Attack graphs are important tools for analyzing security vulnerabilities in enterprise networks. Previous work on attack graphs has not provided an account of the scalability of the graph generating process, and there is often a lack of logical formalism in the representation of attack graphs, which results in the attack graph being difficult to use and understand by human beings. Pioneer work by Sheyner, et al. is the first attack-graph tool based on formal logical techniques, namely model-checking. However, when applied to moderate-sized networks, Sheyner's tool encountered a significant exponential explosion problem. This paper describes a new approach to represent and generate attack graphs. We propose logical attack graphs, which directly illustrate logical dependencies among attack goals and configuration information. A logical attack graph always has size polynomial to the network being analyzed. Our attack graph generation tool builds upon MulVAL, a network security analyzer based on logical programming. We demonstrate how to produce a derivation trace in the Mul-VAL logic-programming engine, and how to use the trace to generate a logical attack graph in quadratic time. We show experimental evidence that our logical attack graph generation algorithm is very efficient. We have generated logical attack graphs for fully connected networks of 1000 machines using a Pentium 4 CPU with 1GB of RAM.

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-Since digital control systems were introduced to the market more than 30 years ago, the operational efficiency and stability gained through their use have fueled our migration and ultimate dependence on them for the monitoring and control of critical infrastructure. While these systems have been designed for functionality and reliability, a hostile cyber environment and uncertainties in complex networks and human interactions have placed additional parameters on the design expectations for control systems.

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Abstract.We propose a new model for estimating the time to compromise a system component that is visible to an attacker. The model provides an estimate of the expected value of the time-to-compromise as a function of known and visible vulnerabilities, and attacker skill level. The time-to-compromise random process model is a composite of three subprocesses associated with attacker actions aimed at the exploitation of vulnerabilities. In a case study, the model was used to aid in a risk reduction estimate between a baseline Supervisory Control and Data Acquisition (SCADA) system and the baseline system enhanced through a specific set of control system security remedial actions. For our case study, the total number of system vulnerabilities was reduced by 86% but the dominant attack path was through a component where the number of vulnerabilities was reduced by only 42% and the time-to-compromise of that component was increased by only 13% to 30% depending on attacker skill level.

Abstract.Various tools exist to analyze enterprise network systems and to produce attack graphs detailing how attackers might penetrate into the system. These attack graphs, however, are often complex and difficult to comprehend fully, and a human user may find it problematic to reach appropriate configuration decisions. This paper presents methodologies that can 1) automatically identify portions of an attack graph that do not help a user to understand the core security problems and so can be trimmed, and 2) automatically group similar attack steps as virtual nodes in a model of the network topology, to immediately increase the understandability of the data. We believe both methods are important steps toward improving visualization of attack graphs to make them more useful in configuration management for large enterprise networks. We implemented our methods using one of the existing attack-graph toolkits. Initial experimentation shows that the proposed approaches can 1) significantly reduce the complexity of attack graphs by trimming a large portion of the graph that is not needed for a user to understand the security problem, and 2) significantly increase the accessibility and understandability of the data presented in the attack graph by clearly showing, within a generated visualization of the network topology, the number and type of potential attacks to which each host is exposed.

Software consumers often need to choose between different software that provide the same functionality. Today, security is a quality that many consumers, especially system administrators, care about and will use in choosing one software system over another. An attack surface metric is a security metric for comparing the relative security of similar software systems [7]. The measure of a system's attack surface is an indicator of the system's security: given two systems, we compare their attack surface measurements to decide whether one is more secure than another along each of the following three dimensions: methods, channels, and data. In this paper, we use the attack surface metric to measure the attack surfaces of two open source FTP daemons: ProFTPD 1.2.10 and Wu-FTPD 2.6.2. Our measurements show that ProFTPD is more secure along the method dimension, ProFTPD is as secure as Wu-FTPD along the channel dimension, and Wu-FTPD is more secure along the data dimension. We also demonstrate how software consumers can use the attack surface metric in making a choice between the two FTP daemons.