Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model. In this paper, we propose a risk management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels. We show how to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multiobjective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.

Dynamic Security Risk Management Using Bayesian Attack Graphs

To protect critical resources in today's networked environments, it is desirable to quantify the likelihood of potential multi-step attacks that combine multiple vulnerabilities. This now becomes feasible due to a model of causal relationships between vulnerabilities, namely, attack graph. This paper proposes an attack graph-based probabilistic metric for network security and studies its efficient computation. We first define the basic metric and provide an intuitive and meaningful interpretation to the metric. We then study the definition in more complex attack graphs with cycles and extend the definition accordingly. We show that computing the metric directly from its definition is not efficient in many cases and propose heuristics to improve the efficiency of such computation.

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An Attack Graph-Based Probabilistic Security Metric

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.

NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems

DAG-based attack and defense modeling: don’t miss the forest for the attack trees

Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects. Our previous work tackle this issue by exploring the causal relationships between vulnerabilities encoded in an attack graph. However, the evolving nature of vulnerabilities and networks has largely been ignored. In this paper, we propose a Dynamic Bayesian Networks (DBNs)-based model to incorporate temporal factors, such as the availability of exploit codes or patches. Starting from the model, we study two concrete cases to demonstrate the potential applications. This novel model provides a theoretical foundation and a practical framework for continuously measuring network security in a dynamic environment.

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Measuring network security using dynamic bayesian network

Given the increasing dependence of our societies on information systems, the overall security of these systems should be measured and improved. Existing work generally focuses on measuring individual vulnerabilities instead of measuring their combined effects. Recent research has explored the application of attack graphs and probabilistic security metrics to address this challenge. However, such work usually assumes metrics of individual vulnerabilities to be independently distributed and combines them in an arbitrary manner. They cannot address more realistic cases, such as exploiting one vulnerability makes another vulnerability easier to exploit. In this paper, we propose to model probability metrics based on attack graphs as a special Bayesian Network. This approach provides a sound theoretical foundation to such metrics. It can also provide the capabilities of using conditional probabilities to address the general cases of interdependency between vulnerabilities.

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Measuring Network Security Using Bayesian Network-Based Attack Graphs

Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. This chapter examines several approaches to combining the CVSS scores of individual vulnerabilities into an overall measure for network security. First, we convert CVSS base scores into probabilities and then propagate such probabilities along attack paths in an attack graph in order to obtain an overall metric, while giving special considerations to cycles in the attack graph. Second, we show that the previous approach implicitly assumes the metric values of individual vulnerabilities to be independent, and we remove such an assumption by representing the attack graph and its assigned probabilities as a Bayesian network and then derive the overall metric value through Bayesian inferences. Finally, to address the evolving nature of vulnerabilities, we extend the previous model to dynamic Bayesian networks such that we can make inferences about the security of dynamically changing networks.

Marcel Frigault

Papers

Measuring network security using dynamic bayesian network

Measuring Network Security Using Bayesian Network-Based Attack Graphs

Measuring the Overall Network Security by Combining CVSS Scores Based on Attack Graphs and Bayesian Networks