibit that the maxima of rate bound of attackcontained traffic is statistically greater than that of attackfree traffic drastically.
The rest of paper is organized as follows. Experimental data and related work are briefed in Section 2. The histogram of the maxima of traffic rate bound is proposed in Section 3. Experimental results are demonstrated in Section 4, which is followed by discussions and conclusions.
2. Experimental Data and Related Work
2.1. Experimental Data
While DDOS attacks continue to be a problem, there is currently not much quantitative data available for researchers to study the behaviors of DDOS flood attacks. The data in the 19981999 DARPA (http://www.ll.mit.edu/IST/ideval) are valuable but rare for public use though there are points worth further discussion [31]. Those data were obtained under the conditions of realistic background traffic and mean examples of realistic attacks [32, 33]. The used data sets in 1999 contain more than 200 instances and 58 attacks types, see, for details [34]. Two data sets are explained below.
2.1.1. Set One: AttackFree Traffic (1999 Training Data—Week 1)
The first set of data containing 5 traces. We name them by OMW1i1999AF (, 2, 3, 4, 5), meaning OutsideMITweek1i1999attackfree. Table 1 indicates the actual times at which the first packet and last one were extracted for each trace.

2.1.2. Set Two: AttackContained Traffic (1999 Training Data—Week 2)
Five traces are included in the second data set. They are named as OMW2i1999AC (, 2, 3, 4, 5), implying OutsideMITweek2i1999attack contained. The actual times at which the first packet and last one were extracted for each trace are listed in Table 2.

2.2. Traffic Rate under DDOS Flood Attacks
Roughly, high rate is the radical feature of attackcontained traffic. The paper [35] reported the real events in 2000. He noticed that “the attacks inundated servers with 1 gigabit per second of incoming data, which is much more traffic than they were built to handle [35, page 12].” The analysis given by Moore et al. says that “to load the network, an attacker generally sends small packets as rapidly as possible since most network devices (both routers and NICs) are limited not by bandwidth but by packet processing rate [36, Section 2.1].” They infer that traffic rate is usually the best measure of network load during an attack. In short, computer scientists consider high rate as a basic feature of attackcontained traffic, also see, for example, [37–42]. The experimental results in this paper are simply for the data of the 1999 DARPA in the case of highrate attacks.
2.3. Traffic Bounds
In this subsection, we brief the deterministic bounds for accumulated traffic and traffic rate with the help of demonstrations using traffic traces OMW111999AF and OMW111999CF.
Let be the series, indicating the number of bytes in the th packet () of arrival traffic at time . Then, is a discrete series, indicating the number of bytes in the th packet of arrival traffic. Figure 1 shows a plot of for the first 1024 points of OMW111999AF.
According to [27, 43], an upper bound of arrival traffic is given below.
Definition 2.1. Let be the arrival traffic function. Then, is called traffic upper bound of over the duration of length .
Note 1. The physical meaning of is that the accumulated amount of arrival traffic over the duration of length is upper bounded by . The unit of is bytes. is an increasing function in terms of . Figure 2 indicates of OMW111999AF for .
Definition 2.2. Let be the arrival traffic function. Then, is called upper bound of traffic rate (traffic rate bound for short) of .
Note 2. Equation (2.2) specifies that GAMA is the maximum arrival rate at a specific point in the network over any duration of length . The unit of GAMA is defined as Bytes per . GAMA is a decreasing function in terms of . Figure 3 demonstrates GAMA of OMW111999AF for .
3. Histogram of Maxima of Traffic Rate Bound: A Feature for Identifying Abnormal Variation of Traffic under DDOS Attacks
In this section, we first introduce the time series of traffic rate bound. Then, we establish the maxima of traffic rate bound. Finally, we achieve the histogram of the maxima of traffic rate bound. The demonstrations with the experimental data are used for facilitating the discussions.
3.1. Traffic Bound Series
Theoretically, can be any positively real number. In practice, however, is selected as a finite positive integer. Fix the value of and observe traffic bounds in the interval . Then, we express traffic bounds as a function in terms of the interval index . Considering the index , we express traffic upper bound by , which is a series.
Note that is a stochastic series and so is . That is, for . We term traffic upper bound series. Similarly, we use GAMA to represent traffic rate bound series. Figure 4 shows the traffic upper bound series. Figure 5 plots the rate bound series.
Since GAMA is random, identification in a single interval is not enough. We use Figure 6 to explain this point of view. From Figure 6, we see that the rate bound of attackcontained traffic is greater than that of attackfree traffic in some intervals, for example, in the second and third intervals. However, it is less than the rate bound of attackfree traffic in some intervals, for example, in the first and fourth intervals. Therefore, we will study the issue how the bound series of traffic rate statistically varies under DDOS flood attacks. For this reason, we study the maxima of traffic rate bound.
3.2. Maxima of Traffic Rate Bound
Denote that over the index in each interval . Then, MGAMA() represents a series to describe the maximum value of GAMA() in each interval . In other words, MGAMA() stands for the maxima of GAMA(). The unit of MGAMA() is the same as that of GAMA(). Here and below, we use the notation MGAMA_F() for attackfree traffic and MGAMA_C() for attackcontained traffic. Figures 7(a) and 7(b) give the plots of MGAMA_F() and MGAMA_C() for OMW111999AF and OMW211999AC, respectively.
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3.3. Histogram of Maxima
Denote Hist[MGAMA_F()] and Hist[MGAMA_C()] as the histograms of MGAMA_F() and MGAMA_C(), respectively. Then, they represent empirical distributions of MGAMA_F() and MGAMA_C(). Figures 8(a) and 8(b) indicate the Hist[MGAMA_F()] and Hist[MGAMA_C()] for OMW111999AF and OMW111999CF, respectively. From Figure 8(c), we see that the pattern of Hist[MGAMA_F()] considerably differs from that of Hist[MGAMA_C()]. To investigate this phenomenon quantitatively, we need a measure to describe the similarity or dissimilarity between the pattern of Hist[MGAMA_F()] and that of Hist[MGAMA_C()], which will be explained in the next subsection.
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3.4. Correlation Coefficient Used as a Similarity Measure for Pattern Matching
There are many measures to characterize the similarity or the dissimilarity of two patterns in the field of pattern matching, see, for example, [44, 45]. Among them, the correlation coefficient between two patterns is commonly used in engineering, see, for example, [46]. We use it to measure the pattern similarity in this research. Denote that where corr implies the correlation operation.
It is known that 0 ≤ Corr_FC ≤ 1. The larger the value of Corr_FC the more similar between the pattern of Hist[MGAMA_F()] and that of Hist[MGAMA_C()]. Mathematically, the case of Corr_FC = 1 implies that the pattern of Hist[MGAMA_F()] is exactly the same as that of Hist[MGAMA_C()]. On the contrary, Corr_FC = 0 means that the pattern of Hist[MGAMA_F()] is totally different from that of MGAMA_C()]. From the point of view of engineering, however, the extreme case of either Corr_FC = 1 or Corr_FC = 0 does not make much sense due to errors and uncertainties in measurement and digital computation. In practical terms, one uses a threshold for Corr_FC to evaluate the similarity between two. The concrete value of the threshold depends on the requirement designed by researchers that but it is quite common to take 0.7 as the smallest value of the threshold for the pattern patching purpose. Suppose that we consider 0.8 as the threshold value. Then, we say that the pattern of Hist[MGAMA_F()] is similar to that of Hist[MGAMA_C()] if Corr_FC ≥ 0.8 and dissimilar otherwise.
By computing, we obtain Corr_FC = 0.01751 for OMW111999AF and OMW211999CF, implying the pattern of Hist[MGAMA_F()] considerably differs from that of Hist[MGAMA_C()] as indicated in Figure 8(c). We will further demonstrate this interesting phenomenon in the next section.
4. Experimental Results
The value of Corr_FC for OMW111999AF and OMW211999CF has been mentioned above. In this section, we illustrate experimental results describing Corr_FC for OMW121999AF and OMW221999CF. The plots to illustrate Corr_FC for OMW131999AF and OMW231999CF, OMW141999AF and OMW241999CF, OMW151999AF and OMW251999CF and are listed in the appendices.
Figures 9(a) and 9(b) are the plots of the first 1024 points of OMW121999AF and OMW221999CF, respectively. Figures 10(a) and 10(b) indicate the series of traffic rate bound for OMW121999AF and OMW221999CF for with , respectively. Figures 11(a) and 11(b) demonstrate the maxima of rate bound for both traffic traces for . Figures 12(a) and 12(b) show the histograms of the maxima of traffic rate bound for both traces. Figure 12(c) gives the comparison between two. By computation, we have Corr_FC = 0.163261, meaning that the pattern of Hist[MGAMA_F(n)] considerably differs from that of Hist[MGAMA_C(n)] for OMW121999AF and OMW221999AC.
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Note that the values of Corr_FC for other three pairs of test traces, see Figures 16(c), 20(c), and 24(c), also exhibit that the pattern of Hist[MGAMA_F(n)] is noticeably different from that of Hist[MGAMA_C(n)]. We summarize the values of Corr_FC of all five pairs of traces in Table 3, which shows that Corr_FC < 0.2 for all pairs of test traces.

5. Discussions and Conclusions
The maxima of rate bound of attackcontained traffic is not always higher than that of attackfree traffic, see Figure 7. Statistically, however, it is higher than that of attackfree traffic significantly as can be seen from the experimental results illustrated by Figures 8(c), 12(c), 16(c), 20(c), and 24(c). In addition, the results expressed in Table 3 indicate that the pattern of Hist[MGAMA_F(n)] is obviously different from that of Hist[MGAMA_C(n)]. Thus, the results in this paper suggest that the histogram of the maxima of traffic rate bound may yet be a traffic feature to distinctly identify abnormal variation of traffic under DDOS flood attacks.
In comparison with fractal model of traffic as discussed in , the present feature has an apparent advantage. Recall that statistical models like LRD processes, see, for example, , are usually for traffic in the aggregate case, but there is lack of evidence to use them to characterize statistical patterns of real traffic at connection. As a matter of fact, finding statistical patterns of traffic at connection may be a tough task. To overcome difficulties in describing traffic at connection level, bounded modeling is introduced [25–29]. Thus, if we let be all flows going through server from input link and let be the maximum traffic constraint function of , the present analysis method of traffic is technically sound and usable for but fractal models may not. Since the bounded models of traffic are mainly used at connection level in some applications, such as realtime admission control, it is clear that the present traffic feature for identifying abnormal variation of traffic under DDOS flood attacks can be extracted at early stage of attacks.
Appendices
These appendices gives experimental results for three pairs of traces. They are OMW131999AF and OMW231999CF, OMW141999AF and OMW241999CF, and OMW151999AF and OMW251999CF. The values of Corr_FC for each pair of traces are given in the captions of Figures 16(c), 20(c), and 24(c), respectively.
A. Experiments for OMW131999AF and OMW231999CF
See Figures 13, 14, 15, and 16.
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B. Experiments for OMW141999AF and OMW241999CF
See Figures 17, 18, 19, and 20.
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C. Experiments for OMW151999AF and OMW251999CF
See Figures 21, 22, 23, and 24.
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Acknowledgments
This work was supported in part by the 973 plan under the project number 2011CB302801/2011CB302802, by the National Natural Science Foundation of China under the project grant numbers, 60873264, 61070214, 61173096, by Zhejiang Provincial Natural Science Foundation of China (R1110679), and by the University of Macau.
References
 R. Shirey, Internet Security Glossary, RFC 2828, 2000.
 N. Hussain, Measurement and spectral analysis of denial of service attacks, Ph.D. dissertation, University of Southern California, 2005.
 S. Chebrolu, A. Abraham, and J. P. Thomas, “Feature deduction and ensemble design of intrusion detection systems,” Computers & Security, vol. 24, no. 4, pp. 295–307, 2005. View at: Publisher Site  Google Scholar
 E. G. Amoroso, Intrusion Detection: An Introduction to Internet Surveillance, Correlation, Traps, Trace Back, and Response, Intrusion.Net Books, 1999. View at: Zentralblatt MATH
 J. Mirkovic, S. Dietrich, D. Dittrich, and P. Reiher, Internet Denial of Service: Attack and Defense Mechanisms, Prentice Hall, 2004.
 K. Liston, “Intrusion Detection FAQ: can you explain traffic analysis and anomaly detection?” 2004, http://www.sans.org/securityresources/idfaq/anomaly_detection.php. View at: Google Scholar
 E. Schultz, “Intrusion prevention,” Computers and Security, vol. 23, no. 4, pp. 265–266, 2004. View at: Publisher Site  Google Scholar
 J. Leach, “TBSE—an engineering approach to the design of accurate and reliable security systems,” Computers and Security, vol. 23, no. 1, pp. 265–266, 2004. View at: Publisher Site  Google Scholar
 S. H. Oh and W. S. Lee, “An anomaly intrusion detection method by clustering normal user behavior,” Computers and Security, vol. 22, no. 7, pp. 596–612, 2003. View at: Publisher Site  Google Scholar
 F. Gong, “Deciphering detection techniques: part III denial of service detection,” White Paper, McAfee Network Security Technologies Group, 2003. View at: Google Scholar
 S. Sorensen, “Competitive overview of statistical anomaly detection,” White Paper, Juniper Networks, 2004. View at: Google Scholar
 S. B. Cho and H. J. Park, “Efficient anomaly detection by modeling privilege flows using hidden Markov model,” Computers and Security, vol. 22, no. 1, pp. 45–55, 2003. View at: Publisher Site  Google Scholar
 S. Cho and S. Cha, “SAD: web session anomaly detection based on parameter estimation,” Computers and Security, vol. 23, no. 7, pp. 312–319, 2004. View at: Publisher Site  Google Scholar
 R. A. Kemmerer and G. Vigna, “Intrusion detection: a brief history and overview,” Computer, vol. 35, pp. 27–30, 2002. View at: Google Scholar
 E. E. Schultz, “Representing information security fairly and accurately,” Computers and Security, vol. 25, no. 4, p. 237, 2006. View at: Publisher Site  Google Scholar
 S. S. Kim, A. L. Narasimha Reddy, and M. Vannucci, “Detecting traffic anomalies through aggregate analysis of packet header data,” Lecture Notes in Computer Science, vol. 3042, pp. 1047–1059, 2004. View at: Google Scholar
 M. Li, “An approach to reliably identifying signs of DDOS flood attacks based on LRD traffic pattern recognition,” Computers and Security, vol. 23, no. 7, pp. 549–558, 2004. View at: Publisher Site  Google Scholar
 M. Li, “Change trend of averaged Hurst parameter of traffic under DDOS flood attacks,” Computers and Security, vol. 25, no. 3, pp. 213–220, 2006. View at: Publisher Site  Google Scholar
 A. Scherrer, N. Larrieu, P. Owezarski, P. Borgnat, and P. Abry, “NonGaussian and long memory statistical characterizations for Internet traffic with anomalies,” IEEE Transactions on Dependable and Secure Computing, vol. 4, no. 1, pp. 56–70, 2007. View at: Publisher Site  Google Scholar
 B. Tsybakov and N. D. Georganas, “Selfsimilar processes in communications networks,” Institute of Electrical and Electronics Engineers. Transactions on Information Theory, vol. 44, no. 5, pp. 1713–1725, 1998. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 M. Li, “Modeling autocorrelation functions of longrange dependent teletraffic series based on optimal approximation in Hilbert spaceA further study,” Applied Mathematical Modelling, vol. 31, no. 3, pp. 625–631, 2007. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 M. Li and S. C. Lim, “Modeling network traffic using generalized Cauchy process,” Physica A, vol. 387, no. 11, pp. 2584–2594, 2008. View at: Publisher Site  Google Scholar
 M. Li and W. Zhao, “Detection of variations of local irregularity of traffic under DDOS flood attack,” Mathematical Problems in Engineering, vol. 2008, Article ID 475878, 2008. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 H. Michiel and K. Laevens, “Teletraffic engineering in a broadband era,” Proceedings of the IEEE, vol. 85, no. 12, pp. 2007–2032, 1997. View at: Google Scholar
 R. L. Cruz, “A calculus for network delay—I: network elements in isolation,” IEEE Transactions on Information Theory, vol. 37, no. 1, pp. 114–131, 1991. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 J.Y. Le Boudec, J. Yves, and T. Patrick, Network Calculus, A Theory of Deterministic Queuing Systems for the Internet, vol. 2050 of Lecture Notes in Computer Science, Springer, Berlin, Germany, 2001. View at: Publisher Site
 S. Wang, D. Xuan, R. Bettati, and W. Zhao, “Providing absolute differentiated services for realtime applications in staticpriority scheduling networks,” IEEE/ACM Transactions on Networking, vol. 12, no. 2, pp. 326–339, 2004. View at: Publisher Site  Google Scholar
 M. Li and W. Zhao, “Representation of a stochastic traffic bound,” IEEE Transactions on Parallel and Distributed Systems, vol. 21, no. 9, Article ID 5342414, pp. 1368–1372, 2010. View at: Publisher Site  Google Scholar
 M. Li and W. Zhao, “A model to partly but reliably distinguish DDOS flood traffic from aggregated one,” Mathematical Problems in Engineering, vol. 2012, Article ID 860569, 12 pages, 2012. View at: Google Scholar
 M. Li and W. Zhao, “Asymptotic identity in minplus algebra: a report on CPNS,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 154038, 11 pages, 2012. View at: Google Scholar
 J. McHugh, “Testing intrusion detection systems: a critique of the 1988 and 1999 DARPA intrusion detection system evaluations as performed by lincoln laboratory,” ACM Transactions Information System Security, vol. 3, no. 4, pp. 262–294, 2000. View at: Google Scholar
 J. W. Haines, L. M. Rossey, R. Lippmann, and R. K. Cunningharm, “Extending the DARPA offline intrusion detection evaluations,” in Proceedings of the DARPA Information Survivability Conference and Exposition II, vol. 1, pp. 77–88, IEEE, Anaheim, Calif, USA, 2001. View at: Google Scholar
 L. Feinstein, D. Schnackenberg, R. Balupari, and D. Kindred, “Statistical approaches to DDoS attack detection and response,” in Proceedings of the DARPA Information Survivability Conference and Exposition, vol. 1, pp. 303–314, Washington, DC, USA, 2003. View at: Google Scholar
 R. Lippmann, J. W. Haines, D. J. Fried, J. Korba, and K. Das, “The 1999 DARPA offline intrusion detection evaluation,” Computer Networks, vol. 34, no. 4, pp. 579–595, 2000. View at: Publisher Site  Google Scholar
 L. Garber, “Denialofservice attacks rip the internet,” Computer, vol. 33, no. 4, pp. 12–17, 2000. View at: Google Scholar
 D. Moore, G. M. Veolker, and S. Savage, “Inferring internet denialofservice activity,” in Proceedings of the 10th USENIX Security Symposium, 2001. View at: Google Scholar
 R. Mahajan, S. M. Bellovin, and S. Floyd, “Controlling high bandwidth aggregates in the network,” vol. 32, no. 3, pp. 62–73. View at: Publisher Site  Google Scholar
 A. Lakhina, M. Crovella, and C. Diot, “Characterization of networkwide anomalies in traffic flows,” in Proceedings of the ACM SIGCOMM Internet Measurement Conference (IMC '04), pp. 201–206, Sicily, Italy, October 2004. View at: Google Scholar
 P. Barford and D. Plonka, “Characteristics of network traffic flow anomalies,” in Proceedings of the 1st ACM SIGCOMM Internet Measurement Workshop (IMW '01), pp. 69–73, San Francisco, Calif, USA, November 2001. View at: Google Scholar
 V. A. Siris and F. Papagalou, “Application of anomaly detection algorithms for detecting SYN flooding attacks,” Computer Communications, vol. 29, no. 9, pp. 1433–1442, 2006. View at: Publisher Site  Google Scholar
 H. Wang, D. Zhang, and K. G. Shin, “Detecting SYN flooding attacks,” in Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1530–1539, New York, NY, USA, June 2002. View at: Google Scholar
 M. Li, J. Li, and W. Zhao, “Simulation study of flood attacking of DDOS,” in Proceedings of the IEEE 3rd International Conference on Internet Computing in Science and Engineering (ICICSE '08), pp. 289–293, Harbin, China, 2008. View at: Google Scholar
 R. Bettati, W. Zhao, and D. Teodor, “Realtime intrusion detection and suppression in ATM networks,” in Proceedings of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring, pp. 111–118, 1999. View at: Google Scholar
 K. S. Fu, Ed., Digital Pattern Recognition, Springer, 2nd edition, 1980.
 M. Basseville, “Distance measures for signal processing and pattern recognition,” Signal Processing, vol. 18, no. 4, pp. 349–369, 1989. View at: Publisher Site  Google Scholar
 M. Li, “An iteration method to adjusting random loading for a laboratory fatigue test,” International Journal of Fatigue, vol. 27, no. 7, pp. 783–789, 2005. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2012 Jie Xue et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.