MulVAL

MulVAL: A Logic-based Network Security Analyzer. To determine the security impact software vulnerabilities have on a particular network, one must consider interactions among multiple network elements. For a vulnerability analysis tool to be useful in practice, two features are crucial. First, the model used in the analysis must be able to automatically integrate formal vulnerability specifications from the bug-reporting community. Second, the analysis must be able to scale to networks with thousands of machines. We show how to achieve these two goals by presenting MulVAL, an end-to-end framework and reasoning system that conducts multihost, multistage vulnerability analysis on a network. MulVAL adopts Datalog as the modeling language for the elements in the analysis (bug specification, configuration description, reasoning rules, operating-system permission and privilege model, etc.). We easily leverage existing vulnerability-database and scanning tools by expressing their output in Datalog and feeding it to our MulVAL reasoning engine. Once the information is collected, the analysis can be performed in seconds for networks with thousands of machines. We implemented our framework on the Red Hat Linux platform. Our framework can reason about 84% of the Red Hat bugs reported in OVAL, a formal vulnerability definition language. We tested our tool on a real network with hundreds of users. The tool detected a policy violation caused by software vulnerabilities and the system administrators took remediation measures.


References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Lallie, Harjinder Singh; Debattista, Kurt; Bal, Jay: A review of attack graph and attack tree visual syntax in cyber security (2020)
  2. Liu, Jing; Zhang, Yuchen; Hu, Hao; Tan, Jinglei; Leng, Qiang; Chang, Chaowen: Efficient defense decision-making approach for multistep attacks based on the attack graph and game theory (2020)
  3. Hu, Hao; Liu, Yuling; Yang, Yingjie; Zhang, Hongqi; Zhang, Yuchen: New insights into approaches to evaluating intention and path for network multistep attacks (2018)
  4. Kordy, Barbara; Piètre-Cambacédès, Ludovic; Schweitzer, Patrick: DAG-based attack and defense modeling: don’t miss the forest for the attack trees (2014)
  5. Bai, Hao; Wang, Kunsheng; Hu, Changzhen; Zhang, Gang; Jing, Xiaochuan: Boosting performance in attack intention recognition by integrating multiple techniques (2011) ioport
  6. Cheng, Feng; Roschke, Sebastian; Schuppenies, Robert; Meinel, Christoph: Remodeling vulnerability information (2010)
  7. Kotapati, Kameswari; Liu, Peng; La Porta, Thomas F.: Evaluating MAPSec by marking attack graphs (2009) ioport
  8. Kotapati, Kameswari; Liu, Peng; La Porta, Thomas F.: Dependency relation based vulnerability analysis of 3G networks: Can it identify unforeseen cascading attacks? (2007) ioport