In network management, when it comes to security breaches, human error constitutes a dominant factor. We present our tool topoS which automatically synthesizes low-level network configurations from high-level security goals. The automation and a feedback loop help to prevent human errors. Except for a last serialization step, topoS is formally verified with Isabelle HOL, which prevents implementation errors. In a case study, we demonstrate topoS by example. For the first time, the complete transition from high-level security goals to both firewall and SDN configurations is presented.
Most analysis tools make simplifying assumptions about the underlying network boxes. Diekmann al @cite_18 present simplification of iptables firewalls to make complex real-world firewalls available for tools with simplifying assumptions.
@cite_18
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The security provided by a firewall for a computer network almost completely depends on the rules it enforces. For over a decade, it has been a well-known and unsolved problem that the quality of many firewall rule sets is insufficient. Therefore, there are many tools to analyze them. However, we found that none of the available tools could handle typical, real-world iptables rulesets. This is due to the complex chain model used by iptables, but also to the vast amount of possible match conditions that occur in real-world firewalls, many of which are not understood by academic and open source tools. The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework. 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. Great progress has been made recently in verifying the correctness of router forwarding tables [17, 19, 20, 26]. However, these approaches do not work for networks containing middleboxes such as caches and firewalls whose forwarding behavior depends on previously observed traffic. We explore how to verify isolation properties in networks that include such “dynamic datapath” elements using model checking. Our work leverages recent advances in SMT solvers, and the main challenge lies in scaling the approach to handle large and complicated networks. While the straightforward application of model checking to this problem can only handle very small networks (if at all), our approach can verify simple realistic invariants on networks containing 30,000 middleboxes in a few minutes.