A survey of network anomaly detection techniques

TL;DR: In this article , a comparison between different one-class techniques, such as statistical models, geometric boundaries, or dimensional reduction to face anomaly detection in specific subsystems of a warship, with the prospect of applying it to the whole ship.

TL;DR: DSAE put more shrinkage on the normal data in the middle hidden layer of AutoEncoder to pull out some anomalies that are very similar to normal data, and often out-performs this model on the attacks group that is difficult for the previous methods.

TL;DR: The proposed MOoM model, stands for Mining Outlier Opinion Model, offers for the first time the ability to mine outlier opinions from product’s free-text reviews and can help the decision makers to improve the overall sentiment analysis process and perform further analysis on the outlier views to get better understanding for them and avoid their negative impact.

TL;DR: An anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns and can not only accurately detect and identify abnormal behaviors but also trace the source of anomalies.

TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.

TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.

TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.