AI attack planning for emulated networks
Name
1252064242-MIT.pdf
Size
1.52 MB
Format
Adobe PDF
Checksum (MD5)
e943edc3f79a5708f177f2e7502caaa2
Author(s)
Reinstadler, Bryn Marie.
Advisor(s)
Una-May O'Reilly.
Alternative Title
Artificial intelligence attack planning for emulated networks
Date Issued
2021
Publisher
Massachusetts Institute of Technology
Abstract
In recent decades, cybersecurity has become one of the most pressing problems facing the private and public sectors alike. Cybersecurity threats are pervasive but extremely difficult to defend against, given the constantly changing vulnerability surfaces of ever-evolving networks. There has been an increasing drive for automated cyber attacking or red teaming, which allows cyberdefenders to build better discovery and response workflows. The last few years has seen a rise in availability of structured threat data for cybersecurity, which has made possible the use of new techniques for automated red teaming. This thesis proposes the use of traditional artificial intelligence (AI) planning with domain-specific adaptations for solving this cybersecurity automation problem. Our development of two successful AI planning systems for automated red teaming, ClassAttack and ConAttack, demonstrate the utility of our approach. ClassAttack consists of a classical planner which constructs static, executable attack scenarios that can be run on an emulated network. An extra degree of complexity but also realism is available in ConAttack, a contingent planner that inter-leaves planning and execution, to better simulate the attack of a real red teamer in real time. Both systems utilize a complex knowledge base which was engineered specifically for this cybersecurity application. These two systems and the knowledge engineering required to build them represent a significant and novel effort in the cybersecurity space.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 84-87).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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