Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system's components given their vulnerabilities and interconnections, and accounts for multi-step attacks spreading through the system. Whilst static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, e.g. from SIEM software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this paper we show how Loopy Belief Propagation - an approximate inference technique - can be applied to attack graphs, and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm's accuracy is acceptable and converge to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages of approximate inference techniques to scale to larger attack graphs.
AG representations are built by analysing the interdependencies between the vulnerabilities and the security conditions identified in a network. Two representations are commonly encountered in the literature: representations @cite_24 @cite_39 @cite_10 @cite_44 @cite_41 depict the whole state of the network in each node in the graph whilst AGs @cite_43 @cite_2 @cite_1 are bipartite graphs representing the dependencies between vulnerabilities and security conditions. Although state-based AGs contain all the possible attack paths that can allow an attacker to reach a target security condition, they scale exponentially with the number of vulnerabilities and nodes in the network, which limits their application to very small networks. Relying on the monotonicity principle, logical AGs eliminate duplicate paths and provide a more compact representation that scales polynomially with the number of vulnerabilities @cite_43 @cite_2 .
@cite_41 @cite_1 @cite_39 @cite_24 @cite_44 @cite_43 @cite_2 @cite_10
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Security models, such as an attack graph (AG), are widely adopted to assess the security of networked systems, such as utilizing various security metrics and providing a cost-effective network hardening solution. There are various methods of generating these models, but the scalability problem exists for single-layered graph-based security models when analyzing all possible attack paths. To address this problem, we propose to use a multi-layer hierarchical attack representation model (HARM) that models various components in the networked system in different layers to reduce the computational complexity. First, we formulate key questions that need to be answered to assess the scalability of security models. Second, we formally define the multi-layer HARM. Last, we conduct experiments to show the scalability of security models. Our experimental results show that using the HARM can improve the scalability of assessing the security of the networked system significantly in comparison to the single-layered security models in various network scenarios. Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection. This paper presents a novel approach for predicting contours which advances the state of the art in two fundamental aspects, i.e. multi-scale feature generation and fusion. Different from previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, we introduce a hierarchical deep model which produces more rich and complementary representations. Furthermore, to refine and robustly fuse the representations learned at different scales, the novel Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The experiments ran on two publicly available datasets (BSDS500 and NYUDv2) demonstrate the effectiveness of the latent AG-CRF model and of the overall hierarchical framework. 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 This paper presents a tool for assessment of security attributes and vulnerabilities in computer networks. The tool generates attack graphs (Phillips and Swiler, 1998). Each node in the attack graph represents a possible attack state. Edges represent a change of state caused by a single action taken by the attacker or unwitting assistant, and are weighted by some metric (such as attacker effort or time to succeed). Generation of the attack graph requires algorithms that match information about attack requirements (specified in attack templates) to information about the network configuration and assumed attacker capabilities (attacker profile). The set of near-optimal shortest paths indicates the most exploitable components of the system configuration. This paper presents the status of the tool and discusses implementation issues, especially focusing on the data input needs and methods for eliminating redundant paths and nodes in the graph.