Proactive Intrusion Prevention and Response via Attack Graphs

This chapter is from the book

Practical Intrusion Analysis: Prevention and Detection for the Twenty-First Century: Prevention and Detection for the Twenty-First Century

Learn More Buy

This chapter is from the book

This chapter is from the book 

Practical Intrusion Analysis: Prevention and Detection for the Twenty-First Century: Prevention and Detection for the Twenty-First Century

Learn More Buy

Attack Modeling and Simulation

TVA decomposes attack graph generation into two phases: capture of an input network attack model and using the model to simulate multistep network penetration. The attack model represents the network configuration and potential attacker exploits. In attack simulation, the input model is analyzed to form an attack graph of causally interdependent exploits, according to user-specified constraints.

Network Attack Modeling

The network attack model includes aspects of the network configuration relevant to attack penetration and a set of potential attacker exploits that match attributes of the configuration. The TVA approach can apply to many different types of attack models, even noncyber models, as long as a common schema is employed across the model.

Figure 5-4 shows an example of one such schema for network models. This schema simply shows the hierarchical relationships among model elements (for example, a parent element "contains" its children). For clarity, the various attributes of the model elements are not shown, such as name attributes for machines and domains.

Figure 5-4 Example schema of network models.

In this model schema, a network is comprised of machines and/or machines organized into protection domains. Protection domains capture the idea that the set of machines in a domain implicitly have unrestricted access to one another's vulnerable services. This abstraction is a scalable alternative to having a completely connected subgraph within the attack graph. The domain reference allows for domains within domains (subdomains).

A machine includes subelements and attributes relevant for modeling network attack penetration (exploits). This includes operating system (an attribute of machine, not shown) connections to vulnerable services on other machines, sets of machines that are trusted, application programs on a machine, groups to which the machine belongs (for example, Windows NT domains), and user-defined generic attributes. A harden element defines the hardening of a vulnerability. (For example, exploitation of a given vulnerability on a given machine is omitted from the attack graph.)

A connection describes how a machine connects to potentially vulnerable services across the network, to ports on other machines, or to its own ports. This mirrors the Transmission Control Protocol/Internet Protocol (TCP/IP) reference model, in which a layered connectivity structure represents the various network architectures and protocols.⁴ A service connection indicates a running service on a destination machine, to which a source machine can connect.

Each connection is composed of a service or application type at the appropriate TCP/IP layer. For example, an HTTP connection specifies the Web server name/version at the Transport layer. Link-layer connectivity models exploit against the Address Resolution Protocol (ARP). This scopes attacks based on traffic sniffing, such as man-in-the-middle (MITM) attacks based on ARP poisoning. Application-layer connectivity models exploits rely on particular application configurations, trust relationships, or other high-level details.

To keep pace with emerging threats, you must continually monitor sources of reported vulnerabilities and add those to your database of modeled exploits. Attack graphs model an attacker exploit in terms of preconditions and postconditions and for generic attacker and victim machines, which are subsequently mapped to the target network. For convenience, map vulnerable network connections to known standard vulnerability identifiers, such as CVE⁵ and Bugtraq.⁶

For populating models automatically, map outputs of network-scanning tools to the network schema, which in turn provide preconditions for attack graph exploits. Figure 5-5 shows example output data for Centennial Discovery,⁷ which is a network-asset management tool. A Discovery agent deployed on a network host machine reports detailed host configuration data, such as product/manufacturer/version for each detected software component.

Figure 5-5 Red Hat Fedora discovered by the network-asset management too

The discovered host software information is then mapped to preconditions for modeled exploits. Figure 5-6 shows the preconditions and postconditions for exploitation of a Bugtraq vulnerability, in terms of generic attacker/victim machines. The preconditions are that the attacker can execute code on the attacking machine, and a vulnerable connection exists from attacker to victim, identified as Bugtraq 13232.

Figure 5-6 The preconditions and postconditions for the identified Red Hat Fedora machine

Symantec DeepSight,⁸ a Web service direct feed of the Bugtraq database, gives the vulnerable software components for each reported vulnerability. Host configuration data gathered from an asset management tool, such as Discovery, generally differs from software descriptions in DeepSight. So discovered host software components need to be mapped to corresponding vulnerability records, as Figure 5-7 shows. This figure also shows a Discovery software description for Red Hat Fedora 4 mapped to Bugtraq vulnerability 13232. Symantec DeepSight has fields that correspond to product/manufacturer/service that help you with this mapping by matching against Discovery through regular expressions.

Figure 5-7 Software-to-vulnerability mapping indicates that a version of Linux has a particular Bugtraq vulnerability

Figure 5-8 illustrates a resulting connection to vulnerable software (Bugtraq 13232) on the host machine. This connection is built into the attack model by mapping the discovered host software to a known vulnerability. Then, because a connection with Bugtraq 13232 is a precondition for a particular exploit, this exploit might be included in this network's attack graph.

Figure 5-8 Network connection to vulnerable software specifies that a particular machine connects to another, with a given Bugtraq vulnerability on the destination machine

The Discovery asset management tool also defines protection domains, such as sets of machines with full connectivity to one another's vulnerable services (see Figure 5-9). Each protection domain is identified along with its member machines.

Figure 5-9 Protection domains reported by the asset management tool

The purpose of modeling the network configuration is to support preconditions of modeled attacker exploits. As this chapter has shown, you can map software components to their reported vulnerabilities. Alternatively, you can run remote vulnerability scans with tools such as Nessus, Retina,⁹ or FoundScan.¹⁰ With this approach, the tool actively tests for the existence of host vulnerabilities. The scanner reports a detected vulnerability explicitly by using a standard vulnerability identifier instead of reporting a particular software component. The corresponding exploit precondition is written in terms of this vulnerability identifier.

An advantage of this approach is that you can capture the effects of connectivity-limiting devices, such as routers and firewalls. That is, you scan from different network vantage points, targeting hosts through firewalls. The idea is that the scanner assumes the role of an attacker who reaches a certain point in the network. Thus, you avoid creating any special firewall exceptions for the scanning machine, which is typically done for network vulnerability scans.

You then combine multiple scans from various network locations, building a complete map of connectivity to vulnerable services throughout the network. Alternatively, you can directly analyze firewall rules, adding the resulting vulnerable connections to the model. In this case, only local subnet scans are needed.

Attack Simulation

In attack simulation, modeled exploits are matched against the network configuration model, which forms an attack graph of causally interdependent exploits, according to user-specified simulation constraints. Because the model is prepopulated through network scans and vulnerability databases, all that remains is defining the attack scenario (for example, the starting point, the attack goal, and any what-if changes to the network configuration).

In other words, given an input model of network configuration and attacker exploits, the exploits are instantiated for specific attacker/victim machine pairs in the network. Preconditions for instantiated exploits are tested, and resulting postconditions are matched with preconditions of other exploits. Figure 5-10 shows an exploit that has been instantiated for particular machines in the network model. The attacker and victim machines are no longer generic; they are defined for actual machines in the network.

Figure 5-10 Exploit instantiated for particular network. Attacker and victim are actual network machines, and preconditions are satisfied from the network model.

An attack graph also needs to follow the structure of protection domains defined for the network. Within a protection domain, it is assumed that each machine has unrestricted connectivity to vulnerabilities on all other machines in the domain. This implies that the attack graph is completely connected with a domain.

Figure 5-11 shows example protection domains in attack graph data. Within each domain, the set of all member machines is specified, as well as exploits relevant to each domain. Two possible types of exploits exist: within-domain and across-domain. Within-domain exploits are only accessible to machines within the protection domain. Thus, it is sufficient to specify only the victim machine, because the attacking machines are implicit. Across-domain exploits are those that attack machines in other domains. Those exploits have both attacker and victim machines specified.

Figure 5-11 Protection domains in attack graph data

An attack graph can be completely unconstrained (for example, all possible attack paths regardless of assumed starting and ending points in the network). In such a scenario, the source of the threat is assumed unknown, and no particular critical network assets are identified as specific attack goals. Figure 5-12 shows an example of such an unconstrained attack graph.

Figure 5-12 An unconstrained attack graph scenario

Another option is to constrain the attack graph to a given starting point (or points) for the attack. The idea is that the origin of the attack is assumed, and only paths that can be reached from the origin are included. Figure 5-13 shows an example attack graph in which the attack starting point (Internet) is specified.

Figure 5-13 An attack graph with constrained starting point

Another option is to constrain the attack graph so that it ends at a given ending point (or points) serving as the attack goal. Here, the idea is that certain critical network assets are to be protected, and only attack paths that reach the critical assets are included. This option can be exercised alone, with an unconstrained starting point, or combined with a constrained starting point. Figure 5-14 shows an example of the latter, in which both the attack starting point (Internet) and attack ending point (Databases) are specified.

Figure 5-14 Attack graph with constrained starting and ending points

The motivation for constraining the attack graph is to reduce the scope of the graph to the expected attack scenarios, which eliminates unnecessary clutter. For example, in Figure 5-14, the outgoing edges from the Database protection domain are omitted. If the primary goal is to protect the databases, attacks away from there are less important, (because, for example, the databases have already been compromised). Similarly, any attacks into the starting point can be omitted, because the attacker already has control of it.

Particularly important attack paths to consider are the most direct ones, such as the shortest paths from attack start and/or attack goal (see Figure 5-15). Two scenarios are considered. In Figure 5-15(a), the graph shows direct (shortest) paths from a given starting point. In Figure 5-15(b), both the attack starting point and goal points are given. The graph shows all direct paths from the starting point to the goal point.

Figure 5-15 Attack graph constrained to direct attacks from (a) the given starting point and (b) the given starting and ending points.

Again, the idea is to identify the most critical paths and vulnerabilities, for preattack network hardening and real-time alarm correlation, prediction, and response. Thus, given the assumed threat sources, attacker behavior, and critical network resources, you can tailor your analysis and defensive measures accordingly.