Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study

International audienceOriginally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance

Ontologies for specifying and reconciling contexts of web services

This paper presents an ontology-based approach for the specification (using OWL-C as a definition language) and reconciliation (using ConWeS as a mediation tool) of contexts of Web services. Web services are independent components that can be triggered and composed for the satisfaction of user needs (e.g., hotel booking). Because Web services originate from different providers, their composition faces the obstacle of the context heterogeneity featuring these Web services. An unawareness of this context heterogeneity during Web services composition and execution results in a lack of the quality and relevancy of information that permits tracking the composition, monitoring the execution, and handling exceptions. © 2006 Elsevier B.V. All rights reserved

Snapshot Provisioning of Cloud Application Stacks to Face Traffic Surges

Traffic surges, like the Slashdot effect, occur when a web application is overloaded by a huge number of requests, potentially leading to unavailability. Unfortunately, such traffic variations are generally totally unplanned, of great amplitude, within a very short period, and a variable delay to return to a normal regime. In this report, we introduce PeakForecast as an elastic middleware solution to detect and absorb a traffic surge. In particular, PeakForecast can, from a trace of queries received in the last seconds, minutes or hours, to detect if the underlying system is facing a traffic surge or not, and then estimate the future traffic using a forecast model with an acceptable precision, thereby calculating the number of resources required to absorb the remaining traffic to come. We validate our solution by experimental results demonstrating that it can provide instantaneous elasticity of resources for traffic surges observed on the Japanese version of Wikipedia during the Fukushima Daiichi nuclear disaster in March 2011.Les pics de trafic, tels que l'effet Slashdot, apparaissent lorsqu'une application web doit faire face un nombre important de requêtes qui peut potentiellement entraîner une mise hors service de l'application. Malheureusement, de telles variations de traffic sont en général totalement imprévues et d'une grande amplitude, arrivent pendant une très courte période de temps et le retour à un régime normal prend un délai variable. Dans ce rapport, nous présentons PeakForecast qui est une solution intergicielle élastique pour détecter et absorber les pics de trafic. En particulier, PeakForecast peut, à partir des traces de requêtes reçues dans les dernières secondes, minutes ou heures, détecter si le système sous-jacent fait face ou non à un pic de trafic, estimer le trafic futur en utilisant un modèle de prédiction suffisamment précis, et calculer le nombre de ressources nécessaires à l'absorption du trafic restant à venir. Nous validons notre solution avec des résultats expérimentaux qui démontrent qu'elle fournit une élasticité instantanée des ressources pour des pics de trafic qui ont été observés sur la version japonaise de Wikipedia lors de l'accident nucléaire de Fukushima Daiichi en mars 2011

Snapshot Provisioning of Cloud Application Stacks to Face Traffic Surges

Traffic surges, like the Slashdot effect, occur when a web application is overloaded by a huge number of requests, potentially leading to unavailability. Unfortunately, such traffic variations are generally totally unplanned, of great amplitude, within a very short period, and a variable delay to return to a normal regime. In this report, we introduce PeakForecast as an elastic middleware solution to detect and absorb a traffic surge. In particular, PeakForecast can, from a trace of queries received in the last seconds, minutes or hours, to detect if the underlying system is facing a traffic surge or not, and then estimate the future traffic using a forecast model with an acceptable precision, thereby calculating the number of resources required to absorb the remaining traffic to come. We validate our solution by experimental results demonstrating that it can provide instantaneous elasticity of resources for traffic surges observed on the Japanese version of Wikipedia during the Fukushima Daiichi nuclear disaster in March 2011.Les pics de trafic, tels que l'effet Slashdot, apparaissent lorsqu'une application web doit faire face un nombre important de requêtes qui peut potentiellement entraîner une mise hors service de l'application. Malheureusement, de telles variations de traffic sont en général totalement imprévues et d'une grande amplitude, arrivent pendant une très courte période de temps et le retour à un régime normal prend un délai variable. Dans ce rapport, nous présentons PeakForecast qui est une solution intergicielle élastique pour détecter et absorber les pics de trafic. En particulier, PeakForecast peut, à partir des traces de requêtes reçues dans les dernières secondes, minutes ou heures, détecter si le système sous-jacent fait face ou non à un pic de trafic, estimer le trafic futur en utilisant un modèle de prédiction suffisamment précis, et calculer le nombre de ressources nécessaires à l'absorption du trafic restant à venir. Nous validons notre solution avec des résultats expérimentaux qui démontrent qu'elle fournit une élasticité instantanée des ressources pour des pics de trafic qui ont été observés sur la version japonaise de Wikipedia lors de l'accident nucléaire de Fukushima Daiichi en mars 2011

Investigating the influence of special on-off attacks on challenge-based collaborative intrusion detection networks

Intrusions are becoming more complicated with the recent development of adversarial techniques. To boost the detection accuracy of a separate intrusion detector, the collaborative intrusion detection network (CIDN) has thus been developed by allowing intrusion detection system (IDS) nodes to exchange data with each other. Insider attacks are a great threat for such types of collaborative networks, where an attacker has the authorized access within the network. In literature, a challenge-based trust mechanism is effective at identifying malicious nodes by sending challenges. However, such mechanisms are heavily dependent on two assumptions, which would cause CIDNs to be vulnerable to advanced insider attacks in practice. In this work, we investigate the influence of advanced on–off attacks on challenge-based CIDNs, which can respond truthfully to one IDS node but behave maliciously to another IDS node. To evaluate the attack performance, we have conducted two experiments under a simulated and a real CIDN environment. The obtained results demonstrate that our designed attack is able to compromise the robustness of challenge-based CIDNs in practice; that is, some malicious nodes can behave untruthfully without a timely detection

Detecting Software Aging in a Cloud Computing Framework by Comparing Development Versions

Abstract-Software aging, i.e. degradation of software performance or functionality caused by resource depletion is usually discovered only in the production scenario. This incurs large costs and delays of defect removal and requires provisional solutions such as rejuvenation (controlled restarts). We propose a method for detecting aging problems shortly after their introduction by runtime comparisons of different development versions of the same software. Possible aging issues are discovered by analyzing the differences in runtime traces of selected metrics. The required comparisons are workload-independent which minimizes the additional effort of dedicated stress tests. Consequently, the method requires only minimal changes to the traditional development and testing process. This paves the way to detecting such problems before public releases, greatly reducing the cost of defect fixing. Our study focuses on the memory leaks of Eucalyptus, a popular open source framework for managing cloud computing environments

Short Paper: Automatic Configuration for an Optimal Channel Protection in Virtualized Networks

Data confidentiality, integrity and authentication are security properties which are often enforced with the generation of secure channels, such as Virtual Private Networks, over unreliable network infrastructures. Traditionally, the configuration of the systems responsible of encryption operations is performed manually. However, the advent of software-based paradigms, such as Software-Defined Networking and Network Functions Virtualization, has introduced new arms races. In particular, even though network management has become more flexible, the increased complexity of virtual networks is making manual operations unfeasible and leading to errors which open the path to a large number of cyber attacks. A possible solution consists in reaching a trade-off between flexibility and complexity, by automatizing the configuration of the channel protection systems through policy refinement. In view of these considerations, this paper proposes a preliminary study for an innovative methodology to automatically allocate and configure channel protection systems in virtualized networks. The proposed approach would be based on the formulation of a MaxSMT problem and it would be the first to combine automation, formal verification and optimality in a single technique

Integrating virtual reality and gis tools for geological mapping, data collection and analysis: An example from the metaxa mine, santorini (Greece)

In the present work we highlight the effectiveness of integrating different techniques and tools for better surveying, mapping and collecting data in volcanic areas. We use an Immersive Virtual Reality (IVR) approach for data collection, integrated with Geographic Information System (GIS) analysis in a well-known volcanological site in Santorini (Metaxa mine), a site where volcanic processes influenced the island’s industrial development, especially with regard to pumice mining. Specifically, we have focused on: (i) three-dimensional (3D) high-resolution IVR scenario building, based on Structure from Motion photogrammetry (SfM) modeling; (ii) subsequent geological survey, mapping and data collection using IVR; (iii) data analysis, e.g., calculation of extracted volumes, as well as production of new maps in a GIS environment using input data directly from the IVR survey; and finally, (iv) presentation of new outcomes that highlight the importance of the Metaxa Mine as a key geological and volcanological geosite

A Novel Efficient Dynamic Throttling Strategy for Blockchain-Based Intrusion Detection Systems in 6G-Enabled VSNs

Vehicular Social Networks (VSNs) have emerged as a new social interaction paradigm, where vehicles can form social networks on the roads to improve the convenience/safety of passengers. VSNs are part of Vehicle to Everything (V2X) services, which is one of the industrial verticals in the coming sixth generation (6G) networks. The lower latency, higher connection density, and near-100% coverage envisaged in 6G will enable more efficient implementation of VSNs applications. The purpose of this study is to address the problem of lateral movements of attackers who could compromise one device in a VSN, given the large number of connected devices and services in VSNs and attack other devices and vehicles. This challenge is addressed via our proposed Blockchain-based Collaborative Distributed Intrusion Detection (BCDID) system with a novel Dynamic Throttling Strategy (DTS) to detect and prevent attackers’ lateral movements in VSNs. Our experiments showed how the proposed DTS improve the effectiveness of the BCDID system in terms of detection capabilities and handling queries three times faster than the default strategy with 350k queries tested. We concluded that our DTS strategy can increase transaction processing capacity in the BCDID system and improve its performance while maintaining the integrity of data on-chain