Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming

Edward S. Canepa; Alexandre M. Bayen; Christian G. Claudel; Edward S. Canepa; Alexandre M. Bayen; Christian G. Claudel

doi:10.3934/nhm.2013.8.783

Networks and Heterogeneous Media

2013, Volume 8, Issue 3: 783-802. doi: 10.3934/nhm.2013.8.783

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Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming

1.
King Abdullah University of Science and Technology, Electrical Engineering Department, Thuwal, Makkah 23955, KSA
2.
University of California at Berkely, Electrical Engineering and Computer Sciences, Berkeley CA 94720-170

Received: 01 September 2012 Revised: 01 March 2013
68U35, 90C11, 90C90.

Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham-Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data generated by multiple sensors of different types, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for a specific decision variable. We use this fact to pose the problem of detecting spoofing cyber attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California.
- Mixed integer linear programming,
- hyperbolic partial differential equations,
- cybersecurity
Citation: Edward S. Canepa, Alexandre M. Bayen, Christian G. Claudel. Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming[J]. Networks and Heterogeneous Media, 2013, 8(3): 783-802. doi: 10.3934/nhm.2013.8.783

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Abstract

Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham-Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data generated by multiple sensors of different types, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for a specific decision variable. We use this fact to pose the problem of detecting spoofing cyber attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California.

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