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Optimizing Traffic Enforcement: From the Lab to the Roads Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Proactive Defense Against Physical Denial of Service Attacks Using Poisson Signaling Games

Authors : Jeffrey Pawlick, Quanyan Zhu

Published in: Decision and Game Theory for Security

Publisher: Springer International Publishing

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Abstract

While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks. In particular, distributed denial-of-service (DDoS) attacks overload the bandwidth of a server. But many IoT devices form part of cyber-physical systems (CPS). Therefore, they can be used to launch “physical” denial-of-service attacks (PDoS) in which IoT devices overflow the “physical bandwidth” of a CPS. In this paper, we quantify the population-based risk to a group of IoT devices targeted by malware for a PDoS attack. In order to model the recruitment of bots, we develop a “Poisson signaling game,” a signaling game with an unknown number of receivers, which have varying abilities to detect deception. Then we use a version of this game to analyze two mechanisms (legal and economic) to deter botnet recruitment. Equilibrium results indicate that (1) defenders can bound botnet activity, and (2) legislating a minimum level of security has only a limited effect, while incentivizing active defense can decrease botnet activity arbitrarily. This work provides a quantitative foundation for proactive PDoS defense.

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previous chapter A Stackelberg Game and Markov Modeling of Moving Target Defense
next chapter A Large-Scale Markov Game Approach to Dynamic Protection of Interdependent Infrastructure Networks
Appendix
Available only for authorised users
Footnotes
1
This is based on the idea that deceptive senders have a harder time communicating some messages than truthful senders. In interpersonal deception, for instance, lying requires high cognitive load, which may manifest itself in external gestures [23].
 
2
This could literally be a hardware or software detector, such as email filters which attempt to tag phishing emails. But it could also be an abstract notion meant to signify the innate ability of a person to recognize deception.
 
3
In fact, although all receivers with the same type y have the same likelihood \(\delta _{y}(e\,|\,x,m)\) of observing evidence e given sender type x and message m, our formulation allows the receivers to observe different actual realizations e of the evidence.
 
4
A second string can also be considered for the username.
 
5
For strong and active receivers, \(\delta _{y}\left( b\,|\,d,p\right) >\delta _{y}\left( b\,|\,l,p\right) ,\) \(y\in \{o,v\}.\) That is, these receivers are more likely to observe suspicious evidence if they are interacting with a malicious sender than if they are interacting with a legitimate sender. Mathematically, \(\delta _{k}(b\,|\,d,p)=\delta _{k}(b\,|\,l,p)\) signifies that type k receivers do not implement a detector.
 
6
We abuse notation slightly to write \(\bar{U}_{v}^{R}(a\,|\,m,e,\mu _{y}^{R})\) for the expected utility that R of type v obtains by playing action a.
 
7
In Fig. 8(b), \(\sigma _{v}^{R*}(f\,|\,p,b)=1\) for \(\upomega _{d}^{f}=-12.\)
 
8
A natural interpretation in an evolutionary game framework would be that \(\sigma _{d}^{S*}(p)=1,\) and \(q^{S}(d)\) decreases when the total activity is bounded. In other words, malicious senders continue recruiting, but some malicious senders drop out since not all of them are supported in equilibrium.
 
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Metadata
Title
Proactive Defense Against Physical Denial of Service Attacks Using Poisson Signaling Games
Authors
Jeffrey Pawlick
Quanyan Zhu
Copyright Year
2017
Book
Decision and Game Theory for Security

Print ISBN: 978-3-319-68710-0

Electronic ISBN: 978-3-319-68711-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68711-7_18

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