Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power side-channel attacks aiming at hiding the secret-revealing dependencies from the power footprint of a vulnerable implementation. However, this type of software mitigation often depends on general-purpose compilers, which do not preserve non-functional properties. Moreover, microarchitectural features, such as the memory bus and register reuse, may also leak secret information. These abstractions are not visible at the high-level implementation of the program. Instead, they are decided at compile time. To remedy these problems, security engineers often sacrifice code efficiency by turning off compiler optimization and/or performing local, post-compilation transformations. This paper proposes Secure by Construction Code Generation (SecCG), a constraint-based compiler approach that generates optimized yet protected against power side channels code. SecCG controls the quality of the mitigated program by efficiently searching the best possible low-level implementation according to a processor cost model. In our experiments with twelve masked cryptographic functions up to 100 lines of code on Mips32 and ARM Thumb, SecCG speeds up the generated code from 77% to 6.6 times compared to non-optimized secure code with an overhead of up to 13% compared to non-secure optimized code at the expense of a high compilation cost. For security and compiler researchers, this paper proposes a formal model to generate power side channel free low-level code. For software engineers, SecCG provides a practical approach to optimize performance critical and vulnerable cryptographic implementations that preserve security properties against power side channels.

Embedded devices are increasingly present in our everyday life. They often process critical information, and hence, rely on cryptographic protocols to achieve security. However, embedded devices remain particularly vulnerable to attackers seeking to hijack their operation and extract sensitive information by exploiting side channels and code reuse. Code-Reuse Attack (CRA) can steer the execution of a program to malicious outcomes, altering existing on-board code without direct access to the device memory. Moreover, Side-Channel Attacks (SCAs) may reveal secret information to the attacker based on mere observation of the device. Thwarting Code-Reuse Attacks (CRAs) and Side-Channel Attacks (SCAs) against embedded devices is especially challenging because embedded devices are usually resource constrained. Fine-grained code diversification can hinder CRAs by introducing uncertainty to the binary code; while software mechanisms can thwart timing or power SCAs. The resilience to either attack may come at the price of the overall efficiency. Moreover, a unified approach that preserves these mitigations against both CRAs and SCAs is not available. In this paper, we propose a novel Secure Diversity by Construction (SecDivCon) approach that tackles this challenge. SecDivCon is a combinatorial compiler-based approach that combines software diversification against CRAs with software mitigations against SCAs. SecDivCon restricts the performance overhead introduced by the generated code that thwarts the attacks and hence, offers a secure-by-design approach enabling control over the performance-security trade-off. Our experiments, using 16 benchmark programs, show that SCA-aware diversification is effective against CRAs, while preserving SCA mitigation properties at a low, controllable overhead. Given the combinatorial nature of our approach, SecDivCon is suitable for small, performance-critical functions that are sensitive to SCAs. SecDivCon may be used as a building block to whole-program code diversification or in a re-randomization scheme of cryptographic code.

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks, such as Jump-Oriented Programming (JOP) 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, such as the code size or speed, 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. For larger problems, we propose a combination of LNS with a structural decomposition of the problem. To further improve the diversification efficiency of DivCon against JOP attacks, we propose an application-specific distance measure tailored to the characteristics of JOP attacks. We evaluate DivCon with 20 functions from a popular benchmark suite for embedded systems. These experiments show that DivCon's combination of LNS and our application-specific distance measure generates binary programs that are highly resilient against JOP attacks (they share between 0.15% to 8% of JOP gadgets) with an optimality gap of 10%. Our results confirm that there is a trade-off between the quality of each assembly code version and the diversity of the entire pool of versions. In particular, the experiments show that DivCon is able to generate binary programs that share a very small number of gadgets, while delivering near-optimal code. 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.

MicroRNAs (miRNAs) are small RNA molecules that target particular genes and prohibit their expression. Since many important diseases are related to the expression or non-expression of particular genes, knowing the miRNAs that affect these genes can help in finding possible treatments. In the last decade, a large amount of experimental studies trying to reveal the targets of several miRNAs has been published. A handful of curated databases that collect miRNA targets from the literature have been developed to make this information more easily available. However, due to the large number of existing published articles, maintaining these databases up-to-date is a tedious task that requires important resources. In this work we introduce TarMiner, a pipeline for automatic extraction of miRNA targets that can facilitate the curation process of databases that maintain miRNA validated targets.