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Paper Presentation
“Network Security Risk Assessment System
Based on Attack Graph and Markov Chain”
written by
Fuxiong Sun, Juntao Pi, Jin Lv and Tian Cao
presented by
Kishor Datta Gupta
COMP-8327
Friday 14 Feb 2018
Introduction
Propose new model for Network Security Risk Assessment
Model (NSRAM)
Based on
• Penetration test
• Attack graph
• CVSS
By Using
Markov chain : For Calculate the risk value
End result:
Detect the risk level with magnitude of probability
Background
• Markov chain :
A stochastic model describing a sequence of possible events in which the probability of
each event depends only on the state attained in the previous event.
• Attack Graphs:
Attack graphs depict ways in which an adversary exploits system vulnerabilities to achieve
a desired state.
• CVSS
Common Vulnerability Scoring System
Related works
NSRAM model based on
• Bayesian network and event tree by Jinsoo shin
• Fuzzy, AHP and D-S evidence theories by Fang yang
• Risk theory by Advanced material research
• Immunity algorithm by Yuah Ha
• Polynomial complexity with network flow Attack
• Attack Graph based on graph kernel by Yu yanjun
• Markov time varying model by Wang Xiao
NSRAM Structure
Modules
• Information acquisition
• Penetration test
• Automatic Generation of attack graph
• Attack graph evaluation
Information Acquisition
• Network Environment
• Test steps
• Target information – five tuple pattern
 Host_id : host identifier
 Host_vulSet : Vulnerabilities in host with CVE score
 Host_con: Network of Host
 Host_port: Open ports in host
 Host_data: Host weight based on location & importance
Penetration Implementation
• Ruby script call security tools
• Tools scan and penetrate network
• Use host_vulset to generate report
Used tools: burp suite, nikto, metasploit
Automatic Generation of Attack Graph
Breadth-First traversal use to generate graph
• Att _id represents an attacker.
• Att_start is the Host_id which starts to attack.
• Att_target is the Host_id of target host.
• Att_getAuth is access rights obtained after a successful attack.
• Att_other refers to the attack path, attack time, attack ability
and other information.
Attack Graph Evaluation
• CVSS standard is used to score vulnerabilities on an
atomic node.
• According to Host_con, all attack paths are drawn
• NSRAM takes the largest probability of attack path as
the whole risk probability of the system
System Modeling(Attack Graph)
Attack graph is defined as 𝐴 = (𝐴𝑠 ∪ 𝐴𝑑 , 𝑇, 𝑅, 𝐸).
As=Starting node
Ad=destination node
T=an attack of node
R=Relation between state
E= Directed edges
Markov Chain Risk Assessment
MC=(I, P, A)
where
• I= {𝑏1, 𝑏2, … 𝑏𝑚} is the state space
• P =transition probability matrix.
• A={𝐺1, 𝐺2, 𝐺3, . . . , 𝐺𝑘 }. The A is called the set of all available
attack actions.
Markov Chain Risk Assessment(cont..)
Attack Graph
Experiment
Experiment
Attack Graph with Transition Probability
Attack Graph Evaluation
Conclusion
• Automatically and periodically collect network
topology and vulnerability data
• Combining the improved CVSS and probability
evaluation of attack path
Future work:
Incremental evaluation of attack graph and path will
greatly improve the efficiency of the model
The End – Thank You!

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Information security

  • 1. Paper Presentation “Network Security Risk Assessment System Based on Attack Graph and Markov Chain” written by Fuxiong Sun, Juntao Pi, Jin Lv and Tian Cao presented by Kishor Datta Gupta COMP-8327 Friday 14 Feb 2018
  • 2. Introduction Propose new model for Network Security Risk Assessment Model (NSRAM) Based on • Penetration test • Attack graph • CVSS By Using Markov chain : For Calculate the risk value End result: Detect the risk level with magnitude of probability
  • 3. Background • Markov chain : A stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. • Attack Graphs: Attack graphs depict ways in which an adversary exploits system vulnerabilities to achieve a desired state. • CVSS Common Vulnerability Scoring System
  • 4. Related works NSRAM model based on • Bayesian network and event tree by Jinsoo shin • Fuzzy, AHP and D-S evidence theories by Fang yang • Risk theory by Advanced material research • Immunity algorithm by Yuah Ha • Polynomial complexity with network flow Attack • Attack Graph based on graph kernel by Yu yanjun • Markov time varying model by Wang Xiao
  • 6. Modules • Information acquisition • Penetration test • Automatic Generation of attack graph • Attack graph evaluation
  • 7. Information Acquisition • Network Environment • Test steps • Target information – five tuple pattern  Host_id : host identifier  Host_vulSet : Vulnerabilities in host with CVE score  Host_con: Network of Host  Host_port: Open ports in host  Host_data: Host weight based on location & importance
  • 8. Penetration Implementation • Ruby script call security tools • Tools scan and penetrate network • Use host_vulset to generate report Used tools: burp suite, nikto, metasploit
  • 9. Automatic Generation of Attack Graph Breadth-First traversal use to generate graph • Att _id represents an attacker. • Att_start is the Host_id which starts to attack. • Att_target is the Host_id of target host. • Att_getAuth is access rights obtained after a successful attack. • Att_other refers to the attack path, attack time, attack ability and other information.
  • 10. Attack Graph Evaluation • CVSS standard is used to score vulnerabilities on an atomic node. • According to Host_con, all attack paths are drawn • NSRAM takes the largest probability of attack path as the whole risk probability of the system
  • 11. System Modeling(Attack Graph) Attack graph is defined as 𝐴 = (𝐴𝑠 ∪ 𝐴𝑑 , 𝑇, 𝑅, 𝐸). As=Starting node Ad=destination node T=an attack of node R=Relation between state E= Directed edges
  • 12. Markov Chain Risk Assessment MC=(I, P, A) where • I= {𝑏1, 𝑏2, … 𝑏𝑚} is the state space • P =transition probability matrix. • A={𝐺1, 𝐺2, 𝐺3, . . . , 𝐺𝑘 }. The A is called the set of all available attack actions.
  • 13. Markov Chain Risk Assessment(cont..)
  • 17. Attack Graph with Transition Probability
  • 19. Conclusion • Automatically and periodically collect network topology and vulnerability data • Combining the improved CVSS and probability evaluation of attack path Future work: Incremental evaluation of attack graph and path will greatly improve the efficiency of the model The End – Thank You!