1. In/Out Traffic Proportion Based Analyses for Network Anomaly Detection By Zhang FengXiang 2006-07-17

2. 2 Outline  Research background  Traffic analyses for anomaly detection: Based on input/output proportion of traffic Applying GLR test and Bin-test Numerical examples and discussions  Conclusions & further works

3. 3 What is the network anomaly  Anomaly: Operations deviate from normal behavior.  What could cause anomaly? Malfunction of network devices Network overload Malicious attacks, like DoS/DDoS attacks Other network intrusions  Two main kinds of network anomalies. 1. 1. Related to network failures and performance problems. 2. 2. Security-related problems: (1) Resource depletion (2) Bandwidth depletion

4. 4 Anomaly detection meets troubles  There are many schemes based on checking abrupt traffic changes. E.g. apply signal processing technique to detect out traffic ’ s abrupt change  However, this kind of anomaly does not always mean illegitimate. Abrupt change of traffic does not mean an attack has exactly happened  We call this case as: Legitimately-abrupt-change (LAC)

5. 5 Legitimately abrupt changes  Example 1: Famous information gateway websites, e.g. Yahoo.  When bombastic news is announced, it would appear.  Example 2: Special information announce center, e.g. the website of national meteorological agency  When a nature disaster is said to be coming, it would occur. Typhoon, Earthquake, Tsunami  Important outdoor holidays

6. 6 Existing anomaly detection schemes ’ trouble  For those detection schemes based on abrupt changes of the unidirectional traffic: When legitimately abrupt changes appear, false alarms might appear. However, the bidirectional traffic would have some kinds of symmetry:  Check the Input/Output traffic proportion.  Test their Generalized Likelihood Ratio (GLR).  Test expected proportion number in each special value range (Bin).

7. 7 Network Model of Analyzing In Out Input/Outpu t Proportion Near the protected object

8. In/Out Traffic Proportion Based Analyses In/Out proportion, GLR and Bin test……

9. 9 Detect abnormal changes of proportion  For existing LACs, we consider bidirectional traffics. For this case, the Input/Output proportion would not change abruptly as well It seems be in a relatively narrow range.  Due to the nature of the TCP protocol there is a loose symmetry on the In/Out packet rates.  In the legitimate use of networks, more are the request packets, more are the response packets. Almost all bandwidth attacks destroy this attribute.

10. 10 Generalized Likelihood Ratio test  In statistical analysis, network anomalies are modeled as correlated abrupt changes in time series of network data.  GLR shows the likelihood of the residuals in two adjacent windows. Abrupt changes are detected by comparing the variance in two windows.  When GLR is closer to 1, the data distribution in test window is more likely to happen after the learn window It is more likely to be anomaly when GLR is smaller then a preset threshold.

11. 11 How to do GLR test  Get the In/Out proportion sequence  Apply GLR scheme between two adjacent windows:

12. 12 Calculation of GLR  Abrupt changes in time series data can be modeled using an auto- regressive (AR) process. Abrupt changes are correlated in time, yet are short-range dependent.  As some other detection schemes, we use an AR process of order 1 here to model the data in a 80-sec window.

13. 13 S L, S S : the sample variance of the residual in the learn and test window S P : the pooled sample variance of two adjacent windows : the GLR with the value range (0,1] W: the length of each window

14. 14 The analyzed traffic data  Use 4 traffic sets between the Science Information Network (SINET) and other two commercial Internet exchange service networks, JaPan Internet eXchange (JPIX) and JPNAP. They are bit rates in: 1. 1. 24 hours on 10 Gigabit Ethernet line of JPIX from 17:44 on May 03, 2005. 2. 2. 24 hours on 10 Gigabit Ethernet line of JPIX from 13:06 on March 25, 2004. 3. 3. 4 hours on 10 Gigabit Ethernet line of JPIX from 14:01 to 18:01on March 24, 2004. 4. 4. 24 hours on 10 Gigabit Ethernet line of JPNAP from 17:44 on May 03, 2005.

15. 15 SINET   JPIX ( 1 day traffic )

16. 16 The GLR sequence of the bit rate proportion time series between JPIX and SINET

17. 17 SINET   JPNAP ( 1 day traffic )

18. 18 The GLR sequence of the bit rate proportion time series between JPNAP and SINET

19. 19 The percentage distribution of the GLR value  Most GLR values are close to 1, and mostly above 0.8.  This means the distribution of Input/Output traffic proportion is most likely to its former one.

20. 20 Bin-test scheme  According to proportion data, we can decide several value ranges (bins). From most frequently appearing value range to the seldom appearing value range  Give the expected number of proportions in each bin under the normal and legitimate case.  Test the data points in the observing window If not match the expected distribution of the bins, alert.

21. 21 Proportion of Gigabit Ethernet line of JPIX to SINET March 24/2004(14:01 -> 18:01)

22. 22 An illustration of Bin-test 1:others 2 3 4 5 1st Most common 2nd Most common Get the expected number N i in the ith bin; In higher level bin the N i should be larger. Normal seldom never Count data number n i in each bin; Compare n i with N i. If the deviation exceeds some confidence interval, an anomaly is declared.

23. 23 Bin-test based on Input/Output proportion  Four data sets ’ number distribution in 4 bins:

24. 24 Conclusions and future works  We ’ ve noticed the effects of the legitimately abrupt changes for anomaly detections.  Showed the bidirectional In/Out traffic monitored for the same networks is close to a constant. A valuable reference for reduction of false positive alarms in the detection of bandwidth attacks.  Proposed a Bin-test detection method based on traffic analysis.  In the future, Further study the In/Out traffic proportion constancy. Simulate DoS/DDoS attacks and apply the detection scheme.

25. Thank You! Advices?