Constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality of the generated code variants. This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. Experiments using two popular compiler benchmark suites confirm that there is a trade-off between quality of each assembly code version and diversity of the entire pool of versions. Our results show that DivCon allows users to trade between these two properties by generating diverse assembly code for a range of quality bounds. In particular, the experiments show that DivCon is able to mitigate code-reuse attacks effectively while delivering near-optimal code (< 10% optimality gap). For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications.Comment: 15 pages, 26th International Conference on Principles and Practice of Constraint Programmin

Automated risk analysis for IOT systems

Designing and assessing the security of IoT systems is very challenging, mainly due to the fact that new threats and vulnerabilities affecting IoT devices are continually discovered and published. Moreover, new (typically low-cost) devices are continuously plugged-in into IoT systems, thus introducing unpredictable security issues. This paper proposes a methodology aimed at automating the threat modeling and risk analysis processes for an IoT system. Such methodology enables to identify existing threats and related countermeasures and relies upon an open catalogue, built in the context of EU projects, for gathering information about threats and vulnerabilities of the IoT system under analysis. In order to validate the proposed methodology, we applied it to a real case study, based on a commercial smart home application