Game Theoretic Deception and Threat Screening for Cyber Security


Aaron Schlenker. 8/2018. “Game Theoretic Deception and Threat Screening for Cyber Security ”. Thesis Type: PhD thesis.


Protecting an organization’s cyber assets from intrusions and breaches due to attacks by malicious actors is an increasingly challenging and complex problem. Companies and organizations (hereon referred to as the defender) who operate enterprise networks employ the use of numerous protection measures to intercept these attacks, such as Intrusion and Detection Systems (IDS) and along with dedicated Cyber Emergency Readiness Teams (CERT) composed of cyber analysts tasked with the general protection of an organization’s cyber assets. In order to optimize the use of the defender’s limited resources and protection mechanisms, we can look to game theory which has been successfully used to handle complex resource allocation problems and has several deployed real-world applications in physical security domains. Applying previous research on security games to cybersecurity domains introduce several novel challenges which I address in my thesis to create models that deceive cyber adversaries and provide the defender with an alert prioritization strategy for IDS. My thesis provides three main contributions to the emerging body of research in using game theory for cyber and physical security , namely (i) the first game theoretic framework for cyber deception of a defender’s network, (ii) the first gametheoretic framework for cyber alert allocation and (iii) algorithms for extending these frameworks to general-sum domains.

In regards to the first contribution, I introduce a novel game model called the Cyber Deception Game (CDG) model which captures the interaction between the defender and adversary during the recon phase of a network attack. The CDG model provides the first game-theoretic framework for deception in cybersecurity and allows the defender to devise deceptive strategies that deceptively alters system responses on the network. I study two different models of cyber adversaries and provide algorithms to solve CDGs that handle the computational complexities stemming from the adversary’s static view of the defender’s network and the varying differences between adversary models. The second major contribution of my thesis is the first game theoretic model for cyber alert prioritization for a network defender. This model, the Cyber-alert Allocation Game, provides an approach which balances intrinsic characteristics of incoming alerts from an IDS with the defender’s analysts that are available to resolve alerts. Additionally, the aforementioned works assume the games are zero-sum which is not true in many real-world domains. As such, the third contribution in my thesis extends CAGs to general-sum domains. I provide scalable algorithms that have additional applicability to other physical screening domains (e.g., container screening, airport passenger screening).

See also: 2018