Biblio

In this work, we propose a design flow for automatic generation of hardware sandboxes purposed for IP security in trusted system-on-chips (SoCs). Our tool CAPSL, the Component Authentication Process for Sandboxed Layouts, is capable of detecting trojan activation and nullifying possible damage to a system at run-time, avoiding complex pre-fabrication and pre-deployment testing for trojans. Our approach captures the behavioral properties of non-trusted IPs, typically from a third-party or components off the shelf (COTS), with the formalism of interface automata and the Property Specification Language's sequential extended regular expressions (SERE). Using the concept of hardware sandboxing, we translate the property specifications to checker automata and partition an untrusted sector of the system, with included virtualized resources and controllers, to isolate sandbox-system interactions upon deviation from the behavioral checkers. Our design flow is verified with benchmarks from Trust-Hub.org, which show 100% trojan detection with reduced checker overhead compared to other run-time verification techniques.

Quantitative risk assessment is a critical first step in risk management and assured design of networked computer systems. It is challenging to evaluate the marginal probabilities of target states/conditions when using a probabilistic attack graph to represent all possible attack paths and the probabilistic cause-consequence relations among nodes. The brute force approach has the exponential complexity and the belief propagation method gives approximation when the corresponding factor graph has cycles. To improve the approximation accuracy, a region-based method is adopted, which clusters some highly dependent nodes into regions and messages are passed among regions. Experiments are conducted to compare the performance of the different methods.

The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.

The initiative to protect against future cyber crimes requires a collaborative effort from all types of agencies spanning industry, academia, federal institutions, and military agencies. Therefore, a Cybersecurity Information Exchange (CYBEX) framework is required to facilitate breach/patch related information sharing among the participants (firms) to combat cyber attacks. In this paper, we formulate a non-cooperative cybersecurity information sharing game that can guide: (i) the firms (players)1 to independently decide whether to “participate in CYBEX and share” or not; (ii) the CYBEX framework to utilize the participation cost dynamically as incentive (to attract firms toward self-enforced sharing) and as a charge (to increase revenue). We analyze the game from an evolutionary game-theoretic strategy and determine the conditions under which the players' self-enforced evolutionary stability can be achieved. We present a distributed learning heuristic to attain the evolutionary stable strategy (ESS) under various conditions. We also show how CYBEX can wisely vary its pricing for participation to increase sharing as well as its own revenue, eventually evolving toward a win-win situation.