Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.Comment: Proceedings of the Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016

Network Security through Software Defined Networking: a Survey

International audienceNetwork security is a predominant topic both in academia and industry. Many methods and tools have been proposed but the attackers are still able to launch massive and effective attacks. Keeping the pace with the new threats appearing or becoming more sophisticated everyday is of a paramount of importance. Software Defined Networking (SDN) has recently emerged and promotes the programmability of the networks, which thus allows to enable in-network security functions. This includes firewalls, monitoring applications or middlebox support through OpenFlow devices. Therefore, this paper reviews the related approaches which have been proposed by identifying their scope, their practicability, their advantages and their drawbacks

Network Configuration and Flow Scheduling for Big Data Applications

International audienceThis chapter focuses on network configuration and flow scheduling for Big Data applications. It highlights how the performance of Big Data applications is tightly coupled with the performance of the network in supporting large data transfers. Deploying high-performance networks in data centers is thus vital but configuration and performance management as well as the usage of the network are of paramount importance. This chapter starts by discussing the problem of virtual machine placement and its solutions considering the underlying network topology. It then provides an analysis of alternative topologies highlighting their advantages from the perspective of Big Data applications needs. In this context, different routing and flow scheduling algorithms are discussed in terms of their potential for using the network most efficiently. In particular, Software-Defined Networking relying on centralized control and the ability to leverage global knowledge about the network state is propounded as a promising approach for efficient support of Big Data applications

Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets

Distilling Provider-Independent Data for General Detection of Non-Technical Losses

Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses

Multi-dimensional Aggregation for DNS Monitoring'

International audienceDNS is an essential service in the Internet as it allows to translate human language based domain names into IP addresses. DNS traffic reflects the user activities and behaviors. It is thus a helpful source of information in the context of large scale network monitoring. In particular, passive DNS monitoring garnered much interest for the security perspectives by highlighting the services the machines want to access. In this paper, we propose a new method for assessing the dynamics of the match between DNS names and IP subnetworks using an efficient aggregating scheme combined with relevant steadiness metrics. The evaluation relies on real data collected over several months and is able to detect anomalies related to malicious domains

A Generic Framework to Support Application-Level Flow Management in Software-Defined Networks

International audienceSoftware-Defined Networking (SDN) provides a highly flexible flow management platform through a logically centralized controller that exposes network capabilities to the applications. However, most applications do not natively use SDN. An external entity is thus responsible for defining the corresponding flow management policies. Usually network operators prefer to control the flow management policies, rather than granting full control to the applications. Although IP addresses and port numbers can suffice to identify users and applications in ISP networks and determine the policies applicable to their flows, such an assumption does not hold strongly in cloud environments. IP addresses are allocated dynamically to the users, while port numbers can be freely chosen by users or cloud-based applications. These applications, like computing or storage framework, use diverse port numbers which amplifies this phenomenon. This paper introduces higher-level abstractions for defining user- and application-specific policies. These policies are then automatically mapped to OpenFlow rules by retrieving flow-based information of active users and applications in real-time. We implemented this framework and evaluated its practicality by measuring the underlying overhead

Manifestation of the Dao A study in Daoist art from the Northern Dynasty to the Tang (5th-9th centuries)

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN030361 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

THE Port - Hackathon in Geneva

Pier43, Pier97, Pier63, Pier79, Pier87, PierX3 teams summarie