5,895 research outputs found

    Overlay networks for smart grids

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    Review of Health Prognostics and Condition Monitoring of Electronic Components

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    To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

    Soft fault detection using MIBs in computer networks

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    To improve network reliability and management in today\u27s high-speed communication system, a statistical anomaly network intrusion detection system (NIDS) has been investigated, for network soft faults using the Management Information Base (Mm) traffic parameters provided by Simple Network Management Protocol (SNMP), for both wired and wireless networks. The work done would be a contribution to a system to be designed MIB Anomaly Intrusion Detection, a hierarchical multi-tier and multiobservation-window Anomaly Intrusion Detection system. The data was derived from many experiments that had been carried out in the test bed that monitored 27 MIB traffic parameters simultaneously, focusing on the soft network faults. The work here has been focused on early detection, i.e., detection at low values of the ratio of fault to background traffic. The performance of this system would be measured using traffic intensity scenarios, as the fault traffic decreased from 10% to 0.5% of the background

    Lateinisches und Romanisches in den Reichenauer Glossen

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    In today’s complex networks, timely identification and resolution of performance problems is extremely challenging. Current diagnostic practices to identify the root causes of such problems primarily rely on human intervention and investigation. Fully automated and scalable systems, which are capable of identifying complex problems are needed to provide rapid and accurate diagnosis. The study presented in this thesis creates the necessary scientific basis for the automatic diagnosis of network performance faults using novel intelligent inference techniques based on machine learning. We propose three new techniques for characterisation of network soft failures, and by using them, create the Intelligent Automated Network Diagnostic (IAND) system. First, we propose Transmission Control Protocol (TCP) trace characterisation techniques that use aggregated TCP statistics. Faulty network components embed unique artefacts in TCP packet streams by altering the normal protocol behaviour. Our technique captures such artefacts and generates a set of unique fault signatures. We first introduce Normalised Statistical Signatures (NSSs) with 460 features, a novel representation of network soft failures to provide the basis for diagnosis. Since not all 460 features contribute equally to the identification of a particular fault, we then introduce improved forms of NSSs called EigenNSS and FisherNSS with reduced complexity and greater class separability. Evaluations show that we can achieve dimensionality reduction of over 95% and detection accuracies up to 95% while achieving micro-second diagnosis times with these signatures. Second, given NSSs have features that are dependent on link properties, we introduce a technique called Link Adaptive Signature Estimation (LASE) using regression-based predictors to artificially generateNSSs for a large number of link parameter combinations. Using LASE, the system can be trained to suit the exact networking environment, however dynamic, with a minimal set of sample data. For extensive performance evaluation, we collected 1.2 million sample traces for 17 types of device failures on 8 TCP variants over various types of networks using a combination of fault injection and link emulation techniques. Third, to automate fault identification, we propose a modular inference technique that learns from the patterns embedded in the signatures, and create Fault Classifier Modules (FCMs). FCMs use support vector machines to uniquely identify individual faults and are designed using soft class boundaries to provide generalised fault detection capability. The use of a modular design and generic algorithm that can be trained and tuned based on the specific faults, offers scalability and is a key differentiator from the existing systems that use specific algorithms to detect each fault. Experimental evaluations show that FCMs can achieve detection accuracies of between 90% – 98%. The signatures and classifiers are used as the building blocks to create the IAND system with its two main sub-systems: IAND-k and IAND-h. The IANDk is a modular diagnostic system for automatic detection of previously known problems using FCMs. The IAND-k system is applied for accurately detecting faulty links and diagnosing problems in end-user devices in a wide range of network types (IAND-kUD, IAND-kCC). Extensive evaluation of the systems demonstrated high overall detection accuracies up to 96.6% with low false positives and over 90% accuracy even in the most difficult scenarios. Here, the FCMs use supervised machine learning methods and can only detect previously known problems. To extend the diagnostic capability to detect previously unknown problems, we propose IAND-h, a hybrid classifier system that uses a combination of unsupervised machine learning-based clustering and supervised machine learning-based classification. The evaluation of the system shows that previously unknown faults can be detected with over 92% accuracy. The IAND-h system also offers real-time detection capability with diagnosis times between 4 μs and 66 μs. The techniques and systems proposed during this research contribute to the state of the art of network diagnostics and focus on scalability, automation and modularity with evaluation results demonstrating a high degree of accuracy

    Vision systems with the human in the loop

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    The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

    GREEND: An Energy Consumption Dataset of Households in Italy and Austria

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    Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining

    AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

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    This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

    Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces

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    Embedded devices are becoming more widespread, interconnected, and web-enabled than ever. However, recent studies showed that these devices are far from being secure. Moreover, many embedded systems rely on web interfaces for user interaction or administration. Unfortunately, web security is known to be difficult, and therefore the web interfaces of embedded systems represent a considerable attack surface. In this paper, we present the first fully automated framework that applies dynamic firmware analysis techniques to achieve, in a scalable manner, automated vulnerability discovery within embedded firmware images. We apply our framework to study the security of embedded web interfaces running in Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement a scalable framework for discovery of vulnerabilities in embedded web interfaces regardless of the vendor, device, or architecture. To achieve this goal, our framework performs full system emulation to achieve the execution of firmware images in a software-only environment, i.e., without involving any physical embedded devices. Then, we analyze the web interfaces within the firmware using both static and dynamic tools. We also present some interesting case-studies, and discuss the main challenges associated with the dynamic analysis of firmware images and their web interfaces and network services. The observations we make in this paper shed light on an important aspect of embedded devices which was not previously studied at a large scale. We validate our framework by testing it on 1925 firmware images from 54 different vendors. We discover important vulnerabilities in 185 firmware images, affecting nearly a quarter of vendors in our dataset. These experimental results demonstrate the effectiveness of our approach
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