You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
As penetration testing frameworks have evolved and have become more complex, the problem of controlling automatically the pentesting tool has become an important question. This can be naturally addressed as an attack planning problem. Previous approaches to this problem were based on modeling the actions and assets in the PDDL language, and using off-the-shelf AI tools to generate attack plans. These approaches however are limited. In particular, the planning is classical (the actions are deterministic) and thus not able to handle the uncertainty involved in this form of attack planning. We herein contribute a planning model that does capture the uncertainty about the results of the actions, which is modeled as a probability of success of each action. We present efficient planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These algorithms take into account the probability of success of the actions and their expected cost (for example in terms of execution time, or network traffic generated). We thus show that probabilistic attack planning can be solved efficiently for the scenarios that arise when assessing the security of large networks. Two "primitives" are presented, which are used as building blocks in a framework separating the overall problem into two levels of abstraction. We also present the experimental results obtained with our implementation, and conclude with some ideas for further work.

Cited Abstract(s):
[1]: An integral part of modeling the global view of network security is constructing attack graphs. Manual attack graph construction is tedious, error-prone, and impractical for attack graphs larger than a hundred nodes. In this paper we present an automated technique for generating and analyzing attack graphs. We base our technique on symbolic model checking algorithms, letting us construct attack graphs automatically and efficiently. We also describe two analyses to help decide which attacks would be most cost-effective to guard against. We implemented our technique in a tool suite and tested it on a small network example, which includes models of a firewall and an intrusion detection system.

[2]: Currently, network administrators must rely on labor-intensive processes for tracking network configurations and vulnerabilities, which requires a great deal of expertise and is error prone. The organization of networks and the inter dependencies of vulnerabilities are so complex as to make traditional vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we discover attack paths (organized as graphs) that convey the impact of individual and combined vulnerabilities on overall security. We provide sophisticated attack graph visualizations, with high-level overviews and detail drill down. Decision support capabilities let analysts make optimal tradeoffs between safety and availability, and show how to best apply limited security resources. We employ efficient algorithms that scale well to larger networks.

[3]: We apply adjacency matrix clustering to network attack graphs for attack correlation, prediction, and hypothesizing. We self-multiply the clustered adjacency matrices to show attacker reachability across the network for a given number of attack steps, culminating in transitive closure for attack prediction over all possible number of steps. This reachability analysis provides a concise summary of the impact of network configuration changes on the attack graph. Using our framework, we also place intrusion alarms in the context of vulnerability-based attack graphs, so that false alarms become apparent and missed detections can be inferred. We introduce a graphical technique that shows multiple-step attacks by matching rows and columns of the clustered adjacency matrix. This allows attack impact responses to be identified and prioritized according to the number of attack steps to victim machines, and allows attack origins to be determined. Our techniques have quadratic complexity in the size of the attack graph

[4]: Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graph-based algorithms to generate attack trees (or graphs). Either structure represents all possible sequences of exploits, where any given exploit can take advantage of the penetration achieved by prior exploits in its chain, and the final exploit in the chain achieves the attacker's goal. The most recent approach in this line of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems, and there is good reason to doubt whether a model checker can handle directly a realistic set of exploits for even a modest-sized network. In this paper, we revisit the idea of attack graphs themselves, and argue that they represent more information explicitly than is necessary for the analyst. Instead, we propose a more compact and scalable representation. Although we show that it is possible to produce attack trees from our representation, we argue that more useful information can be produced, for larger networks, while bypassing the attack tree step. Our approach relies on an explicit assumption of monotonicity, which, in essence, states that the precondition of a given exploit is never invalidated by the successful application of another exploit. In other words, the attacker never needs to backtrack. The assumption reduces the complexity of the analysis problem from exponential to polynomial, thereby bringing even very large networks within reach of analysis

[5]: 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.
Early work on attack graph solving relied on model checking techniques [3]9 [1] , with their inherent scalability restrictions; or on monotonicity assumptions [4] [2] [3] that are not able to express situations in which compromised resources are lost due to crashes, detection or other unforeseen circumstances.