ASF Winter School 2019

The goal of this presentation is to provide an introduction to the domain of security relevant bug discovery in embedded systems which are at the core of the Internet of Things. Embedded software has a number of particularities which makes it slightly different than general purpose software. In particular, embedded devices are more exposed to software attacks but have less defenses and are often left unattended. At the same time, analyzing their security is more difficult because they are very "opaque'', while the execution of custom and embedded software is often entangled with the hardware and peripherals. Those differences have an impact on our ability to find software bugs in such systems. This talk will present how software vulnerabilities can be identified, at different stages of the software life-cycle, (for example during development, integration of different components, testing, by the deployment of the device or in the field by third parties) and using different approaches (static analysis, emulation with hardware in the loop, or full emulation).

Aurélien Francillon is an assistant professor in the Networking and Security department at EURECOM, in the System and Software Security group (http://s3.eurecom.fr). Before this he received PhD degree in 2009 from INRIA and Grenoble INP, then, he was a postdoctoral researcher in the System Security Group at ETH Zurich. He is mainly interested in practical aspects of the security of embedded devices. In this context he has worked on topics such as code injection, code attestation, random number generation, hardware support for software security, bug finding techniques as well as on broader security and privacy topics.

Large-scale distributed systems include millions of components, and they induce two major problems: resilience and energy consumption. Resilience is (loosely) defined as surviving to failures. Failures are usually handled by adding redundancy, either continuously (replication) or at periodic intervals (migration from faulty node to spare node, rollback and recovery). In the latter case, the state of an application must be preserved (checkpointing), and the system must roll back to the last saved checkpoint. However, the amount of replication and/or the frequency of checkpointing must be optimized carefully, and we will discuss how to optimally decide the checkpointing interval. We will also tackle the second important challenge of power consumption. Power management is necessary due to both monetary and environmental constraints. Energy is needed to provide power to the individual cores and also to provide cooling for the system. Using dynamic voltage and frequency scaling (DVFS) is a widely used technique to decrease energy consumption, but it can severely degrade performance and increase execution time. We will exhibit energy-efficient algorithms for various models.

Anne Benoit received the PhD degree from Institut National Polytechnique de Grenoble in 2003, and the Habilitation à Diriger des Recherches (HDR) from Ecole Normale Supérieure de Lyon (ENS Lyon) in 2009. She is currently an associate professor in the Computer Science Laboratory LIP at ENS Lyon, France. She is the author of one book on algorithm design, 43 papers published in international journals, and 87 papers published in international conferences. She is the advisor of 9 PhD theses. Her research interests include algorithm design and scheduling techniques for parallel and distributed platforms, and also the performance evaluation of parallel systems and applications, with a focus on energy awareness and resilience. She is Associate Editor (in Chief) of Elsevier ParCo, and Associate Editor of IEEE TPDS and Elsevier JPDC. She is the program chair of several workshops and conferences, in particular she is the program chair for HiPC’16, ICPP’17, SC’17 (papers chair), and IPDPS’18. She is a senior member of the IEEE, and she has been elected a Junior Member of Institut Universitaire de France in 2009.

Until recently, structured (e.g., relational) and unstructured (e.g., textual) data were managed very differently: Structured data was queried declaratively using languages such as SQL, while unstructured data was searched using boolean queries over inverted indices. Today, we witness the rapid emergence of Big Data Integration techniques leveraging knowledge graphs to bridge the gap between different types of contents and integrate both unstructured and structured information more effectively. I will start this talk by giving a few examples of Big Data Integration. I will then describe two recent systems built in my lab and leveraging such techniques: ZenCrowd, a socio-technical platform that automatically connects Web documents to semi-structured entities in a knowledge graph, and Guider, a Big Data Integration system for the cloud that has been productized on Azure.

Philippe Cudre-Mauroux is a Full Professor and the Director of the eXascale Infolab at the University of Fribourg in Switzerland. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL, where he won both the Doctorate Award and the EPFL Press Mention in 2007. Before joining the University of Fribourg, he worked on information management infrastructures at IBM Watson (NY), Microsoft Research (Asia & Silicon Valley), and MIT. He recently won the Verisign Internet Infrastructures Award, a Swiss National Center in Research award, a Google Faculty Research Award, as well as a 2-millions Euro grant from the European Research Council. His research interests are in next-generation, Big Data management infrastructures for non-relational data.

In order to meet rising expectations in terms of scalability, robustness, and flexibility, large scale distributed systems increasingly espouse sophisticated distributed architectures that require enforcing complex distributed structural invariants. Unfortunately, maintaining these structural invariants at scale is particularly time consuming and error prone, as developers must take into account asynchronous failures, loosely coordinated sub-systems and network delays. In this talk, I will describe PLEIADES, a new platform to construct and enforce large-scale distributed structural invariants under aggressive conditions. PLEIADES combines the resilience of self-organizing overlays, with the expressiveness of an assembly-based design strategy. The result is a highly survivable framework that is able to dynamically maintain arbitrary complex distributed structures under aggressive crash failures.

François Taïani is a Professor in Distributed Computer Systems at the Université de Rennes 1 (ESIR) and IRISA/Inria in Brittany, France. Prior to that, he was with Lancaster University (UK) from 2005 to 2012, where he held a Lectureship (roughly tenured Assistant Professor). François holds a PhD from Université Toulouse III (France) for a work performed at LAAS/CNRS, a Diplom der Informatik from Universität Stuttgart (Germany), and a Diplôme d'Ingénieur from Ecole Centrale Paris (France). François enjoys working with the industry and with partners from other disciplines. This includes past visits and placements at major industrial labs (e.g. at Bayer AG and Daimler in Germany, AT&T Labs in the US), and more recently collaborative projects with Technicolor and Mediego, a startup company focusing on personalization.

With the advent of connected devices with computation and storage capabilities, it becomes possible to run machine learning on-device to provide personalized services. However, the currently dominant approach is to centralize data from all users on an external server for batch processing, sometimes without explicit consent from users and with little oversight. This centralization poses important privacy issues in applications involving sensitive data such as speech, medical records or geolocation logs.

I will start this talk with a short crash course on machine learning in this standard centralized setting. I will then introduce an alternative scenario where many agents with local datasets collaborate to learn models by engaging in a fully decentralized peer-to-peer network without sharing their data, and present some of my recent work in designing efficient and privacy-preserving algorithms for this setting.

Aurélien Bellet is a research scientist at Inria. He received his Ph.D. from the University of Saint-Etienne in 2012 and, prior to joining Inria, he was a postdoctoral researcher at the University of Southern California (USA) and at Télécom ParisTech. His main line of research is statistical machine learning, with particular interests in developing large-scale algorithms which allow good trade-offs between computational complexity (or other "resources", such as privacy or communication) and statistical performance. His recent work focuses on designing and analyzing decentralized and privacy-preserving learning algorithms. His work has been published in top machine learning venues such as ICML, NIPS and AISTATS.

High-end IoT devices are one of the most important instances of the connected devices [6] supporting a noteworthy shift towards mobile Internet access. As our lives become more dependent on pervasive connectivity, our social patterns (as human being in the Internet era) are nowadays being reflected from our real life onto the virtual binary world. This gives birth to two tendencies. From one side, edge networks can now be utilized as mirrors to reflect the inherent human dynamics, their context, and interests thanks to their well organized recording and almost ubiquitous coverage. From the other side, social norms and structure dictating human behavior (e.g., interactions, mobility, interest, cultural patterns) are now directly influencing the way individuals interact with the network services and demand resources or content. This suggests the integration of the heterogeneity and uncertainty of behaviors in designed networking solutions. Besides, this implies the extraction of representative knowledge from end-users’ environment and behavior and the transfer of such knowledge to a networking perspective, adapting thus, current design practices. This brings the idea of a more tactful networking design practice where the network is assigned with the human like capability of observation, interpretation, and reaction to daily life features and entities involving high-end IoT devices. In this talk, I will present my works on this direction and discuss some networking solutions leveraging “human behavior”.

Aline Carneiro Viana (Female) is a permanent researcher at Inria, where she leads the team INFINE-POST. After a 1-year sabbatical leave at the TKN Group of the TU-Berlin, Germany, she got her habilitation degree from UPMC - Sorbonne Universités, France in 2011. Dr. Viana got her PhD in Computer Science from the UPMC - Sorbonne Universités in 2005. After having hold a postdoctoral position at IRISA/Inria Rennes, she obtained a permanent position at Inria in 2006. Her research addresses the design of solutions for tactful networking, smart cities, mobile and self-organizing networks with the focus on: human behavior analysis, opportunistic routing and data dissemination, and mobile traffic data mining. She is a recipient of the French Scientific Excellence award (20015) and was nominated in 2016 as one of the “10 women in networking/communications that you should Watch” (1st-year nomination of N2Women community). She has published 100 papers, presented in these fields in top-tier conferences (i.e., ACM MobiHoc, IEEE SECON, IEEE Infocom, IEEE PERCOM) and in peer-reviewed journals (i.e., IEEE Transaction on Mobile Computing, IEEE Transaction on Smart Grid Journal, Pervasive and Mobile Computing Elsevier, ACM Computing Surveys, Computer Networks Elsevier). She has been involved in the organizing committee as well as been a TPC member of major conferences (ACM Mobicom, IEEE SECON, IEEE MASS, IEEE DCOSS, IEEE LCN, IEEE PIMRC). She is also Area Editor of ACM Computer Communication Review (ACM CCR) and member of the editorial Board of Urban Computing Spring book series as well as of Wireless Communications and Mobile Computing Open Access Journal of John Wiley&Sons and Hindawi. She has participated in many projects and has coordinated the EU CHIST-ERA MACACO (with 5 European partners) and the STIC AmSud UCOOL (with 1 French and 5 Latine American partners) projects. She is also the coordinator of the Inria Associate Team EMBRACE (between Inria and 3 Brazilian partners).

In a virtualized system, the hypervisor relies on a privileged virtual machine (pVM). The pVM accomplishes work both for the hypervisor (e.g., VM life cycle management) and for client VMs (I/O management). Currently, the pVM is based on a standard OS (Linux). This is source of performance unpredictability, low level performance, resource waste, and vulnerabilities. This paper presents Edge4pVM, a principle for designing a suitable OS for the pVM. Edge4pVM consists in respectively scheduling and allocating pVM's tasks and memory as close to the involved client VM as possible. By revisiting Linux and Xen hypervisor, we present a functioning implementation of Edge4pVM. The evaluation results of our implementation show that Edge4pVM outperforms standard implementations.

Alain Tchana received his Ph.D. in computer science in 2011 at Institut National Polytechnique de Toulouse. The research topic of his Ph.D. was autonomic computing applied to cloud environments. He then spent two yeas as a postdoc at Université Joseph Fourier. During that time, he worked on building benchmarking systems. From September 2013 to September 2018, he was Associate Professor at Institut National Polytechnique de Toulouse. He was member of SEPIA research group at IRIT laboratory. His main research domain is virtualization. Since September 2018, he is full professor at Université de Nice Sophia-Antipolis. He is member of Scale research group at I3S. He continues to work in the virtualization domain.