Su Zhang 1

Quick Review. Data Source – NVD. Six Most Popular/Vulnerable Vendors For Our Experiments. Why The Six Vendors Are Chosen. Data Preprocessing. Functions Available For Our Approach. Statistical Results Plan For Next Phase. 2

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National Vulnerability Database U.S. government repository of standards based vulnerability management data. Data included in each NVD entry Published Date Time Vulnerable softwares CPE Specification Derived data Published Date Time Month Published Date Time Day Two adjacent vulnerabilities CPE diff (v1,v2) Version diff CPE Specification Software Name Adjacent different Published Date Time ttpv Adjacent different Published Date Time ttnv 4

Linux: instances Sun: instances Cisco: instances Mozilla: instances Microsoft: instances Apple: instances. 5

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Huge size of nominal types (vendors and software) will result in a scalability issue. Top six take up 43.4% of all instances. We have too many vendors(10411) in NVD. The seventh most popular/vulnerable vendor is much less than the sixth. Vendors are independent for our approach. 8

NVD dataTraining/Testing dataset Starting from 2005 since before that the data looks unstable. Correct some obvious errors in NVD(e.g. cpe:/o:linux:linux_kernel:390). Attributes Published time : Only use month and day. Version diff: A normalized difference between two versions. Vendor: Removed. 9

Attributes Group vulnerabilities published at the same day- we can guarantee ttnv/ttpv are non-zero values. ttnv is the predicted attribute. For each software Delete its first bunch of instances. Delete its last bunch of instances. 10

v1= 3.6.4; v2 = 3.6; MaxVersionLength=4; v1= expand ( v1, 4 ) = v2 =expand ( v2, 4 ) = diff(v1, v2) = (3-3) * (6-6) * (4-0) * (0-0) * = 4 E -4 11

Vendor, soft, version, month, day, vdiff, ttpv, ttnv linux,kernel,2.6.18, 05, 02, 0, 70, 5 linux,kernel, , 05, 07,1.02E-4,5,

Least Mean Square. Linear Regression Multilayer Perceptron. SMOreg. RBF Network. Gaussian Processes. 13

Function: Linear Regression Training Dataset: 66% Linux(Randomly picked since 2005). Test Dataset: the rest 34% Test Result: Correlation coefficient Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances

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Mean absolute error : Root mean square error: 16

Relative absolute error: Root relative squared error: 17

Function: Least Mean Square Training Dataset: 66% Linux(Randomly picked since 2005). Test Dataset: the rest 34% Test Result: Correlation coefficient Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances

Function: Multilayer Perceptron Training Dataset: 66% Linux(Randomly picked since 2005). Test Dataset: the rest 34% Test Result: Correlation coefficient Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances

Function: RBF Network Training Dataset: 66% Linux(Randomly picked since 2005). Test Dataset: the rest 34% Test Result: Linear Regression Model ttnv = * pCluster_0_ Correlation coefficient Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances

Linear Regression: Not accurate enough but looks promising (correlation coefficient: ). Least Mean Square: Probably not good for our approach(negative correlation coefficient). Multilayer Perceptron: Looks good but it couldnt provide us with a linear model. 21

SMOreg: For most vendors, it takes too long time to finish (usually more than 80 hours). RBF Network: Not very accurate. Gaussian Processes: Runs out of heap memory for most of our experiments. 22

Adding CVSS metrics as predictive attributes. Binarize our predictive attributes (e.g. divide ttnv/ttpv into several categories.) Use regression SVM with multiple kernels. 23

Try to find out an optimal model for our prediction. Try to investigate how to apply it with MulVAL if we get a good model. Otherwise, find out the reason why it is not accurate enough. 24

Thank you! 25