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Laboratoire d’analyse et d’architecture des systèmes

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1417documents trouvés

17109
27/04/2017

State of the art of network protocol reverse engineering tools

J.DUCHENE, C.LE GUERNIC, E.ALATA, V.NICOMETTE, M.KAANICHE

TSF, INRIA Rennes

Rapport LAAS N°17109, doi 10.1007/s11416-016-0289-8, Avril 2017, 27p.

Lien : https://hal.inria.fr/hal-01496958

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Abstract

Communication protocols enable structured information exchanges between different entities. A description, at different levels of detail, is necessary for many applications, such as interoperability or security audits. When such a description is not available, one can resort to protocol reverse engineering to infer the format of exchanged messages or a model of the protocol. During the past 12 years, several tools have been developed in order to automate, entirely or partially, the protocol inference process. Each of those tools has been developed with a specific application goal for the inferred model, leading to specific needs, and thus different strengths and limitations. After identifying key challenges, the paper presents a survey of protocol reverse engineering tools developed in the last decade. We consider tools focusing on the inference of the format of individual messages or of the grammar of sequences of messages. Finally, we propose a classification of these tools according to different criteria, that is aimed at providing relevant insights about the techniques used by each of these tools and comparatively to other tools, for the classification of messages, the inference of their format or of the grammar of the protocol. This classification also permits to identify technical areas that are not sufficiently explored so far and that require further development in the future.

139658
17104
25/04/2017

A toolset for mobile systems testing

P.ANDRE, N.RIVIERE, H.WAESELYNCK

TSF

Rapport LAAS N°17104, Avril 2017, 8p.

Lien : https://hal.laas.fr/hal-01499518

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Abstract

Validation of mobile applications needs taking account of context (such network topology) and interactions between mobile nodes. Scenario-based approaches are well-suited to describe the behavior and interactions to observe in distributed systems. The difficulty to control accurately the execution context of such applications has led us to use passive testing. This paper presents a toolset which supports specification and verification of scenarios. A UML-based formal language, called TERMOS, has been implemented for specifying scenarios in mobile computing systems. These scenarios capture the key properties which are automatically checked on the traces, considering both the spatial configuration of nodes and their communication. We give an overview of the language design choices, its semantics and the implementation of the tool chain. The approach is demonstrated on a case study.

139621
17031
14/03/2017

Synthesis of safety rules for active monitoring: application to an airport light measurement robot

L.MASSON, J.GUIOCHET, H.WAESELYNCK, A.DESFOSSES, M.LAVAL

TSF, Sterela

Rapport LAAS N°17031, Mars 2017, 8p.

Lien : https://hal.archives-ouvertes.fr/hal-01465734

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Abstract

Safety-critical autonomous systems, like robots working in collaboration with humans, are about to be used in diverse environments such as industry but also public spaces or hospitals. Those systems evolve in complex and dynamic environments and are exposed to a wide variety of hazards. Several techniques may be used to ensure that their misbehavior cannot cause unacceptable damage or harm. One of them is active safety monitoring. A safety monitor is a component responsible for maintaining the system in a safe state despite the occurrence of hazardous situations. In this paper, we study the introduction of safety monitoring into an airport light measurement robot. The specification of the monitor follows a principled approach that starts with a hazard analysis and ends with a set of safety rules synthesized based on formal methods. This study illustrates the benefits of the approach, and shows the impact of safety on the development of an autonomous system.

139242
16018
01/03/2017

Quantifying interdependent privacy risks with location data

A.M.OLTEANU, K.HUGUENIN, R.SHOKRI, M.HUMBERT, J.P.HUBAUX

EPFL, TSF, University of Texas, Max Planck

Revue Scientifique : IEEE Transactions on Mobile Computing, Vol.16, N°3, pp.829-842, Mars 2017 , N° 16018

Lien : https://hal.archives-ouvertes.fr/hal-01266229

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Abstract

Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users' IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users' locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

138855
17035
01/02/2017

Architecting resilient computing systems: A component-based approach for adaptive fault tolerance

M.STOICESCU, J.C.FABRE, M.ROY

ESOC, TSF

Revue Scientifique : Journal of Systems Architecture, Vol.73, pp.6-16, Février 2017 , N° 17035

Lien : https://hal.archives-ouvertes.fr/hal-01472877

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Abstract

Evolution of systems during their operational life is mandatory and both updates and upgrades should not impair their dependability properties. Dependable systems must evolve to accommodate changes, such as new threats and undesirable events, application updates or variations in available resources. A system that remains dependable when facing changes is called resilient. In this paper, we present an innovative approach taking advantage of component-based software engineering technologies for tackling the on-line adaptation of fault tolerance mechanisms. We propose a development process that relies on two key factors: designing fault tolerance mechanisms for adaptation and leveraging a reflective component-based middleware enabling fine-grained control and modification of the software architecture at run-time. We thoroughly describe the methodology, the development of adaptive fault tolerance mechanisms and evaluate the approach in terms of performance and agility.

139257
17013
31/01/2017

SMOF - A Safety MOnitoring Framework for Autonomous Systems

M.MACHIN, J.GUIOCHET, H.WAESELYNCK, J.P.BLANQUART, M.ROY, L.MASSON

TSF, ASTRIUM

Rapport LAAS N°17013, doi 10.1109/TSMC.2016.2633291, Janvier 2017

Lien : https://hal.archives-ouvertes.fr/hal-01394139

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Abstract

Safety critical systems with decisional abilities, such as autonomous robots, are about to enter our everyday life. Nevertheless, confidence in their behavior is still limited, particularly regarding safety. Considering the variety of hazards that can affect these systems, many techniques might be used to increase their safety. Among them, active safety monitors are a means to maintain the system safety in spite of faults or adverse situations. The specification of the safety rules implemented in such devices is of crucial importance, but has been hardly explored so far. In this paper, we propose a complete framework for the generation of these safety rules based on the concept of safety margin. The approach starts from a hazard analysis, and uses formal verification techniques to automatically synthesize the safety rules. It has been successfully applied to an industrial use case, a mobile manipulator robot for co-working.

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17071
22/01/2017

An abstraction model and a comparative analysis of Intel and ARM hardware isolation mechanisms

G.AVERLANT, B.MORGAN, E.ALATA, V.NICOMETTE, M.KAANICHE

TSF

Manifestation avec acte : IEEE Pacific Rim International Symposium on Dependable Computing ( PRDC ) 2017 du 22 janvier au 25 janvier 2017, Christchurch (Nouvelle Zélande), Janvier 2017, 10p. , N° 17071

Lien : https://hal.laas.fr/hal-01493597

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Abstract

Computer systems software and hardware architec- tures have become increasingly complex today. Meanwhile, cyber- attacks are becoming more and more sophisticated and target any software or hardware components of these systems. Several isolation mechanisms, at the software and the hardware layers, are now available to provide the best protection against these widespread attacks. This paper is aimed at reviewing especially hardware segregation mechanisms available in today’s CPU in order to provide better insights about the intended scope of the protection and the different threats that could be addressed by such mechanisms. An abstraction model presenting the main components of current architectures and their interactions through different communication channels is proposed to support such analysis. The study focuses on Intel and ARM architectures, and outlines various hardware isolation resources that provide a security layer to the software running on these architectures. A comparative analysis of these architectures is also presented together with a discussion of open issues and future challenges.

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16462
13/12/2016

Monitoring et détection d'anomalie par apprentissage dans des infrastructures virtualisées

C.SAUVANAUD

TSF

Doctorat : INSA de Toulouse, 13 Décembre 2016, 174p., Président: E.EXPOSITO, Rapporteurs: S.BOUCHENAK, P.SENS, Examinateurs: K.LAZRI, Directeurs de thèse: M.KAANICHE, K.KANOUN , N° 16462

Lien : https://hal.laas.fr/tel-01445648

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Abstract

Nowadays, the development of virtualization technologies as well as the development of the Internet contributed to the rise of the cloud computing model. A cloud computing enables the delivery of configurable computing resources while enabling convenient, on-demand network access to these resources. Resources hosted by a provider can be applications, development platforms or infrastructures. Over the past few years, computing systems are characterized by high development speed, parallelism, and the diversity of task to be handled by applications and services. In order to satisfy their Service Level Agreements (SLA) drawn up with users, cloud providers have to handle stringent dependability demands. Ensuring these demands while delivering various services makes clouds dependability a challenging task, especially because providers need to make their services available on demand. This task is all the more challenging that users expect cloud services to be at least as dependable as traditional computing systems. In this manuscript, we address the problem of anomaly detection in cloud services. A detection strategy for clouds should rely on several principal criteria. In particular it should adapt to workload changes and reconfigurations, and at the same time require short configurations durations and adapt to several types of services. Also, it should be performed online and automatic. Finally, such a strategy needs to tackle the detection of different types of anomalies namely errors, preliminary symptoms of SLA violation and SLA violations. We propose a new detection strategy based on system monitoring data. The data is collected online either from the service, or the underlying hypervisor(s) hosting the service. The strategy makes use of machine learning algorithms to classify anomalous behaviors of the service. Three techniques are used, using respectively algorithms with supervised learning, unsupervised learning or using a technique exploiting both types of learning. A new anomaly detection technique is developed based on online clustering, and allowing to handle possible changes in a service behavior. A cloud platform was deployed so as to evaluate the detection performances of our strategy. Moreover a fault injection tool was developed for the sake of two goals : the collection of service observations with anomalies so as to train detection models, and the evaluation of the strategy in presence of anomalies. The evaluation was applied to two case studies : a database management system and a virtual network function. Sensitivity analyzes show that detection performances of our strategy are high for the three anomaly types. The context for the generalization of the results is also discussed.

Résumé

Le cloud computing est un modèle de délivrance à la demande d’un ensemble de ressources informatiques distantes, partagées et configurables. Ces ressources, détenues par un fournisseur de service cloud, sont mutualisées grâce à la virtualisation de serveurs qu’elles composent et sont mises à disposition d’utilisateurs sous forme de services disponibles à la demande. Ces services peuvent être aussi variés que des applications, des plateformes de développement ou bien des infrastructures. Afin de répondre à leurs engagements de niveau de service auprès des utilisateurs, les fournisseurs de cloud se doivent de prendre en compte des exigences différentes de sûreté de fonctionnement. Assurer ces exigences pour des services différents et pour des utilisateurs aux demandes hétérogènes représente un défi pour les fournisseurs, notamment de part leur engagement de service à la demande. Ce défi est d’autant plus important que les utilisateurs demandent à ce que les services rendus soient au moins aussi sûrs de fonctionnement que ceux d’applications traditionnelles. Nos travaux traitent particulièrement de la détection d’anomalies dans les services cloud de type SaaS et PaaS. Les différents types d’anomalie qu’il est possible de détecter sont les erreurs, les symptômes préliminaires de violations de service et les violations de service. Nous nous sommes fixé quatre critères principaux pour la détection d’anomalies dans ces services : i) elle doit s’adapter aux changements de charge de travail et reconfiguration de services ; ii) elle doit se faire en ligne, iii) de manière automatique, iv) et avec un effort de configuration minimum en utilisant possiblement la même technique quel que soit le type de service. Dans nos travaux, nous avons proposé une stratégie de détection qui repose sur le traitement de compteurs de performance et sur des techniques d’apprentissage automatique. La détection utilise les données de performance système collectées en ligne à partir du système d’exploitation hôte ou bien via les hyperviseurs déployés dans le cloud. Concernant le traitement des ces données, nous avons étudié trois types de technique d’apprentissage : supervisé, non supervisé et hybride. Une nouvelle technique de détection reposant sur un algorithme de clustering est de plus proposée. Elle permet de prendre en compte l’évolution de comportement d’un système aussi dynamique qu’un service cloud. Une plateforme de type cloud a été déployée afin d’évaluer les performances de détection de notre stratégie. Un outil d’injection de faute a également été développé dans le but de cette évaluation ainsi que dans le but de collecter des jeux de données pour l’entrainement des modèles d’apprentissage. L’évaluation a été appliquée à deux cas d’étude : un système de gestion de base de données (MongoDB) et une fonction réseau virtualisée. Les résultats obtenus à partir d’analyses de sensibilité, montrent qu’il est possible d’obtenir de très bonnes performances de détection pour les trois types d’anomalies, tout en donnant les contextes adéquats pour la généralisation de ces résultats.

Mots-Clés / Keywords
Apprentissage automatique; Cloud computing; Détection d'anomalie; Injection de fautes; Monitoring; Virtualisation;

138473
16433
12/12/2016

XPIR : Private information retrieval for everyone

C.AGUILAR-MELCHOR, J.BARRIER, L.FOUSSE, M.O.KILLIJIAN

IRIT-ENSEEIHT, TSF, LJK

Revue Scientifique : Proceedings on Privacy Enhancing Technologies, Vol.2016, pp.155-174, Décembre 2016 , N° 16433

Lien : https://hal.archives-ouvertes.fr/hal-01396142

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Abstract

A Private Information Retrieval (PIR) scheme is a protocol in which a user retrieves a record from a database while hiding which from the database administrators. PIR can be achieved using mutually-distrustful replicated databases, trusted hardware, or cryptography. In this paper we focus on the later setting which is known as single-database computationally-Private Information Retrieval (cPIR). Classic cPIR protocols require that the database server executes an algorithm over all the database content at very low speeds which impairs their usage. In [1], given certain assumptions , realistic at the time, Sion and Carbunar showed that cPIR schemes were not practical and most likely would never be. To this day, this conclusion is widely accepted by researchers and practitioners. Using the paradigm shift introduced by lattice-based cryptography , we show that the conclusion of Sion and Carbunar is not valid anymore: cPIR is of practical value. This is achieved without compromising security, using standard crytosystems, and conservative parameter choices.

138345
16431
12/12/2016

Dependable advanced robots: a survey

J.GUIOCHET, M.MACHIN, H.WAESELYNCK

TSF

Rapport LAAS N°16431, Décembre 2016

Lien : https://hal.archives-ouvertes.fr/hal-01394136

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Abstract

Developing advanced robotics applications is now facing the confidence issue for users, which is a main limitation for their deployment in real life. This confidence could be justified by the use of dependability techniques as it is done in other safety critical applications. However, due to specific robotic properties (such as continuous human-robot physical interaction or non deterministic deliberative layer), many techniques need to be adapted or revised. This paper reviews the main issues and research work in the field of dependable robots, making the link between the dependability and robotics concepts. It also presents main challenges for increasing robot dependability.

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