Topological analysis of network attack vulnerability

As networks continue to grow in size and complexity, automatic assessment of the security vulnerability becomes increasingly important. The typical means by which an attacker breaks into a network is through a series of exploits, where each exploit in the series satisfies the pre-condition for subsequent exploits and makes a causal relationship among them. Such a series of exploits constitutes an attack path where the set of all possible attack paths form an attack graph. Attack graphs reveal the threat by enumerating all possible sequences of exploits that can be followed to compromise a given critical resource. The contribution of this chapter is to identify the most probable attack path based on the attack surface measures of the individual hosts for a given network and also identify the minimum possible network securing options for a given attack graph in an automated fashion. The identified network securing options are exhaustive and the proposed approach aims at detecting cycles in forward reachable attack graphs. As a whole, the chapter deals with identification of probable attack path and risk mitigation which may facilitate in improving the overall security of an enterprise network.

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 graphs are the graphs which describe attack scenarios, play important roles in analyzing network threats. These attack graphs are able to reveal such potential threats by evaluating the all possible sequences that an attacker can follow to compromise given critical resources or nodes. An Attack graph specifies an attack scenario that results in compromising network values. There are so many methods proposed to evaluate the network security in attack graphs. But no method specifies the overhead occurred due to the evaluation of network security at each and every node. This paper addresses the problem of overhead occurred due to the network security evaluation in Short-Normalizedattack graphs by evaluating a factor called network security risk. In this paper first the possible n valid attack paths are going to be calculated and then the security risk is going to be calculated for those n valid paths. This security risk denotes the amount of overhead occurred due to this evaluation.

In order to discover the network vulnerability timely and solve the very serious problems of network security, this paper puts forward the attack graph which is based on intrusion detection method. The method uses the generation global network attack graph algorithm to build network initial attack graph, and call attack graph optimization algorithm to remove global attack graph unreasonable path, and achieve the goal of simply attack graph. Finally, management personnel get the basis which is computed nodes in each state attack graph algorithms degree of loss to optimize the network security. Experiments show that the intrusion detection method is reasonable and effective, and has the advantages of simple and easy.

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.

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.

An attack graph models the causal relationships between vulnerabilities. Attack graphs have important applications in protecting critical resources in networks against sophisticated multi-step intrusions. Currently, analyses of attack graphs largely depend on proprietary implementations of specialized algorithms. However, developing and implementing algorithms causes a delay to the availability of new analyses. The delay is usually unacceptable due to rapidly-changing needs in defending against network intrusions. An administrator may want to revise an analysis as soon as its outcome is observed. Such an interactive analysis, similar to that in decision support systems, is desirable but difficult with current approaches based on proprietary implementations of algorithms. This paper addresses the above issue through a relational approach. Specifically, we devise a relational model for representing necessary inputs, such as network configurations and domain knowledge, and we generate attack graphs from these inputs as relational views. We show that typical analyses can be supported through different type of searches in an attack graph, and these searches can be realized as relational queries. Our approach eliminates the needs for implementing algorithms, because an analysis is now simply a relational query. The interactive analysis of attack graphs becomes possible, since relational queries can be dynamically constructed and revised at run time. As a side effect, experimental results show that the mature optimization techniques in relational databases can transparently improve the performance of the analysis.

Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets.

The network infrastructure is the most critical technical asset of any organization. This network architecture must be useful, efficient, and secure. However, their cybersecurity challenges are immense as the number of attacks is increasing. Consequently, there is a need to have efficient tools to assess the risks, know the vulnerabilities, and find the solutions before the attackers exploit them. The challenges remain in integrating the vulnerability analysis tools in a holistic process that cyber defenders can use to detect an intrusion and respond quickly. Attack graphs showed great importance in analyzing security. In this paper, we present a survey of raised and related topics to the field of vulnerability analysis and attack graphs.

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.

Today cyber adversaries utilize advanced techniques to victimize target assets. To tackle the adversaries, it is of utmost importance to understand potential techniques they may use to exploit network vulnerabilities. Attack graph has always been a crucial tool for network vulnerability analysis. However, the current state-of-the-art attack graph can not predict adversarial techniques. To overcome the gap, we utilize the MITRE ATT&CK matrix in this work and map the techniques with the attack graph node descriptions. We first formulate a comprehensive dataset from ATT&CK consisting of all the adversarial strategies, subtechniques, associated tactics, and mitigation for the enterprise network. We then capture the attack graph node descriptions and apply the term frequency-inverse document frequency (TF-IDF) algorithm to map the attack techniques with the available node descriptions. Next, we generate the cosine similarity to determine an adversary’s top methods to attack a network. We then map those techniques with the associated tactics and mitigation strategies as enumerated in the ATT&CK matrix. Finally, we illustrate the analysis using a networked system’s attack graph. This proposed method would help identify and validate adversarial techniques and guide in selecting mitigation mechanisms for security enhancement.

Intrusion Detection Systems (IDS) have been used widely to detect malicious behavior in network communication and hosts. IDS management is an important capability for distributed IDS solutions, which makes it possible to integrate and handle different types of sensors or collect and synthesize alerts generated from multiple hosts located in the distributed environment. Sophisticated attacks are difficult to detect and make it necessary to integrate multiple data sources for detection and correlation. Attack graph (AG) is used as an effective method to model, analyze, and evaluate the security of complicated computer systems or networks. The attack graph workflow consists of three parts: information gathering, attack graph construction, and visualization. This paper proposes the integration of the AG workflow with an IDS management system to improve alert and correlation quality. The vulnerability and system information is used to prioritize and tag the incoming IDS alerts. The AG is used during the correlation process to filter and optimize correlation results. A prototype is implemented using automatic vulnerability extraction and AG creation based on unified data models.

To address the scalability problem in attack graphs generation, we propose a novel method to generate attack graphs automatically. Our approach constructs a two- tier attack graph framework, which includes a host access graph and some sub-attack graphs. A sub-attack graph describes concrete attack scenarios from one source host to one target host, while the host access graph describes the attacker’s privilege transition among hosts. Our sub-attack graphs and host access graph have remarkable smaller scales and can help network administrators to find the key hosts in attack sequences. Analysis shows that the upper bound computational cost of our model is O(N3), which could also be competed in real time. The following experiment validates our approach.

In view of the characteristics of large-scale, coordinated and multi-stage network attacks, a network security situation assessment method based on attack Graph model was proposed. Firstly, the network attack behavior characteristics are constructed by integrating the spatio-temporal characteristics of attack events and multi-source alarm data. Secondly, attack nodes are mapped based on alarm information to associate multi-step attack paths. Thirdly, on the basis of constructing the attack Graph, the transfer probability table of attack nodes is constructed combining with the transfer sequence, and the transfer probability is introduced into the attack graph to infer the attack intention of the attacker. Finally, according to the maximum possible attack path, the security situation of attack nodes with high probability is evaluated, and the security situation of potential attack nodes after network attack is scientifically quantified, providing theoretical support and scientific basis for network security management personnel to do a good job of protection in advance.

The network security assessment is vital for improving the overall security posture. With diverse opportunities for using networking devices and configuring them, varying software application portfolios, and the increased flexibility of using numerous applications, today's computer networks are subject to continuous evolution. Such ever-growing computer networks in size and complexity lead to information exposure to an increased threat landscape and attack surface variation. The network attack surface constitutes exploitable technical vulnerabilities, software/hardware misconfigurations (i.e., configuration gaps), vulnerable service connectivities, and service-cum-user privileges. An adversary may exploit the network attack surface to penetrate the enterprise networks incrementally. The discovery of new vulnerabilities and vague access control rules can further set off the attack surface variation. Hence, it is essential to consider the temporal aspect of network security. Attack graph, a graphical network security modeling tool, succinctly captures the attack surface of a vulnerable network in the form of initial security conditions, much needed for an adversary for successful incremental network penetration. Existing attack graph-based metrics are inadequate in capturing the variation in the attack surface. We propose to use a Boolean similarity metric to assess the similarity between the goal-oriented attack graphs generated successively for an enterprise network within the chosen sampling interval. We represent individual attack graphs as a Boolean expression to serve our purpose. A Boolean expression is a sum-of-product expression, i.e., a disjunction of attack paths, each a conjunction of initial conditions. We have conducted a set of experiments to validate the efficacy and applicability of the Boolean similarity metric. The results indicate that the Boolean similarity measure can detect the variation in the network attack surface.