Cyber-Insurance as a Signaling Game: Self-Reporting and External Security Audits

October 2018·
Aron Laszka
Aron Laszka
,
Emmanouil Panaousis
,
Jens Grossklags
PDF
Abstract
An insurer has to know the risks faced by a potential client to accurately determine an insurance premium offer. However, while the potential client might have a good understanding of its own security practices, it may also have an incentive not to disclose them honestly since the resulting information asymmetry could work in its favor. This information asymmetry engenders adverse selection, which can result in unfair premiums and reduced adoption of cyber-insurance. To overcome information asymmetry, insurers often require potential clients to self-report their risks. Still, clients do not have any incentive to perform thorough self-audits or to provide comprehensive reports. As a result, insurers have to complement self-reporting with external security audits to verify the clients’ reports. Since these audits can be very expensive, a key problem faced by insurers is to devise an auditing strategy that deters clients from dishonest reporting using a minimal number of audits. To solve this problem, we model the interactions between a potential client and an insurer as a two-player signaling game. One player represents the client, who knows its actual security-investment level, but may report any level to the insurer. The other player represents the insurer, who knows only the random distribution from which the security level was drawn, but may discover the actual level using an expensive audit. We study the players’ equilibrium strategies and provide numerical illustrations.
Type
Venue
9th Conference on Decision and Game Theory for Security (GameSec 2018), 508–520
publications

Authors

Aron Laszka
Associate Professor

Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.

His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.

His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.