Biblio
Security against hardware trojans is currently becoming an essential ingredient to ensure trust in information systems. A variety of solutions have been introduced to reach this goal, ranging from reactive (i.e., detection-based) to preventive (i.e., trying to make the insertion of a trojan more difficult for the adversary). In this paper, we show how testing (which is a typical detection tool) can be used to state concrete security guarantees for preventive approaches to trojan-resilience. For this purpose, we build on and formalize two important previous works which introduced ``input scrambling" and ``split manufacturing" as countermeasures to hardware trojans. Using these ingredients, we present a generic compiler that can transform any circuit into a trojan-resilient one, for which we can state quantitative security guarantees on the number of correct executions of the circuit thanks to a new tool denoted as ``testing amplification". Compared to previous works, our threat model covers an extended range of hardware trojans while we stick with the goal of minimizing the number of honest elements in our transformed circuits. Since transformed circuits essentially correspond to redundant multiparty computations of the target functionality, they also allow reasonably efficient implementations, which can be further optimized if specialized to certain cryptographic primitives and security goals.
As the frequency, severity, and sophistication of cyber attacks increase, along with our dependence on reliable computing infrastructure, the role of Intrusion Detection Systems (IDS) gaining importance. One of the challenges in deploying an IDS stems from selecting a combination of detectors that are relevant and accurate for the environment where security is being considered. In this work, we propose a new measurement approach to address two key obstacles: the base-rate fallacy, and the unit of analysis problem. Our key contribution is to utilize the notion of a `signal', an indicator of an event that is observable to an IDS, as the measurement target, and apply the multiple instance paradigm (from machine learning) to enable cross-comparable measures regardless of the unit of analysis. To support our approach, we present a detailed case study and provide empirical examples of the effectiveness of both the model and measure by demonstrating the automated construction, optimization, and correlation of signals from different domains of observation (e.g. network based, host based, application based) and using different IDS techniques (signature based, anomaly based).
Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.