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16513 Finding Multi-step Attacks in Computer Networks using Heuristic Search and Mobile Ambients
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Nunes Leal Franqueira, V. (2009) Finding Multi-step Attacks in Computer Networks using Heuristic Search and Mobile Ambients. PhD thesis, University of Twente. CTIT Ph.D.-thesis series No. 09-154 ISBN 978-90-365-2923-5

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Official URL: http://dx.doi.org/10.3990/1.9789036529235

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Abstract

An important aspect of IT security governance is the proactive and continuous
identification of possible attacks in computer networks. This is complicated due
to the complexity and size of networks, and due to the fact that usually network
attacks are performed in several steps. This thesis proposes an approach
called MsAMS (Multi-step Attack Modelling and Simulation), demonstrated by a
proof-of-concept tool, to automatically find such multi-step attacks. The novelty
of MsAMS is the fact that it applies Mobile Ambients and Combinatorial Optimization,
more specifically Heuristic Search, to the domain of multi-step network
attacks. A variant of ambient calculus is used to model networks, and heuristic
search is used to simulate attackers searching for possible attacks in the modelled
network. Additionally, and in support to these two aspects, MsAMS uses algorithms
from the domain of Link Analysis Ranking, traditionally applied to the
domain of Web search.
Mobile Ambients allow us to fully represent the hierarchical topology of a network
as part of the network model itself. This is essential to relate insights gained
from the model to the real network. Furthermore, we can represent dynamics of
attacks such as credential theft, what increases the spectrum of possibilities available
for attackers since it allows considering non-vulnerable as well as vulnerable
hosts as attack steps.
Optimization allows managing the complexity of the problem of finding multi-step
attacks involving credentials without compromising the scalability of the
approach for practical use. Therefore, the MsAMS approach comprises: (i) a
formal representation of the solution which allows its automatic computation,
in our case, the representation of an attack step in a notation based on Mobile
Ambients, (ii) a search engine which implements a heuristic method for composing
attack steps into multi-step attacks, and (iii) fitness functions used by the
search engine for the selection of attack steps among alternatives, according to
automatically computed metrics.
Similar to search engines that use the structure of the World Wide Web to
score webpages, the MsAMS approach proposes the use of the structure of a
network to score network assets. In particular, MsAMS uses PageRank and HITS
ranking schemes as sources of scalable metrics to:
1. assign asset value automatically to all ambients represented in the network,
based on network connectivity rather than on financial value, providing an
absolute and comparable view of asset value. Those values support the
network administrator in the process of selecting a target.
2. assign a cost value automatically to all ambients represented in the network,
also based on network connectivity rather than on financial value, providing
an absolute and comparable view of cost for attack steps. Such a measure
of cost allows the incorporation of rationality to the ambient-attacker which
simulates a strategy of a real-attacker.

Item Type:PhD Thesis
Supervisors:Wieringa, R.J.
Assistant Supervisors:van Eck, P.A.T.
Research Group:EWI-IS: Information Systems
Research Program:CTIT-ISTRICE: Integrated Security and Privacy in a Networked World
Research Project:IPID: Integrated Policy-based Intrusion Detection
Uncontrolled Keywords:Attack Modelling, Link Analysis Ranking algorithms, PageRank, HITS
ID Code:16513
Deposited On:16 November 2009
More Information:statisticsmetis

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