A monitoring-based approach for WSN security using IEEE-802.15.4/6LowPAN and DTLS communication

International audienceIn this paper, we present a monitoring based approach for securing upper layer communications of WSN (Wireless Sensor Networks), the latter using IEEE802.15.4/6LoWPAN stacks and tinyDTLS. The monitoring techniques have been integrated as an extension to the industrial tool MMT (Montimage Monitoring Tool). The MMT-extension verifies that the network is working following a set of security rules that have been defined by ETSI. The security rules check if the protocol stack is working properly. If MMT detects a security rule that was not respected, then it sends an alarm to the system manager so that he can take properly reactive adjustments. We tested each of the security rules in MMT's extension using point-to-point configuration. After all these tests were verified, we tested our MMT-extension using real data gathered from the FIT IoT-LAB platform. The results of these tests shown that our MMT's extension for WSN using IEEE-802.15.4/6LowPAN and DTLS communication is feasible

Genome-wide association reveals host-specific genomic traits in Escherichia coli

Background Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. Results We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. Conclusions This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli

The owner, the provider and the subcontractors : how to handle accountability and liability management for 5G end to end service

The adoption of 5G services depends on the capacity to provide high-value services. In addition to enhanced performance, the capacity to deliver Security Service Level Agreements (SSLAs) and demonstrate their fulfillment would be a great incentive for the adoption of 5G services for critical 5G Verticals (e.g., service suppliers like Energy or Intelligent Transportation Systems) subject to specific industrial safety, security or service level rules and regulations (e.g., NIS or SEVESO Directives). Yet, responsibilities may be difficult to track and demonstrate because 5G infrastructures are interconnected and complex, which is a challenge anticipated to be exacerbated in future 6G networks. This paper describes a demonstrator and a use case that shows how 5G Service Providers can deliver SSLAs to their customers (Service Owners) by leveraging a set of network enablers developed in the INSPIRE-5Gplus project to manage their accountability, liability and trust placed in subcomponents of a service (subcontractors). The elaborated enablers are in particular a novel sTakeholder Responsibility, AccountabIity and Liability deScriptor (TRAILS), a Liability-Aware Service Management Referencing Service (LASM-RS), an anomaly detection tool (IoT-MMT), a Root Cause Analysis tool (IoT-RCA), two Remote Attestation mechanisms (Systemic and Deep Attestation), and two Security-by-Orchestration enablers (one for the 5G Core and one for the MEC)

Genome-wide association reveals host-specific genomic traits in Escherichia coli

Background: Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. Results: We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. Conclusions: This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli

Genome-wide association reveals host-specific genomic traits in Escherichia coli

BACKGROUND: Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. RESULTS: We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. CONCLUSIONS: This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Monitorage des aspects sécuritaires pour les protocoles de réseaux et applications

Computer security, also known as cyber-security or IT security, is always an emerging topic in computer science research. Because cyber attacks are growing in both volume and sophistication, protecting information systems or networks becomes a difficult task. Therefore, researchers in research community give an ongoing attention in security including two main directions: (i)-designing secured infrastructures with secured communication protocols and (ii)-monitoring/supervising the systems or networks in order to find and re-mediate vulnerabilities. The former assists the later by forming some additional monitoring-supporting modules. Whilst, the later verifies whether everything designed in the former is correctly and securely functioning as well as detecting security violations. This is the main topic of this thesis.This dissertation presents a security monitoring framework that takes into consideration different types of audit dataset including network traffic and application logs. We propose also some novel approaches based on supervised machine learning to pre-process and analyze the data input. Our framework is validated in a wide range of case studies including traditional TCP/IPv4 network monitoring (LAN, WAN, Internet monitoring), IoT/WSN using 6LoWPAN technology (IPv6), and other applications' logs. Last but not least, we provide a study regarding intrusion tolerance by design and propose an emulation-based approach to simultaneously detect and tolerate intrusion.In each case study, we describe how we collect the audit dataset, extract the relevant attributes, handle received data and decode their security meaning. For these goals, the tool Montimage Monitoring Tool (MMT) is used as the core of our approach. We assess also the solution's performance and its possibility to work in "larger scale" systems with more voluminous datasetLa sécurité informatique, aussi connue comme la cyber-sécurité, est toujours un sujet d'actualité dans la recherche en sciences informatiques. Comme les cyber-attaques grandissent de plus en plus en volume et en sophistication, la protection des systèmes ou réseaux d'information devient une tâche difficile. Les chercheurs dans la communauté de recherche prêtent une attention constante à la sécurité, en particulier ils s'orientent vers deux directions principales: (i) - la conception des infrastructures sécurisées avec des protocoles de communication sécurisés et (ii) - surveillance / supervision des systèmes ou des réseaux afin de trouver et de remédier des vulnérabilités. La dernière vérifie que tout ce qui a été conçu dans la première fonctionne correctement et en toute sécurité, ainsi détectant les violations de sécurité. Ceci étant le sujet principal de cette thèse.Cette dissertation présente un cadre de surveillance de la sécurité en tenant en compte des différents types de jeu de données d'audit y compris le trafic de réseaux et les messages échangés dans les applications. Nous proposons également des approches innovantes fondées sur l'apprentissage statistique, la théorie de l'information et de l'apprentissage automatique pour prétraiter et analyser l'entrée de données. Notre cadre est validé dans une large gamme des études de cas, y compris la surveillance des réseaux traditionnels TCP / IP (v4) (LAN, WAN, la surveillance de l'Internet), la supervision des réseaux de objets connectés utilisant la technologie 6LoWPAN (IPv6), et également, l’analyse des logs d'autres applications. Enfin, nous fournissons une étude sur la tolérance d’intrusion par conception et proposons une approche basée sur l’émulation pour détecter et tolérer l’intrusion simultanément.Dans chaque étude de cas, nous décrivons comment nous collectons les jeux de données d'audit, extrayons les attributs pertinents, traitons les données reçues et décodons leur signification de sécurité. Pour attendre ces objectifs, l'outil MMT est utilisé comme le cœur de notre approche. Nous évaluons également la performance de la solution et sa possibilité de marcher dans les systèmes “à plus grande échelle” avec des jeux de données plus volumineu

Towards improving explainability, resilience and performance of cybersecurity analysis of 5G/IoT networks

International audienceArtificial Intelligence (AI) is envisioned to play a critical role in controlling and orchestrating 5G/IoT networks and their applications, thanks to its capabilities to recognize abnormal patterns in complex situations and produce accurate decisions. However, AI models are vulnerable to adversarial attacks, thus the societal view is far from trustworthy as to its usage in safety critical areas relying on 5G/IoT networks. In this paper, we present ongoing work being done in the H2020 SPATIAL project that targets developing and evaluating AI-based modules for anomaly detection and Root Cause Analysis in the 5G/IoT context regarding different criteria, such as explainability, resilience and performance on a real 5G/IoT testbed

Mobility-aware estimation of content consumption hotspots for urban cellular networks

International audience—A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at the base station and backhauling network level. Intuitively, an adaptive placement of content and computing resources in the most crowded regions can grant important traffic offloading, improve network efficiency and user quality of experience. In this paper we document the content consumption in the Orange cellular network for the Paris metropolitan area, from spatial and application-level extensive analysis of real data from a few million users, reporting the experimental distributions. In this scope, we propose a hotspot cell estimator computed over user's mobility metrics and based on linear regression. Evaluating our estimator on real data, it appears as an excellent hotspot detection solution of cellular and backhauling network management. We show that its error strictly decreases with the cell load, and it is negligible for reasonable hotspot cell load upper thresholds. We also show that our hotspot estimator is quite scalable against mobility data volume and against time variations