Attack graphs are a valuable tool to network defenders, illustrating paths an attacker can use to gain access to a targeted network. Defenders can then focus their efforts on patching the vulnerabilities and configuration errors that allow the attackers the greatest amount of access. We have created a new type of attack graph, the multiple-prerequisite graph, that scales nearly linearly as the size of a typical network increases. We have built a prototype system using this graph type. The prototype uses readily available source data to automatically compute network reachability, classify vulnerabilities, build the graph, and recommend actions to improve network security. We have tested the prototype on an operational network with over 250 hosts, where it helped to discover a previously unknown configuration error. It has processed complex simulated networks with over 50,000 hosts in under four minutes.

/pdf/practical-attack-graph-generation-for-network-defense-2bww4ler79.pdf

Practical Attack Graph Generation for Network Defense

Capturing the uncertain aspects in cyber security is important for security analysis in enterprise networks. However, there has been insufficient effort in studying what modeling approaches correctly capture such uncertainty, and how to construct the models to make them useful in practice. In this paper, we present our work on justifying uncertainty modeling for cyber security, and initial evidence indicating that it is a useful approach. Our work is centered around near real-time security analysis such as intrusion response. We need to know what is really happening, the scope and severity level, possible consequences, and potential countermeasures. We report our current efforts on identifying the important types of uncertainty and on using Bayesian networks to capture them for enhanced security analysis. We build an example Bayesian network based on a current security graph model, justify our modeling approach through attack semantics and experimental study, and show that the resulting Bayesian network is not sensitive to parameter perturbation.

http://people.cis.ksu.edu/~xou/publications/dsn10.pdf

Using Bayesian networks for cyber security analysis

By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective countermeasures. This paper describes substantial enhancements to the NetSPA attack graph system required to model additional present-day threats (zero-day exploits and client-side attacks) and countermeasures (intrusion prevention systems, proxy firewalls, personal firewalls, and host-based vulnerability scans). Point-to-point reachability algorithms and structures were extensively redesigned to support "reverse" reachability computations and personal firewalls. Host-based vulnerability scans are imported and analyzed. Analysis of an operational network with 84 hosts demonstrates that client-side attacks pose a serious threat. Experiments on larger simulated networks demonstrated that NetSPA's previous excellent scaling is maintained. Less than two minutes are required to completely analyze a four-enclave simulated network with more than 40,000 hosts protected by personal firewalls.

/pdf/modeling-modern-network-attacks-and-countermeasures-using-1ysyjviffr.pdf

Modeling Modern Network Attacks and Countermeasures Using Attack Graphs

The cyber security modeling language (CySeMoL) is a modeling language for enterprise-level system architectures coupled to a probabilistic inference engine. If the computer systems of an enterprise are modeled with CySeMoL, this inference engine can assess the probability that attacks on the systems will succeed. The theory used for the attack-probability calculations in CySeMoL is a compilation of research results on a number of security domains and covers a range of attacks and countermeasures. The theory has previously been validated on a component level. In this paper, the theory is also validated on a system level. A test indicates that the reasonableness and correctness of CySeMoL assessments compare with the reasonableness and correctness of the assessments of a security professional. CySeMoL's utility has been tested in case studies.

/pdf/the-cyber-security-modeling-language-a-tool-for-assessing-2c6rppfc2r.pdf

The Cyber Security Modeling Language: A Tool for Assessing the Vulnerability of Enterprise System Architectures

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.

/pdf/k-zero-day-safety-a-network-security-metric-for-measuring-1ipema85bq.pdf

k-Zero Day Safety: A Network Security Metric for Measuring the Risk of Unknown Vulnerabilities

Defense in depth is a common strategy that uses layers of firewalls to protect Supervisory Control and Data Acquisition (SCADA) subnets and other critical resources on enterprise networks. A tool named NetSPA is presented that analyzes firewall rules and vulnerabilities to construct attack graphs. These show how inside and outside attackers can progress by successively compromising exposed vulnerable hosts with the goal of reaching critical internal targets. NetSPA generates attack graphs and automatically analyzes them to produce a small set of prioritized recommendations to restore defense in depth. Field trials on networks with up to 3,400 hosts demonstrate that firewalls often do not provide defense in depth due to misconfigurations and critical unpatched vulnerabilities on hosts. In all cases, a small number of recommendations was provided to restore defense in depth. Simulations on networks with up to 50,000 hosts demonstrate that this approach scales well to enterprise-size networks.

Mike Artz

Papers

Validating and Restoring Defense in Depth Using Attack Graphs