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In this paper, we introduce a new visualization tool for network-wide intrusion detection. It is based in multivariate anomaly detection with a combination between Principal Component Analysis (PCA) and a new variant called Group-wise PCA (GPCA). Combining these methodologies with the capabilities of interactive visualization, the resulting tool is a highly flexible and intuitive interface that allows...
The research literature on cybersecurity incident response is very rich in automatic intrusion detection methodologies. The most accepted approach to compare the detection performance of the methods is by using a real traffic data set where normal traffic and anomalies are conveniently combined and labeled. In this paper, we follow this approach in a real network where a number of controlled attacks...
Process Control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa...
In this paper, a framework for anomaly detection and forensics in Big Data is introduced. The framework tackles the Big Data 4 Vs: Variety, Veracity, Volume and Velocity. The varied nature of the data sources is treated by transforming the typically unstructured data into a highly dimensional and structured data set. To overcome both the uncertainty (low veracity) and high dimension introduced, a...
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