Formalizing Sensitivity in Static Analysis for Intrusion Detection

Abstract

A key function of a host-based intrusion detection system is to monitor program execution. Models constructed using static analysis have the highly desirable feature that they do not produce false alarms; however, they may still miss attacks. Prior work has shown a trade-off between efficiency and precision. In particular, the more accurate models based upon pushdown automata (PDA) are very inefficient to operate due to non-determinism in stack activity. In this paper, we present techniques for determinizing PDA models. We first provide a formal analysis framework of PDA models and introduce the concepts of determinism and stack-determinism. We then present the VP-Static model, which achieves determinism by extracting information about stack activity of the program, and the Dyck model, which achieves stack-determinism by transforming the program and inserting code to expose program state. Our results show that in run-time monitoring, our models slow execution of our test programs by 1% to 135%. This shows that reasonable efficiency needs not be sacrificed for model precision. We also compare the two models and discover that deterministic PDA are more efficient, although stack-deterministic PDA require less memory.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA449050

Entities

People

  • Barton P. Miller
  • Henry H. Feng
  • Jonathon T. Giffin
  • Somesh Jha
  • Wenke Lee
  • Yong Huang

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • C4I
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Alphabets
  • Anomaly Detection
  • Automata
  • Change Detection
  • Computer Programs
  • Computer Science
  • Detection
  • Detectors
  • Instrumentation
  • Intrusion
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Language
  • Measurement
  • Monitors
  • Sensitivity

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.
  • Software Engineering.