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Anomaly The Synergy

kostin11
30.06.2018

Content:

  • Anomaly The Synergy
  • SYNERGY: Detecting and Diagnosing Correlated Network Anomalies
  • SYNERGY: Detecting and Diagnosing Correlated Network Anomalies
  • In addition, SYNERGY provides a great facility for the area of anomaly detection We evaluate SYNERGY using data collected at a tier-1 ISP network and show . ate the performance of different anomaly detection methods. We evaluate SYNERGY using data collected at a tier-1 ISP network and show that it performs very. In this paper, we design and prototype a novel system, SYNERGY, that can detect network anomalies with high confidence by correlating across multiple data.

    Anomaly The Synergy

    Do you want to read the rest of this article? We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising. For further information, including about cookie settings, please read our Cookie Policy.

    By continuing to use this site, you consent to the use of cookies. Network anomalies occur in operational networks and may be logged by a number of network measurement tools such as SNMP and NetFlow. However, accurate and efficient de-tection of these anomalies in the logged data is very chal-lenging due to the huge data volume and complex charac-teristics of anomalies.

    The existing approaches are limited by the nature of underlying mathematical models and might be incapable of capturing some abnormal patterns. More importantly, existing approaches do not provide insights on the root causes or impact of the detected anomalies, which makes it hard for a network operator to troubleshoot net-work performance issues. In this paper, we design and pro-totype a novel system, SYNERGY, that can detect network anomalies with high confidence by correlating across multi-ple data sources.

    In addition, SYNERGY provides a great facility for the area of anomaly detection research — it can serve as a general framework to evalu-ate the performance of different anomaly detection methods. For IP networks, a number of approaches was suggested that correlate events with the same dimensions to confirm an attack.

    They can process flows in separate time windows [6, 7], detect unexpected changes in time series [10, 21], or measure co-occurrence of events of the same dimensions across multiple data sources. This observation is in accord with observation of other re- searchers [5, 14, 21]. Jun Lect Notes Comput Sci. The rapid development of network technologies entails an increase in traffic volume and attack count.

    The associated increase in computational complexity for methods of deep packet inspection has driven the development of behavioral detection methods. These methods distinguish attackers from valid users by measuring how closely their behavior resembles known anomalous behavior. In real-life deployment, an attacker is flagged only on very close resemblance to avoid false positives. However, many attacks can then go undetected. We believe that this problem can be solved by using more detection methods and then correlating their results.

    These methods can be set to higher sensitivity, and false positives are then reduced by accepting only attacks reported from more sources. To this end we propose a novel sketch-based method that can detect attackers using a correlation of particular anomaly detections.

    This is in contrast with the current use of sketch-based methods that focuses on the detection of heavy hitters and heavy changes. We illustrate the potential of our method by detecting attacks on RDP and SSH authentication by correlating four methods detecting the following anomalies: Similarity as a central approach to flow-based anomaly detection. For further information, including about cookie settings, please read our Cookie Policy. By continuing to use this site, you consent to the use of cookies.

    This paper presents two complementing algorithms for remote sensing based coal fire research and the results derived thereof.

    The first algorithm automatically delineates coal fire risk areas from multispectral satellite data. The second automatically extracts local coal fire related thermal anomalies from thermal data. The presented methods aim at the automated, unbiased retrieval of coal fire related information.

    The delineation of coal fire risk areas is based on land cover extraction through a knowledge based spectral test sequence.

    This sequence has been proven to extract coal fire risk areas not only in time series of the investigated study areas in China, but also in transfer regions of India and Australia. The algorithm for the extraction of thermal anomalies is based on a moving window approach analysing sub-window histograms. It allows the extraction of thermally anomalous pixels with regard to their surrounding background and therefore supports the extraction of very subtle, local thermal anomalies of different temperature.

    It thus shows clear advantages to anomaly extraction via simple thresholding techniques. Since the thermal algorithm also does extract thermal anomalies, which are not related to coal fires, the derived risk areas can help to eliminate false alarms.

    However, detection rates are very good. This is the first time in coal fire research that unknown coal fires were detected in satellite remote sensing data exclusively and were validated later subsequently during in situ field checks. Figures - uploaded by Claudia Kuenzer. Author content All content in this area was uploaded by Claudia Kuenzer. Coal fire risk area delineation supporting the exclusion of false alarms. Content uploaded by Claudia Kuenzer. Coal fires in the underground as well as in surface mines, not only damage a considerable amount of resources but the entire mine environment is badly affected.

    Therefore, over the course of the last decade, many research studies on coal fires have been conducted Litschke et al.

    Delineation and mapping of coal mine fire using remote sensing data — a review. Various countries around the globe face numerous hazards due to the burning of coal on the surface as well as below ground. Countries like China, India, United States of America USA , Australia, Indonesia, and many other countries have reported the burning of coal fires, and thus it is the urgent need to control the coal fire propagation to prevent the loss of energy resources.

    Coal is a fossil fuel that has a tendency to catch fire for many reasons; spontaneous combustion being the most frequent reasons for its burning. Other factors leading to coal fire include forest fires close to coal seams, natural hazards, old mining techniques, and external heat sources. The review work demonstrates the application of various satellite data in fire detection and mapping.

    The literature reveals that remote sensing plays an important role in facilitating quick and complete delineation of coal mine fires. Many algorithms have been developed around the world for fire detection from different satellite data. A comprehensive demonstration of different algorithms along with their merits and demerits are outlined. Comparative performances of the different algorithms with their case studies are also explained. It can be inferred from the various literature that it is very difficult to select a particular sensor algorithm for generating global fire products.

    Suggestions are given to further explore the possibility of improvement of fire detection algorithms. All known occurrences of high-rank coal around the world are invariably associated with the problem of 'coal fires,' particularly in China, USA, Australia, Indonesia and India Cracknell and Mansor ;Mansor et al. Temporal monitoring of coal fires in Jharia Coalfield, India. A body of literature has shown that it is feasible to conduct the assessment of LST or GHF from high spatial resolution satellite data [29][30][31][35][36] [37] [38][39][40][41][42][43].

    However, there is no previous study of geothermal exploration using satellite-based infrared data in Taiwan. Therefore, it is need to conduct the assessment of LST or geothermal heat flux GHF from high spatial resolution, such as Landsat data e.

    Se sabe que los incendios de carb? Spontaneous combustion is a subject of great concern, causing mainly environmental problems by generating emissions of polluting gases, losses of reserves, problems of geotechnical instability and health problems. The propagation of fires in highwall and footwall is caused by the progressive unleashing of chemical reactions, coupled with an intense release of heat in the reaction front and can be studied taking into account the thermodynamics and chemical kinetics, which are due to the conservation equation of energy and chemical species, respectively.

    Technical improvements were proposed in the methods of removal, suffocation and the PROPEX proposal as an alternative method of innovative extinction worldwide. Results of the master's thesis in mineral resources.

    Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field.

    Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery.

    This study aims to apply and integrate thermal infrared TIR remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations.

    The correlation coefficient of linear regression between air temperature and LST retrieval is 0. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings.

    It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

    SYNERGY: Detecting and Diagnosing Correlated Network Anomalies

    A Community-Based Cooperative Anomaly Detection System by the Synergy of Mobile Sensing and Delay Tolerant Networks. Conference. We show that (a) the magnetocaloric effect exhibits an unexpected anomaly at the ferroelectric transition occurring at a high temperature, even. The synergy between anomaly detectors permits to detect twice as many anomalies Significant anomalous traffic features are extracted from reported alarms.

    SYNERGY: Detecting and Diagnosing Correlated Network Anomalies



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