Implementing interactive analysis of attack graphs using relational databases

Attack Graphs have been widely used by the network security administrators to gain an understanding of possible attack paths, an attacker may follow to compromise critical resources. As networks get larger and more complex, one needs to use databases to perform iterative, interactive analysis tasks with attack graphs. In this paper we investigate how property graph based graph databases like Neo4j can be effectively used towards this application. We show that extending the standard property graph model with a constraint specification mechanism aids attack graph modeling and interactive analytics. We briefly sketch a preliminary attempt to implement a constraint layer over Neo4j and show how attack graph generation and analysis can be performed faithfully in Neo4j using the extended model.

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 MulVAL 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.

As computer networks continue to grow, it becomes increasingly more important to automate the process of evaluating their vulnerability to attacks. Despite the best efforts of software architects and developers, network hosts inevitably contain a number of vulnerabilities. Hence, it is not feasible for a network administrator to remove all vulnerabilities present in the network hosts. Therefore, the recent focus in security of such networks is on analysis of vulnerabilities globally, finding exploits that are more critical, and preventing them to thwart an intruder. When evaluating the security of a network, it is rarely enough to consider the presence or absence of isolated vulnerabilities. This is because intruders often combine exploits against multiple vulnerabilities in order to reach their goals (Abadi & Jalili, 2005). For example, an intruder might exploit the vulnerability of a particular version of FTP to overwrite the .rhosts file on a victim host. In the next step, the intruder could remotely log in to the victim. In a subsequent step, the intruder could use the victim host as a base to launch another exploit on a new victim, and so on. (Phillips & Swiler, 1998) proposed the concept of attack graphs, where each node represents a possible attack state. Edges represent a change of state caused by a single action taken by the intruder. (Sheyner et al., 2002) used a modified version of the model checker NuSMV (NuSMV, 2010) to produce attack graphs. (Ammann et al., 2002) introduced a monotonicity assumption and used it to develop a polynomial algorithm to encode all of the edges in an attack graph without actually computing the graph itself. These attack graphs are essentially similar to (Phillips & Swiler, 1998), where any path in the graph from an initial node to a goal node shows a sequence of exploits that an intruder can launch to reach his goal. (Noel et al., 2005) presented a number of techniques for managing attack graph complexity through visualization. (Mehta et al., 2006) presented a ranking scheme for the nodes of an attack graph. Rank of a node shows its importance based on factors like the probability of an intruder reaching that node. Given a ranked attack graph, the system administrator can concentrate on relevant subgraphs to figure out how to start deploying security measures. (Ou et al., 2006) presented logical attack graphs, which directly illustrate logical dependencies among attack goals and configuration information. Their attack graph generation tool builds upon MulVAL (Ou et al., 2005), a network security analyzer based on logical programming. The aim of minimization analysis of network attack graphs is to find a minimum critical set of exploits that completely disconnect the initial nodes and the goal nodes of the graph.

Attack graph is an essential tool for the security assessment of industrial Internet systems. Unfortunately, the traditional attack graph mainly focuses on discrete information and cannot handle continuous information such as temperature and humidity. These continuous data represent the states of the industrial Internet and are also indicators of attacks, which should be included in the attack graph. To this end, this paper presents a hybrid attack graph model, which can simultaneously describe the discrete and continuous information of the system. Based on the hybrid attack graph model, a security analysis method is presented. Firstly, the transformation rule from hybrid attack graph to timed automata is established. Secondly, the security attribute of the system is described by sequential logic of continuous interval, and then the model checker TACK is used to complete the analysis of the system. Finally, an example is analyzed and verified by a smart home system to illustrate the method’s effectiveness. The method in this paper provides a new idea for attack graph construction and analysis for industrial Internet systems.

: Report developed under SBIR contract for Topic No. N03-T010. In an ideal world, system and network administrators would keep all of their systems fully patched all the time. Unfortunately, for a variety of reasons few sites have the luxury of such an approach. Given that administrators cannot apply all possible countermeasures (e.g., patches), our next best strategy is to identify an ordering of countermeasures that will provide the optimal level of security for a given amount of changes to the network. One of the primary approaches to identify this optimized ordering is through attack graph analysis, and this approach has received considerable attention recently. Unfortunately, there are a number of critical problems with this strategy. This paper presents an approach that takes advantage of simple filtering capabilities in commodity routers that partially addresses the limitation of the attack graph strategy. Furthermore, in at least one problem, dealing with unknown vulnerabilities, the simple strategy presented in this paper can produce better results than the attack graph approach.

An attack graph is a succinct representation of all paths through a system that end in a state where an intruder has successfully achieved his goal. Today Red Teams determine the vulnerability of networked systems by drawing gigantic attack graphs by hand. Constructing attack graphs by hand is tedious, error-prone, and impractical for large systems. By viewing an attack as a violation of a safety property, we can use off-the-shelf model checking technology to produce attack graphs automatically: a successful path from the intruder's viewpoint is a counterexample produced by the model checker In this paper we present an algorithm for generating attack graphs using model checking as a subroutine. Security analysts use attack graphs for detection, defense and forensics. In this paper we present a minimization analysis technique that allows analysts to decide which minimal set of security measures would guarantee the safety of the system. We provide a formal characterization of this problem: we prove that it is polynomially equivalent to the minimum hitting set problem and we present a greedy algorithm with provable bounds. We also present a reliability analysis technique that allows analysts to perform a simple cost-benefit trade-off depending on the likelihoods of attacks. By interpreting attack graphs as Markov Decision Processes we can use the value iteration algorithm to compute the probabilities of intruder success for each attack the graph.

Smart infrastructure integrates cyberphysical systems, self-organizing networks of devices, fog computing to enhance the quality and convenience of manufacturing and social life, providing efficient resource management and increasing productivity and availability of traditional services. New technologies are characterized with a high degree of heterogeneity, structural complexity, and dynamic topology that result in a large number of new information and cyber security threats. Given a high degree of integration of digital cyber space into the processes of management, monitoring, aggregation, and data transferring, an assessment of its security level is a necessary requirement to maintain and control a safe and reliable environment for handling data and performing physical processes. Risk analysis and assessment is an extremely important task at all stages of the life cycle of subsystems and components of the smart environment. It allows us to assess a level of protection, identify vulnerable assets, and counteract the most likely directions of security threats. The known methods for security risk assessment do not consider the features of modern smart technologies, and this forces us to search for new approaches. This paper proposes a comprehensive method that includes a calculation of security indicators, risk assessment and selection of protective measures, based on attack graphs and thus allows us to consider the dynamics of changes in the components of the smart infrastructure. The automated system for assessing the security risks in the smart infrastructure and choosing the protective measures has been implemented.

Attack graph plays an important role in network security, as it directly shows the existence of vulnerabilities in network and how attackers use these vulnerabilities to implement an effective attack, the analysis on the attack graph or the simulation of dynamic attacks through attack graph can help us easily find out the vulnerabilities in network, and take corresponding security measures, in order to strengthen network security. Previous attack graph generation methods are generally not suitable for large network, because of their high complexity of time, high consumption of space, and the large scale of attack graphs. Based on substantive analysis of the vulnerabilities in network, this paper describes a model for automatically generating and analyzing network attack graph. Besides, a prototype system bases on this model has been designed. At last, this prototype system was tested by a model network we built, and it was proved to be simple, flexible, and efficient.

Attack graph analysis has been established as a powerful tool for analyzing network vulnerability. However, previous approaches to network hardening look for exact solutions and thus do not scale. Further, hardening elements have been treated independently, which is inappropriate for real environments. For example, the cost for patching many systems may be nearly the same as for patching a single one. Or patching a vulnerability may have the same effect as blocking traffic with a firewall, while blocking a port may deny legitimate service. By failing to account for such hardening interdependencies, the resulting recommendations can be unrealistic and far from optimal. Instead, we formalize the notion of hardening strategy in terms of allowable actions, and define a cost model that takes into account the impact of interdependent hardening actions. We also introduce a near-optimal approximation algorithm that scales linearly with the size of the graphs, which we validate experimentally.

Attack graph is a general paradigm to model the weakness of an information system network and all possible attack sequences that attackers can obtain specific targets. In real systems, a vast majority of attack graph generation methods suffer from the states explosion issue. However, if we can predict which attack actions will own the maximum probability to be exploited by intruders precisely, namely finding the optimal attack path, we can solve this problem. In this paper, we propose an attack graph generation algorithm based on supervised Kohonen neural network. Using this method, we can presage the attack success rate and attack status types which would be attained if attackers successfully exploit vulnerabilities. Based on these results and the network topology, a probabilistic matrix and an optimal atomic attack matrix are proposed by us. Finally, the two matrices can be effectively used to generate the optimal attack path. After modeling the optimal path, the core nodes in the target network can be located, and network administrators can enact a series of effective defense strategies according to them.

Attack graphs enable computation of important network security metrics by revealing potential attack paths an adversary could use to gain control of network assets. This paper presents GARNET (Graphical Attack graph and Reachability Network Evaluation Tool), an interactive visualization tool that facilitates attack graph analysis. It provides a simplified view of critical steps that can be taken by an attacker and of host-to-host network reachability that enables these exploits. It allows users to perform what-if experiments including adding new zero-day attacks, following recommendations to patch software vulnerabilities, and changing the attacker starting location to analyze external and internal attackers. Users can also compute and view metrics of assets captured versus attacker effort to compare the security of complex networks. For adversaries with three skill levels, it is possible to create graphs of assets captured versus attacker steps and the number of unique exploits required. GARNET is implemented as a Java application and is built on top of an existing C++ engine that performs reachability and attack graph computations. An initial round of user evaluations described in this paper led to many changes that significantly enhance usability.

All current vulnerability assessment tools only can locate individual vulnerabilities on a single host without considering correlated effect of these vulnerabilities. Aiming at this issue, this paper proposes a method of generating attack graph based on privilege escalation. The vulnerabilities and known attacks with their prerequisites and consequences are modeled based on predicate logic theory and are correlated so as to automatically construct attack graphs with strong operation power of RDBMS. The testing result shows that this system can discover security problems undetectable if only the hosts are assessed individually in a network without simulating attacks. As an application based on relational database, it can be easily integrated with other security tools based on RDBMS.

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

The increasing complexity of cloud-native applications has necessitated advanced methodologies for threat modeling and security analysis. This paper presents ThreatCompute, a novel framework that combines LLMs with attack graphs to automate the generation of threat hypotheses and the quantification of risk in Kubernetes environments. While traditional approaches to attack graph generation require significant manual effort from security experts, ThreatCompute leverages LLMs to extract security insights from system information, reducing reliance on manual intervention while maintaining high accuracy and generating context-specific, system-aware threat insights. The framework utilizes the MITRE ATT&CK Matrix and the Microsoft Threat Matrix for Kubernetes as structured domains of possible attack techniques. Based on LLM-generated threat hypotheses and a quantitative risk metric, ThreatCompute constructs detailed attack graphs that illustrate potential attack paths and assess their associated risks. This enables both qualitative and quantitative evaluations of application security across varying levels of granularity. Through real-world examples of Kubernetes applications, we demonstrate the effectiveness of our approach in identifying and quantifying security risks.

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this article, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components.