ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

International audienceIn this paper, we propose a pioneering framework called ParadisEO-MO-GPU for the reusable design and implementation of parallel local search metaheuristics (S- Metaheuristics) on Graphics Processing Units (GPU). We revisit the ParadisEO-MO software framework to allow its utilization on GPU accelerators focusing on the parallel iteration-level model, the major parallel model for S- Metaheuristics. It consists in the parallel exploration of the neighborhood of a problem solution. The challenge is on the one hand to rethink the design and implementation of this model optimizing the data transfer between the CPU and the GPU. On the other hand, the objective is to make the GPU as transparent as possible for the user minimizing his or her involvement in its management. In this paper, we propose solutions to this challenge as an extension of the ParadisEO framework. The first release of the new GPU-based ParadisEO framework has been experimented on the permuted perceptron problem. The preliminary results are convincing, both in terms of flexibility and easiness of reuse at implementation, and in terms of efficiency at execution on GPU

Fault-Resilient Lightweight Cryptographic Block Ciphers for Secure Embedded Systems

The development of extremely-constrained environments having sensitive nodes such as RFID tags and nano-sensors necessitates the use of lightweight block ciphers. Indeed, lightweight block ciphers are essential for providing low-cost confidentiality to such applications. Nevertheless, providing the required security properties does not guarantee their reliability and hardware assurance when the architectures are prone to natural and malicious faults. In this thesis, considering false-alarm resistivity, error detection schemes for the lightweight block ciphers are proposed with the case study of XTEA (eXtended TEA). We note that lightweight block ciphers might be better suited for low-resource environments compared to the Advanced Encryption Standard, providing low complexity and power consumption. To the best of the author\u27s knowledge, there has been no error detection scheme presented in the literature for the XTEA to date. Three different error detection approaches are presented and according to our fault-injection simulations for benchmarking the effectiveness of the proposed schemes, high error coverage is derived. Finally, field-programmable gate array (FPGA) implementations of these proposed error detection structures are presented to assess their efficiency and overhead. The proposed error detection architectures are capable of increasing the reliability of the implementations of this lightweight block cipher. The schemes presented can also be applied to lightweight hash functions with similar structures, making the presented schemes suitable for providing reliability to their lightweight security-constrained hardware implementations

New Insight into the Isomorphism of Polynomials problem IP1S and its Use in Cryptography

This paper investigates the mathematical structure of the ``Isomorphism of Polynomial with One Secret\u27\u27 problem (IP1S). Our purpose is to understand why for practical parameter values of IP1S most random instances are easily solvable (as first observed by Bouillaguet et al.). We show that the structure of the problem is directly linked to the structure of quadratic forms in odd and even characteristic. We describe a completely new method allowing to efficiently solve most instances. Unlike previous solving techniques, this is not based upon Gröbner basis computations

Extension and hardware implementation of the comprehensive integrated security system concept

Merged with duplicate record (10026.1/700) on 03.01.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.The current strategy to computer networking is to increase the accessibility that legitimate users have to their respective systems and to distribute functionality. This creates a more efficient working environment, users may work from home, organisations can make better use of their computing power. Unfortunately, a side effect of opening up computer systems and placing them on potentially global networks is that they face increased threats from uncontrolled access points, and from eavesdroppers listening to the data communicated between systems. Along with these increased threats the traditional ones such as disgruntled employees, malicious software, and accidental damage must still be countered. A comprehensive integrated security system ( CISS ) has been developed to provide security within the Open Systems Interconnection (OSI) and Open Distributed Processing (ODP) environments. The research described in this thesis investigates alternative methods for its implementation and its optimisation through partial implementation within hardware and software and the investigation of mechanismsto improve its security. A new deployment strategy for CISS is described where functionality is divided amongst computing platforms of increasing capability within a security domain. Definitions are given of a: local security unit, that provides terminal security; local security servers that serve the local security units and domain management centres that provide security service coordination within a domain. New hardware that provides RSA and DES functionality capable of being connected to Sun microsystems is detailed. The board can be used as a basic building block of CISS, providing fast cryptographic facilities, or in isolation for discrete cryptographic services. Software written for UNIX in C/C++ is described, which provides optimised security mechanisms on computer systems that do not have SBus connectivity. A new identification/authentication mechanism is investigated that can be added to existing systems with the potential for extension into a real time supervision scenario. The mechanism uses keystroke analysis through the application of neural networks and genetic algorithms and has produced very encouraging results. Finally, a new conceptual model for intrusion detection capable of dealing with real time and historical evaluation is discussed, which further enhances the CISS concept

Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Спосіб криптографічно-строгої ідентифікації на основі хеш-перетворень з програмованими колізіями

Дослідження присвячено задачі підвищення ефективності криптографічного строгої ідентифікації віддалених користувачів в рамках криптографічної концепції «нульових знань» на основі хеш-перетворень з програмованими колізіями за рахунок збільшення кількості сеансових паролів доступу. В якості практичної сторони розроблено програмний продукт, здатний синтезувати незворотне хеш-перетворення з програмованими колізіями по заданим базовим параметрам відповідно запропонованого методу його побудови.The master's thesis is dedicated to the task of improving the effectiveness of cryptographic strict identification of remote users within the cryptographic concept of "zero-knowledge" based on hash transformations with programmable collisions through the increase of the number of session access passwords. As a practical side, a software product capable of synthesizing irreversible hash transformations with programmable collisions according to the proposed method was developed