Details

Autor(en) / Beteiligte

• Fugate, Sunny
• Ferguson‐Walter, Kimberly

Titel

Artificial Intelligence and Game Theory Models for Defending Critical Networks with Cyber Deception

Ist Teil von

• The AI magazine, 2019-03, Vol.40 (1), p.49-62

Ort / Verlag

La Canada: American Association for Artificial Intelligence

Erscheinungsjahr

2019

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• Traditional cyber security techniques have led to an asymmetric disadvantage for defenders. The defender must detect all possible threats at all times from all attackers and defend all systems against all possible exploitation. In contrast, an attacker needs only to find a single path to the defender's critical information. In this article, we discuss how this asymmetry can be rebalanced using cyber deception to change the attacker's perception of the network environment, and lead attackers to false beliefs about which systems contain critical information or are critical to a defender's computing infrastructure. We introduce game theory concepts and models to represent and reason over the use of cyber deception by the defender and the effect it has on attacker perception. Finally, we discuss techniques for combining artificial intelligence algorithms with game theory models to estimate hidden states of the attacker using feedback through payoffs to learn how best to defend the system using cyber deception. It is our opinion that adaptive cyber deception is a necessary component of future information systems and networks. The techniques we present can simultaneously decrease the risks and impacts suffered by defenders and dramatically increase the costs and risks of detection for attackers. Such techniques are likely to play a pivotal role in defending national and international security concerns.