University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Population & European union. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Triathlon of Lightweight Block Ciphers for the Internet of Things

Abstract: In this paper, we introduce a framework for the benchmarking of lightweight block ciphers on a multitude of embedded platforms Our framework is able to evaluate the execution time, RAM footprint, as well as binary code size, and allows one to define a custom “figure of merit” according to which all evaluated candidates can be ranked We used the framework to benchmark implementations of 19 lightweight ciphers, namely AES, Chaskey, Fantomas, HIGHT, LBlock, LEA, LED, Piccolo, PRESENT, PRIDE, PRINCE, RC5, RECTANGLE, RoadRunneR, Robin, Simon, SPARX, Speck, and TWINE, on three microcontroller platforms: 8-bit AVR, 16-bit MSP430, and 32-bit ARM Our results bring some new insights into the question of how well these lightweight ciphers are suited to secure the Internet of things The benchmarking framework provides cipher designers with an easy-to-use tool to compare new algorithms with the state of the art and allows standardization organizations to conduct a fair and consistent evaluation of a large number of candidates

Coverage-based regression test case selection, minimization and prioritization: a case study on an industrial system

Abstract: This paper presents a case study of coverage-based regression testing techniques on a real world industrial system with real regression faults. The study evaluates four common prioritization techniques, a test selection technique, a test suite minimization technique and a hybrid approach that combines selection and minimization. The study also examines the effects of using various coverage criteria on the effectiveness of the studied approaches. The results show that prioritization techniques that are based on additional coverage with finer grained coverage criteria perform significantly better in fault detection rates. The study also reveals that using modification information in prioritization techniques does not significantly enhance fault detection rates. The results show that test selection does not provide significant savings in execution cost <2%, which might be attributed to the nature of the changes made to the system. Test suite minimization using finer grained coverage criteria could provide significant savings in execution cost 79.5% while maintaining a fault detection capability level above 70%, thus representing a possible trade-off. The hybrid technique did not provide a significant improvement over traditional minimization techniques. Copyright © 2015John Wiley & Sons, Ltd.

Semantic fuzzing with zest

Abstract: Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program. Generator-based testing tools in the lineage of QuickCheck are a promising way to generate random syntactically valid test inputs for these programs. We present Zest, a technique which automatically guides QuickCheck-like random input generators to better explore the semantic analysis stage of test programs. Zest converts random-input generators into deterministic parametric input generators. We present the key insight that mutations in the untyped parameter domain map to structural mutations in the input domain. Zest leverages program feedback in the form of code coverage and input validity to perform feedback-directed parameter search. We evaluate Zest against AFL and QuickCheck on five Java programs: Maven, Ant, BCEL, Closure, and Rhino. Zest covers 1.03x-2.81x as many branches within the benchmarks' semantic analysis stages as baseline techniques. Further, we find 10 new bugs in the semantic analysis stages of these benchmarks. Zest is the most effective technique in finding these bugs reliably and quickly, requiring at most 10 minutes on average to find each bug.