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Effective Detection of Active Worms with Varying Scan Rate

Effective Detection of Active Worms with Varying Scan Rate

Wei Yu‡, Xun Wang†, Dong Xuan† and David Lee†

‡ Texas A&M University

† The Ohio State University

Wei Yu‡, Xun Wang†, Dong Xuan† and David Lee†

‡ Texas A&M University

† The Ohio State University

Presented by Xun Wang

[email protected]

Presented by Xun Wang

[email protected]

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Motivation & Contributions

• Motivation– Active worms are evolving– Existing worm detection can not detect them

effectively– Need to understand them and defend against them

• Contributions– Modeling Varying Scan Rate (VSR) worm– Designing attack target Distribution Entropy based

dynamiC (DEC) detection scheme for VSR and traditional worms

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Outline

• Traditional Worms• Varying Scan Rate Worm Modeling• Existing Worm Detection Schemes• DEC Worm Detection• Performance Evaluations • Discussions• Final Remarks

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Traditional Worms• Self-propagate by exploiting vulnerabilities of hosts

mostly through port scanning

• Scan strategy – Pure Random Scan (PRS): Pure randomly select IP addresses– Hitlist Scan: Use an externally supplied list of vulnerable hosts as

the targets– Local Subnet Scan: Scan the hosts in the same sub network first

• Scan rate– Constant: Does not change scan rate– Random changing scan rate

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Traditional PRS Worm Propagation Model• Traditional PRS worm

- PRS scan strategy with constant port scan rate

• Worm propagation model (Epidemic model [AM91])– S: port scan rate– M(i): the number of infected hosts at time tick i– N(i): the number of un-infected vulnerable hosts at time tick i

respectively – E(i + 1): the number of newly infected hosts from time tick i to i + 1– T: the number of IP addresses in the Internet

• Exponential increase of worm instance number (thus the scan

traffic volume observed by traffic monitors) Easy to be detected by existing detection systems

( )1( 1) ( )(1 (1 ) ),(1)S M iE i N i

T

( 1) ( ) ( 1), (2)M i M i E i

( 1) ( ) ( 1), (3)N i N i E i

( )1( 1) ( ) ( )(1 (1 ) ).(4)S M iM i M i N i

T

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Varying Scan Rate Worms

• Each VSR worm-infected victim (worm instance) adopts– a varying scan rate: S(t)

– a varying attack probability: Pa(t)

VSR worm

Traditional PRS worm

If S(t) is constant and Pa(t) = 1

Change scan strategy

Other worms

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VSR Worm Propagation Model

• VSR worm propagation model:

• VSR worm instance number observed by detection system:

where Pm is the percentage of IP addresses under monitoring.

If S(i)=S and Pa(i)=1

( ) ( ) ( )1( 1) ( ) ( )(1 (1 ) ).(5)aS i P i M iM i M i N i

T

( )1( 1) ( ) ( )(1 (1 ) ).(4)S M iM i M i N i

T

( )ˆ ( ) ( ) ( )[(1 (1 ) ), (6)S ia mM i M i P i P

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Effectiveness of VSR Worms (1)

• VSR worm propagation model is different from that of traditional worms

1( , ) max( , 2)

1

CS t K C

tK

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Effectiveness of VSR Worms (2)

• Detected worm instance number is not mono-increasing any more existing worm detection is not effective

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Worm Detection

• Global traffic monitoring based worm detection

• Distributed monitors passively record and report port scan traffic to the worm detection center [SANs, BCJ+05]

• The detection center determines whether there is a large-scale worm propagation using certain detection schemes

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• Three key elements – Detection data:

port scan record count, scan target (different IP) distribution

– Statistical property of worm detection data:

individual count, mean, variance, entropy

– Detection decision rule:

threshold-based,

trend-based,

static/dynamic rule

Worm Detection Space

• CISH: Count, Individual, Static tHreshold [VSG05]

• CVDH: Count, Variance, Dynamic tHreshold [WVG04]

• CISR: Count, Individual, Static tRend

[ZGT+03]

† Other subspaces other detection schemes?

• DVDH: Distribution, Variance, Dynamic tHreshold [Our extension of WVG04]

• DEC (or DEDH): Distribution, entropy,

Dynamic tHreshold [Ours]

Fig. 3. Space of worm detection.

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Ineffectiveness of Existing Detection Schemes to VSR worms

• Metrics:

- Detection Time (in minute) - Maximal Infection Ratio (%)

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DEC Worm Detection

• Attack target Distribution Entropy based dynamiC (DEC) worm detection

• Three key elements– Detection Data: distribution of worm scan/attack target IP, i.e.,

how many different IP addresses are scanned– Statistical property of worm detection data: entropy– Detection decision Rule: run-time dynamic threshold adaptation

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Why Worm Attack Target Distribution?

• Capture the fundamental feature of active worms• To propagate worm to as many hosts as possible, worm

port scan traffic’s target IP addresses must show a widely dispersed distribution

the worm scan/attack target distribution is a key feature to distinguish worm traffic from other traffic

• Example– Data-set1 = [(IP1, 8)] – Data-set2 = [(IP2, 1), (IP3, 1), (IP4, 1), (IP5, 1),(IP6, 1), (IP7, 1)]– By count, Data-set1’s count is 8 > Data-set2’s count is 6– But Data-set2 is more like worm scan traffic and its IP addresses

set is more distributed

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Why Entropy ?

• Entropy quantifies “the amount of uncertainty” contained in data or “the randomness” of the data– The entropy is 0 when the distribution of data is maximally

concentrated– It takes on the maximal value when the distribution is

maximally dispersed

• We use entropy to measure the target distribution, which is better than other measurements, such as variance

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• Entropy of port scan target distribution– From collected port scan reports in an unit time

Z = ((DestIP1; sn1); ... ; (DestIPM; snM)),

where sn1 is the number of times a IP DestIPi is scanned

– Entropy of Z: where

• Example:– Data-set1: Z1= [(IP1, 8)] – Data-set2: Z2= [(IP2, 1), (IP3, 1), (IP4, 1), (IP5, 1),(IP6, 1), (IP7, 1)]

How to Use Entropy?

1

( ) ( ) log( ),i i

i

sn snH Z

Y Y

1

.ii

Y sn

Variances of two data-sets are same and equal to 0Entropy of Z1 is 0, but entropy of Z2 is 0.78!

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Performance Evaluation• Metrics

- Detection Time (in minute) - Maximal Infection Ratio (%)

• Simulation setup- Real-world trace plus simulated worm traffic

• Evaluated worm detection schemes– CISH: Count, Individual, Static tHreshold – CVDH: Count, Variance, Dynamic tHreshold– CISR: Count, Individual, Static tRend– DVDH: Distribution, Variance, Dynamic tHreshold

– Our DEC (or DEDH): Distribution, entropy, Dynamic tHreshold

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Detection Time of DEC (1)

•DEC can detect VSR worm much faster than other detection schemes

•CISR (trend-based detection) can not detect VSR worm

Fig. 4. Detection time of detection schemes on VSR worms.

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Detection Time of DEC (2)

•DEC can detect traditional worm faster and earlier than other detection schemes

Fig. 5. Detection time of detection schemes on traditional PRS worms.

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Maximal Infection Ratio of DEC (1)

•DEC can detect VSR worm at its very early propagate stage

Fig. 6. Maximal infection ratio of detection schemes on VSR worms.

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Maximal Infection Ratio of DEC (2)

Fig. 7. Maximal infection ratio of detection schemes on traditional PRS worms.

•Higher scan rate worms get detected earlier, and propagate less eventually

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Discussions• Worm Modeling

– Evolving worms: e.g., Atak worm [Zdnet] – VSR worm: varying scan rate– Determination of optimal S(t) and Pa(t) functions

• Detection– Why DEC is effective?

- Attack target distribution - Entropy

– Limitations?- Needs scan target distribution information- Do not protect individual sub network or host

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Final Remarks

• We formally modeled VSR worm and designed DEC worm detection

• Future work– Investigate other potential evolving worms which attempt to

camouflage worm propagation – Design effective detection against them– Example: Self-adjusting worm and detection, ACSAC’06

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References[AM91] R. M. Anderson and R. M. May, Infectious Diseases of

Humans:Dynamics and Control, Oxford University Press, Oxford, 1991.

[BCJ+05] M. Bailey, E. Cooke, F. Jahanian, J. Nazario, and D. Watson. “Internet motion sensor: A distributed blackhole monitoring system”, NDSS’05.

[SANs] SANs, Internet Storm Center, http://isc.sans.org/.[WVG04] J. Wu, S. Vangala, and L. X. Gao, “An effective architecture

and algorithm for detecting worms with various scan techniques,” NDSS’04.

[ZGT02] C. C. Zou, W. Gong, and D. Towsley, “Code red worm propagation modeling and analysis,” CCS’02.

[ZGT+03] C. Zou, W. B. Gong, D. Towsley, and L. X. Gao, “Monitoring and early detection for internet worms,” CCS’03.

[Zdnet] Zdnet, “Smart worm lies low to evade detection”, http://news.zdnet.co.uk/internet/security/0,39020375,39160285,00.htm.

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