honeynet-based collaborative defense using improved highly predictive blacklisting algorithm

Honeynet-based Collaborative Defense using

Improved Highly Predictive Blacklisting Algorithm

Xi’an JiaoTong University

Xiaobo Ma, Jiahong Zhu, Zhiyu Wan, Jing Tao, Xiaohong Guan, Qinghua Zheng

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

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IntroductionIntroduction

Background

Internet attacks:

complicated & changing

Traditional defense:

passive & delay

Completely proactive defense:

impossible

Relatively proactive defense:

less delay

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IntroductionIntroduction

Related work

GWOL (Global Worst Offender Listing)

LWOL (Local Worst Offender Listing)

HPB (Highly Predictive Blacklisting )

HPB’s central idea:

– personalized blacklists for each contributor

– log-sharing system

– correlation between attackers and contributors

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IntroductionIntroduction

Motivation

Limitations of HPB:

Dependent on data contributors

Single metric of attacker’s severity

Fixed size of blacklists

To solve the problems:

HCDF (honeynet-based collaborative defense framework)

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IntroductionIntroduction

Central Idea

HCDF’s advantages:

Honeynet

Multiple metrics of attacker’s severity

Varying size of blacklists

HCDF’s goal:

Blacklists with high hit rate and defense rate

Reduce time delay in defending new attackers

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

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HCDF OverviewHCDF Overview

AttackAttack

Attack trafficAttack traffic

Schematic Diagram of HCDFSchematic Diagram of HCDFTraining processTraining process

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HCDF OverviewHCDF Overview

IHPBIHPB

High High similaritysimilarity

BlacklistsBlacklists

IHPB algorithm processIHPB algorithm process

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HCDF OverviewHCDF Overview

Defense(Testing) processDefense(Testing) process

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

IHPB AlgorithmIHPB Algorithm

Data preparation

An attack event:

1. attacker IP

2. victim’s subnet address

3. port

4. duration

5. total packet size

IHPB AlgorithmIHPB Algorithm

Relevance Ranking

An attack event:

1. attacker IP

2. victim’s subnet address

3. port

4. duration

5. total packet size

v1 v2 v3 v4

a1 ◎ ◎

a2 ◎ ◎

a3 ◎ ◎ ◎

a4 ◎ ◎

Attacker-Victim Matrix

IHPB AlgorithmIHPB Algorithm

Relevance Ranking

1. attacker IP

2. victim’s subnet address

K=ranki{[(I-αW)-1-I]B}

B v1 v2 v3 v4

a1 0 1 0 1

a2 1 0 0 1

a3 0 1 1 1

a4 1 0 1 0

Attacker-Victim Matrix

W v1 v2 v3 v4

v1 1 0 1/4 1/6

v2 0 1 1/4 1/3

v3 1/4 1/4 1 1/6

v4 1/6 1/3 1/6 1

IHPB AlgorithmIHPB Algorithm

Relevance Ranking

1. attacker IP

2. victim’s subnet address

K=ranki{[(I-αW)-1-I]B}

Relevance Ranking

K(i,j): the relevance rank of attacker aj in subnet vi

K v1 v2 v3 v4

a1 2 1 3 1

a2 4 3 4 2

a3 1 2 1 3

a4 3 4 2 4

IHPB AlgorithmIHPB Algorithm

Attacker Severity

Metrics of attacker’s severity

1. attacker IP

2. victim’s subnet address

3. port

4. duration

5. total packet size

F(j): final severity of attacker aj

I(a): amount of unique subnetsP(a): amount of unique ports

T(a): average duration of all attacks

B(a): average packet size in all attacks

IHPB AlgorithmIHPB Algorithm

Subnet Vulnerability

Metrics of subnet vulnerability

1. attacker IP

2. victim’s subnet address

3. port

4. duration

5. total packet size

G(i): final vulnerability of victim vi

P(v): amount of unique ports

T(v): average duration of all attacks

B(v): average packet size in all attacks

I(v): amount of unique attackers

IHPB AlgorithmIHPB Algorithm

Final Blacklist

Relevance ranking – K(i,j)

Attacker Severity – F(j)

Subnet Vulnerability – G(i)

Blacklisting:

1. F(i,j) = K(i,j) – βF(j)

2. larger G(i) – larger L(i). (L: length of blacklists)

3. smallest F(i,j) & L(i) – final blacklist

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

Experiment and Evaluation Experiment and Evaluation

Evaluation MetricsDefense Rate (DR)

Hit Rate (HR)

Collaborative Defense Rate (CDR)

Collaborative Missing Rate (CMR)

Experiment and Evaluation Experiment and Evaluation

Experiment Results

Time (hour)

%

0 2 4 6 8 100

10

20

30

40

50

60

IHPBHPBLWOLGWOL

Hit Rates of Four Blacklists

Experiment and Evaluation Experiment and Evaluation

Experiment Results

Time (hour)

%

Defense Rate of Four Blacklists

0 1 2 3 4 5 6 7 8 9

5

10

15

20

25

GWOLLWOLIHPBHPB

Experiment and Evaluation Experiment and Evaluation

Experiment Results

Time (hour)

%

CDRs of GWOL, HPB and IHPB

0 2 4 6 8 100

5

10

15

20

25

30

GWOLIHPBHPB

Experiment and Evaluation Experiment and Evaluation

Experiment Results

Time (hour)

%

CMRs of GWOL, HPB and IHPB

0 2 4 6 8 100

5

10

15

20

25

GWOLIHPBHPB

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OutlinesOutlines

IntroductionIntroduction

OverviewOverview

AlgorithmAlgorithm

ExperimentExperiment

ConclusionConclusion

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Conclusion & Future WorkConclusion & Future Work

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ConclusionsHoneynets provide abundant and accurate attack data

IHPB algorithm generates highly personalized and predictive blacklists

IHPB’s high collaborative defense rate and capability shows the great application value of HCDF

Future Work

More algorithms in HCDF with shorter training time and generate dynamic blacklists more timely

Thank you!