FORTES: Forensic Information Flow Analysis of Business Processes

Nearly 70% of all business processes in use today rely on automated workflow systems for their execution. Despite the growing expenses in the design of advanced tools for secure and compliant deployment of workflows, an exponential growth of dependability incidents persists. Concepts beyond access control focusing on information flow control offer new paradigms to design security mechanisms for reliable and secure IT-based workflows. This talk presents FORTES, an approach for the forensic analysis of information flow properties. FORTES claims that information flow control can be made usable as a core of an audit-control system. For this purpose, it reconstructs workflow models from secure log files (i.e. execution traces) and, applying security policies, analyzes the information flows to distinguish security relevant from security irrelevant information flows. FORTES thus cannot prevent security policy violations, but by detecting them with well-founded analysis, improve the precision of audit controls and the generated certificates

Breaking into BIM: Performing static and dynamic security analysis with the aid of BIM

The design and construction industry is moving towards Building Information Models (BIM) that provide all of the strengths of traditional 3D CAD with an added layer of data allowing new and powerful applications. We investigate the concept of using the data within BIM to better explore security design and considerations. We achieve this by first graphing the physical entities of BIM to capture their relational representation as nodes and links. This graph representation will facilitate the use of graph theory or agent-based simulation to assist in the analysis of the static and dynamic behaviour of the environment around the BIM. We also demonstrate an application of graphing by investigating the use of BIM to explore automated infrastructure security design and consideration via red-teaming. The intent is to make security analysis easier and a process that can be carried out during the design phase of a project, even by non-expert users

Model-Based Security Testing

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.Comment: In Proceedings MBT 2012, arXiv:1202.582

VERICA - Verification of Combined Attacks

Physical attacks, including passive Side-Channel Analysis and active Fault Injection Analysis, are considered among the most powerful threats against physical cryptographic implementations. These attacks are well known and research provides many specialized countermeasures to protect cryptographic implementations against them. Still, only a limited number of combined countermeasures, i.e., countermeasures that protect implementations against multiple attacks simultaneously, were proposed in the past. Due to increasing complexity and reciprocal effects, design of efficient and reliable combined countermeasures requires longstanding expertise in hardware design and security. With the help of formal security specifications and adversary models, automated verification can streamline development cycles, increase quality, and facilitate development of robust cryptographic implementations. In this work, we revise and refine formal security notions for combined protection mechanisms and specifically embed them in the context of hardware implementations. Based on this, we present the first automated verification framework that can verify physical security properties of hardware circuits with respect to combined physical attacks. To this end, we conduct several case studies to demonstrate the capabilities and advantages of our framework, analyzing secure building blocks (gadgets), S-boxes build from Toffoli gates, and the ParTI scheme. For the first time, we reveal security flaws in analyzed structures due to reciprocal effects, highlighting the importance of continuously integrating security verification into modern design and development cycles

User-friendly Formal Methods for Security-aware Applications and Protocols

Formal support in the design and implementation of security-aware applications increases the assurance in the final artifact. Formal methods techniques work by setting a model that unambiguously defines attacker capabilities, protocol parties behavior, and expected security properties. Rigorous reasoning can be done on the model about the interaction of the external attacker with the protocol parties, assessing whether the security properties hold or not. Unfortunately, formal verification requires a high level of expertise to be used properly and, in complex systems, the model analysis requires an amount of resources (memory and time) that are not available with current technologies. The aim of this thesis is to propose new interfaces and methodologies that facilitate the usage of formal verification techniques applied to security-aware protocols and distributed applications. In particular, this thesis presents: (i) Spi2JavaGUI, a framework for the model-driven development of security protocols, that combines (for the first time in literature) an intuitive user interface, automated formal verification and code generation; (ii) a new methodology that enables the model-driven development and the automated formal analysis of distributed applications, which requires less resources and formal verification knowledge to complete the verification process, when compared to previous approaches; (iii) the formal verification of handover procedures defined by the Long Term Evolution (LTE) standard for mobile communication networks, including the results and all the translation rules from specification documents to formal models, that facilitates the application of formal verification to other parts of the standard in the future

Automating Cyber Analytics

Model based security metrics are a growing area of cyber security research concerned with measuring the risk exposure of an information system. These metrics are typically studied in isolation, with the formulation of the test itself being the primary finding in publications. As a result, there is a flood of metric specifications available in the literature but a corresponding dearth of analyses verifying results for a given metric calculation under different conditions or comparing the efficacy of one measurement technique over another. The motivation of this thesis is to create a systematic methodology for model based security metric development, analysis, integration, and validation. In doing so we hope to fill a critical gap in the way we view and improve a system’s security. In order to understand the security posture of a system before it is rolled out and as it evolves, we present in this dissertation an end to end solution for the automated measurement of security metrics needed to identify risk early and accurately. To our knowledge this is a novel capability in design time security analysis which provides the foundation for ongoing research into predictive cyber security analytics. Modern development environments contain a wealth of information in infrastructure-as-code repositories, continuous build systems, and container descriptions that could inform security models, but risk evaluation based on these sources is ad-hoc at best, and often simply left until deployment. Our goal in this work is to lay the groundwork for security measurement to be a practical part of the system design, development, and integration lifecycle. In this thesis we provide a framework for the systematic validation of the existing security metrics body of knowledge. In doing so we endeavour not only to survey the current state of the art, but to create a common platform for future research in the area to be conducted. We then demonstrate the utility of our framework through the evaluation of leading security metrics against a reference set of system models we have created. We investigate how to calibrate security metrics for different use cases and establish a new methodology for security metric benchmarking. We further explore the research avenues unlocked by automation through our concept of an API driven S-MaaS (Security Metrics-as-a-Service) offering. We review our design considerations in packaging security metrics for programmatic access, and discuss how various client access-patterns are anticipated in our implementation strategy. Using existing metric processing pipelines as reference, we show how the simple, modular interfaces in S-MaaS support dynamic composition and orchestration. Next we review aspects of our framework which can benefit from optimization and further automation through machine learning. First we create a dataset of network models labeled with the corresponding security metrics. By training classifiers to predict security values based only on network inputs, we can avoid the computationally expensive attack graph generation steps. We use our findings from this simple experiment to motivate our current lines of research into supervised and unsupervised techniques such as network embeddings, interaction rule synthesis, and reinforcement learning environments. Finally, we examine the results of our case studies. We summarize our security analysis of a large scale network migration, and list the friction points along the way which are remediated by this work. We relate how our research for a large-scale performance benchmarking project has influenced our vision for the future of security metrics collection and analysis through dev-ops automation. We then describe how we applied our framework to measure the incremental security impact of running a distributed stream processing system inside a hardware trusted execution environment

Towards the Model-Driven Engineering of Secure yet Safe Embedded Systems

We introduce SysML-Sec, a SysML-based Model-Driven Engineering environment aimed at fostering the collaboration between system designers and security experts at all methodological stages of the development of an embedded system. A central issue in the design of an embedded system is the definition of the hardware/software partitioning of the architecture of the system, which should take place as early as possible. SysML-Sec aims to extend the relevance of this analysis through the integration of security requirements and threats. In particular, we propose an agile methodology whose aim is to assess early on the impact of the security requirements and of the security mechanisms designed to satisfy them over the safety of the system. Security concerns are captured in a component-centric manner through existing SysML diagrams with only minimal extensions. After the requirements captured are derived into security and cryptographic mechanisms, security properties can be formally verified over this design. To perform the latter, model transformation techniques are implemented in the SysML-Sec toolchain in order to derive a ProVerif specification from the SysML models. An automotive firmware flashing procedure serves as a guiding example throughout our presentation.Comment: In Proceedings GraMSec 2014, arXiv:1404.163

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols