Intrusion Detection using NASA HTTP Logs AHMAD ARIDA DA CHEN
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1 Intrusion Detection using NASA HTTP Logs AHMAD ARIDA DA CHEN
2 Presentation Overview - Background - Preprocessing - Data Mining Methods to Determine Outliers - Finding Outliers - Outlier Validation -Summary
3 What is an Outlier? Definition: an outlier is an observation point that is distant from other observations Could occur randomly in any distribution Measurement error Irregular distribution Point of reference: In normally distributed data, 1 in every 22 observations will differ by 2x the standard deviation (or more) from the mean *** 1 in 370 will deviation by 3x
4 NASA HTTP Logs Collection of all HTTP requests to the NASA Kennedy Space Center server in Florida Collected from July 1 st 1995 July 31 st 1995 The Good: (Potentially) clean log files The Bad: Total of ~1.9 million access requests
5 Data Collection Note: ~1.9 million rows of data is beyond what Excel can handle
6 Pre-Processing of the Log File
7 The CSV File.
8 How to Start Looking at 1.9 million Rows? We have no real idea of the scope of our data Random outliers in our sample set: 2x mean -~86,000 3x mean -~5,000 Unsupervised Cluster Method Cluster Plot
9 First Attempt Use all rows and see if any patterns emerge
10 Second Attempt Use all rows and see if any patterns emerge
11 Now What? Cluster Plot helped to give us an idea of the general spread of the data Need to figure out if we can eliminate any rows Discretize some data Gain some statistical data
12 SQL Server to the Rescue
13 Learning about our data Host Count piweba3y.prodigy.com piweba4y.prodigy.com piweba1y.prodigy.com 9868 alyssa.prodigy.com 7852 siltb10.orl.mmc.com 7573 piweba2y.prodigy.com 5922 edams.ksc.nasa.gov news.ti.com 4863 p19.t0.halsey.com 1 p131.ac.duke.edu 1 p18-14.dialup.uvic.ca 1 p211.cc.uch.gr 1 pandora.physics.ox.ac.uk 1 panther1297.eiu.edu 1 panther1657.eiu.edu 1 p6mac3.lanl.gov 1
14 Learning about our data
15 Learning about our data
16 Learning about our data
17 Discretization
18 Cluster Plot 3.0
19 Cluster Plot 3.1
20 Cluster Plot 3.1 Group ,POST,/ksc.html,501,- ramsay.ann-arbor.mi.us,head,/shuttle/technology/sts-newsref/sts-lcc.html#sts-countdown,404,- Group 2 This point was near the middle of the screen and likely associated with the POST grouping: newcastle03.nbnet.nb.ca,post,/ksc.html,501,- Group 3 The one in the middle of the screen is associated with a byte amount of 7634: ajohnson.ssc.nasa.gov,get,/shuttle/missions/sts-71/images/images.html,200,7634
21 It s Wabbit Season Now that we have some potential outliers, what do they have in common? Are they real outliers? Are they intrusion attempts?
22 Common / Uncommon Traits
23 Common / Uncommon Traits
24 Common / Uncommon Traits Looked very promising, but didn t help predict an outlier Requested links with bytes of - Bytes of - with Reply Code of 501
25 MahalanobisDistance Gather information about multi-dimensional datasets that measures the standard deviations from the mean of the distribution of the points
26 MahalanobisDistance
27 Identify MahalanobisOutliers Mahalanobis Distance Count % of Total ( )
28 Re-Clean Data The largest deviations turned out to be due to null values that were left from our original preprocessing steps Re-pre-process Try cluster plot analysis again
29 Re-Clean Data The largest deviations turned out to be due to null values that were left from our original preprocessing steps Re-pre-process Try cluster plot analysis again
30 Cluster Plot 4.0
31 Mahalanobis2.0
32 Common Features!! Host TimeStamp TimeZone Command tia1.eskimo.com 20/Jul/1995:08:29: HEAD slip on.ca.ibm.net 16/Jul/1995:03:07: POST ramsay.ann-arbor.mi.us 06/Jul/1995:02:29: HEAD RequestLink HTTP ReplyCode Bytes /robots.txt HTTP/ /cgi-bin/webquery HTTP/ /shuttle/technology/sts-newsref/sts-lcc.html#sts-countdown HTTP/
33 Finding Outliers
34 Are We Sure?
35 Likely Intrusion Attempts 404 reply Tracked by security sites and can reveal hacking attempts POST Command to a web server to accept and store data in a message Is used in malicious attempts to embed information within a server without authorization HEAD Command to retrieve representation of a specific source Used to retrieve meta-data
36 Outliers Outliers are points in a data set that lie far away from the estimated value of the center of the data set. This estimated center could be either the mean, or median, depending on what kind of point or interval estimate you re using. Put a good picture here
37 Outliers Test
38 Chi-Squared Test The chi-squared test of independenceis one of the most basic tests in the statistical analysis.when you are given 2 categorical random variables, the chi-squared test of independence determines whether or not there exists a statistical dependence.
39 Grubb s Test
40 Grubb s test can be used to determine whether or not a single outlying value within a set of measurements varies sufficiently from the mean value that it can be statistically classified as not belonging to the same population.
41 How Good Is Our Search For IntrustionAttempts? NASA has a 2 nd set of HTTP logs from Aug 1 st to Aug 31 st 1995 This data has not been through any of the above tests / steps See if we can directly find our intrusion attempts instead of having to perform data mining
42 Steps for New Log Files Download log file R Studio to clean, create CSV file Import into SQL Server Use original query to find intrusion attempts
43 Cleaning / Importing August Logs
44 Successful Implementation
45 Significance Using our query, we were able to detect highly likely intrusion attempts in our sample Rules from original set applied to a completely different data set, with similar results Found 15 intrusion access attempts per ~3.4 million records ( % ) Can be used to find (in real time) malicious attacks and/or track previous attempts over time
46 Methodology Review Download and clean data using R Studio Rounds of pre-processing Cluster plot, Mahalanobis, Outliers, Chi-Squared, Grubb s Tests Identification of potential outliers Determination of common traits Application of rules to new data set
47 Conclusions Pre-processing is the most important step! Unsupervised methods are useful when dealing with large data sets with no clear starting point Cluster plot analysis Many tests are needed to help identify potential outliers Rule generation may take multiple rounds/ attempts
48
49 Summary Designed of a new decision tree algorithm Robust and insensitive to size of classes Creation of a new measure, CCP, to counter the bias of Information Gain that towards the majority class. Top down and bottom up approach that use Fisher s exact test, and they yield a classifier that performs statistically better than traditional decision trees.
50 Traditional Decision Trees Decision trees such as C4.5 split an attribute whose partition provides the highest confidence High confidence rules do not necessarily imply high significance in imbalanced data
51 Class Confidence Proportion Decision Tree (CCPDT) Developed combat Information Gain that results in decision tree rules which are biased towards the majority class Method uses top-down plus bottom-up approach and the Fisher s exact test to prune branches of the tree which are not statistically significant.
52 Traditional Decision Trees CCP entropy is insensitive to class skewness (will always have a fixed pattern) Entropy is maximized when a node has an equal number of elements from both splitting classes
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