A novel approach for analysis of attack graph

Attack graphs model possible paths that a potential attacker can use to intrude into a target network. They can be used in determining both proactive and reactive security measures. Attack graph generation is a process that includes vulnerability information processing, collecting network topology and application information, determining reachability conditions among network hosts, and applying the core graph building algorithm. This article introduces a classification scheme for a systematical study of the methods applied in each phase of the attack graph generation process, including the usage of attack graphs for network security. The related works in the literature are stated based on the proposed classification scheme and contributive ideas about potential challenges and open issues for attack graph generation and usage are provided.

All types of network systems are subject to computer attacks. The overall security of a network cannot be determined by simply considering the vulnerable points in the network; it is essential to realize how vulnerabilities can be combined in the same host or in a set of connected hosts to initiate an attack. Attack graph is a tool for modeling compositions of vulnerabilities and thus representing possible multi-stage multi-host attacks in networks. Attack graphs can be used for measuring network security; supporting security solutions by identifying vulnerabilities that should be removed such that no attack can be realized targeting given critical resources, and thus hardening the network. We consider a general model of attack graphs and a scheme of attack graph generating algorithm; and investigate graph-theoretical problems related to particular tasks of network hardening.

Network administrators must rely on labour-intensive processes for tracking network configurations and vulnerabilities, which requires a lot of expertise and error prone. Organizational network vulnerabilities and interdependencies are so complex to make traditional vulnerability analysis become inadequate. Decision support capabilities let analysts make tradeoffs between security and optimum availability, and indicates how best to apply limited security resources. Recent work in network security has focused on the fact that a combination of exploitation is the typical way in which the invader breaks into the network. Researchers have proposed various algorithms to generate graphs based attack tree (or graph). In this paper, we present a framework, Architecture and Approach to Vulnerability Analysis.

2011

Computer networks are certainly vulnerable as long as they deliver services from different machines. An attack graph is a security model representing the chains of vulnerability exploits in a network displays the ways an attacker can compromise a network or host. A number of researchers have admitted attack graph visual complications and a large amount of source data must be assembled to accurately build an attack graph, the difficulty scaling to large, enterprise-size networks with tens of thousands of hosts and the lack comprehensive understanding. Information on vulnerabilities is present in public vulnerability databases, such as the National Vulnerability Database and Nessus. But current attack graph tools are reserved to only limited attributes. The automatic formation of vulnerability information has been troublesome and vulnerability descriptions were created by hand or based on limited information. Much vulnerability has still not been discovered and many others without pat...

Proceedings 2002 IEEE Symposium on Security and Privacy

An integral part of modeling the global view of network security is constructing attack graphs. In practice, attack graphs are produced manually by Red Teams. Construction by hand, however, 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 [4] algorithms, letting us construct attack graphs automatically and efficiently. We also describe two analyses to help decide which attacks would be most costeffective 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.

— Attack graph can provide clues for the network defender on how an attacker exploits the vulnerability on the network to achieve goals. System administrators use attack graph to determine how vulnerable their systems and to determine what security measures are used to maintain their systems. In a network of large and complex organizations, securing a network is a very challenging task. Attack graphs are very important in the effort to secure the network, because it can directly indicate the presence of vulnerabilities in network and how attackers use the vulnerabilities to implement an effective attack. In this paper, we will describe some very good algorithms can be used to generate the attack graph. Keywords— Generating Attack Graph; Network Security; Vulnerability Analysis; Attack Graph Generation Algorithm. I. INTRODUCTION Since the number of host in network continues to grow, it becomes increasingly more important to automate the process of evaluating their vulnerability to attack. When evaluating network security, rarely enough to consider the vulnerability of isolated existence. Large networks typically contain [1] multiple platforms and software packages, and employ multiple modes of connectivity. It is undeniable that the network has a security hole that could be spared from the observation, even by experienced administrators. The rapid growth of the internet affects the economic, political, cultural and many aspects of society. The deeper and wild internet applications, the clearer and more complex computer and network security issues are. Hackers and viruses can find more ways [2] to launch an attack with respect to the development of network technology. When analysing the network security of a company, it is important [3] to consider the multi-stage, multi-host attacks. A determined attacker is not possible to stop the machine he first compromise, but can be expected to try to penetrate more deeply into the network by jumping from one computer to another. For this reason, configuring a secure enterprise network is a daunting task for humans. There are many potential interactions between multiple hosts and components in the network, so that the configuration of the machine will affect the security of others in the network. Currently [5], in the field of network vulnerability analysis, there was an effective scanner to scan a single host or multiple vulnerabilities in the observed network. However, these tools only check the security hole from the isolated perspective. In order to objectively analyse network vulnerabilities, an analysis tool should be able to automatically make a systematic attack scenarios based on the vulnerability of the target network, service network, host connection and access authorization. Since the attack graph is suitable for simulating attack scenarios, network vulnerability analysis and the establishment of a defense mechanism, more and more attention is paid to them. As an important aspect of network security, computer security evaluation through the analysis of the computer network is very important and can protect us from being attacked. Attack graphs can provide a view of the security evaluation. In [6] describes a method for generating attack graphs for network security analysis. As computer networks continue to grow in size and complexity, assessing their susceptibility to attack is a real challenge. The combination of exploits is the typical means whereby an attacker breaks into a network. Attack graphs can describe the transition from the initial state to the target state, caused by the action of the attacker, and support the visual analysis [7]. The organization of the rest of this article is as follows. Section 2 introduces the attack graph generation. The algorithms for generating attack graph are given in Section 3. We give a conclusion in Section 4.

Network administrators must rely on labour-intensive processes for tracking network configurations and vulnerabilities, which needs a lot of expertise and error prone. Network vulnerabilities and interdependencies are so complex to make traditional vulnerability analysis become inadequate. Capability of decision support let analysts make tradeoffs between security and optimum availability, and explains how best to apply limited security resources. Recent works in network security has focused on the fact that a combination of exploitation is the typical way in which the invader breaks the network security. Researchers have proposed various algorithms to generate graphs based attack tree (or graph). In this paper, a framework, architecture and approach to Vulnerability Analysis are presented.

International Journal on Electrical Engineering and Informatics, 2015

This paper presents some improvements on the Multihost Multistage Vulnerability Analysis (MulVAL) framework. MulVAL is a framework to generate an attack graph for analysis of a computer network security risk. In MulVAL, it is assumed that the probability of success in exploiting the machine configuration and vulnerability variable is 100%, and the vulnerability variables are independent of each other. In reality each machine has its own security configuration issue, and vulnerabilities are not independent of each other. Moreover the research in MulVAL attack graph solely focuses on the probability of vulnerability, whereas the probability of vulnerability in security configuration is not covered. In this paper we introduce three methods to improve the MulVAL framework. In the first method we employ Common Vulnerability Scoring System (CVSS) for calculating the probability of vulnerability variables, and Common Configuration Scoring System (CCSS) for calculating the probability of vulnerability of system security configuration. In the second method we introduce the interdependence of vulnerability variables in Bayesian senses. Finally, in the third method we analyze the impact of the change in system security configuration to the probability of vulnerability in the context of Bayesian probability. We analyze and discuss the proposed methods through a simulation for a simple network configuration. The simulation results of the proposed methods demonstrate that the introduction of CVSS, CCSS, employing dependency of vulnerability variables and system security configuration represent a more realistic model for vulnerability in MulVAL framework.

2008

We propose in this paper a novel approach to analyze network vulnerability and to obtain a quantitative value representing the level of security achieved in an arbitrary network. Unlike previous graph-based algorithms that generate attack trees (or graphs) to cover all possible sequences of vulnerabilities and therefore are not scalable, our method utilizes the attack graphpsilas principles, but directly analyzes and produces the desired security measure for a network without building the actual attack graph. The proposed approach relies on a unique evaluation of vulnerability metric defined in this paper and is demonstrated through an example of a network that provides voice over IP services.

2017

Attack graphs offer a powerful framework for security risk assessment. They provide a compact representation of the attack paths that an attacker can follow to compromise network resources from the analysis of the network topology and vulnerabilities. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic security risk assessment. Thus, whilst static analysis of attack graphs considers the security posture at rest, dynamic analysis accounts for evidence of compromise at run-time, helping system administrators to react against potential threats. In this paper, we introduce a Bayesian attack graph model that allows to estimate the probabilities of an attacker compromising different resources of the network. We show how exact and approximate inference techniques can be efficiently applied on Bayesian attack graph models with thousands of nodes.