The evaluation of datasets serves as a fundamental basis for tasks in evaluatology. Evaluating the usage patterns of datasets has a significant impact on the selection of appropriate datasets. Many renowned open Sourc...
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This paper presents the implementation of an anomaly-based Intrusion Detection System (IDS), capable to detect well-known and zero-day attacks. First, we extend our previous work by generating the Machine Learning (ML...
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This paper presents the implementation of an anomaly-based Intrusion Detection System (IDS), capable to detect well-known and zero-day attacks. First, we extend our previous work by generating the Machine Learning (ML) predictors based on KDD99, NSL-KDD and CIC-IDS2018 datasets, and providing the programming language evaluation and the final validation platform. We have built IDS detection solution in two phases. The first Training phase explores available datasets to generate the predictors. The second phase is composed of two processes. Extraction generates the statistical network traffic metrics from the PCAP files and processes them into commma separated values (CSV) files. The Prediction loads predictors in main memory and feeds them with CSV files to predict the well-known and zero-day attacks. The aforementioned initial datasets contain the statistical network traffic metrics of the well-known attacks, collected at runtime execution of the malicious software. Zero day attacks can generate a statistical network traffic metrics similar to well-known attacks. Therefore, to showcase the zero-day anomaly detection, we realise a validation platform. Six attacks (three Denial of Service (DoS) and three scanning), not recorded in the initial datasets, are executed in an isolated environment. The achieved result indicates a misclassification prediction error that inhibits the application of the automatic attack responses, although the misclassification errors were minimised, during the Training phase.
In this paper we introduce a novel platform for teams to develop rich, analysis-ready datasets for geospatial machine learning. Europa(1) addresses longstanding challenges that remote sensing and machine vision resear...
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ISBN:
(数字)9781665427920
ISBN:
(纸本)9781665427920
In this paper we introduce a novel platform for teams to develop rich, analysis-ready datasets for geospatial machine learning. Europa(1) addresses longstanding challenges that remote sensing and machine vision researchers face when developing datasets, including data sourcing, dataset development and sharing. By simplifying and accelerating the dataset creation process, Europa serves to expedite the pace of geospatial machine learning innovation. The platform enables users to develop feature-rich, spatio-temporal datasets using multiple sources of satellite imagery. Europa supports the development of datasets for segmentation, classification, object detection, and change detection problems. Europa also enables collaborative dataset development, with a management protocol for crowdsourcing labels and annotations. The web interface and API are built upon a resilient dataset management protocol that supports versioning, forking and access control, enabling greater research collaboration.
To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on ...
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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.
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