A survey of network anomaly detection techniques

TL;DR: In this paper , the authors proposed new state determination standards that use the normal distribution characteristics of the entropy value at the time which an attack did not occur, subdivide the normal and abnormal range represented by the entropy values, improving the accuracy of attack determination.

TL;DR: A deep model DOIForest is designed with two mutation schemes and solution selection, which learns the optimal isolation forest and optimises the parameters in data partitioning and can achieve better detection accuracy and robustness than the state-of-the-arts.

TL;DR: In this article , a Flow-based Self-Active Intrusion Detection System (FSA-IDS) is proposed to reduce the labeling cost and realize an effective IDS, which employs a novel cluster-based sampling approach that facilitates the labeling automation process and minimizes expert involvement.

TL;DR: Explainable Contextual Anomaly Detection using Quantile Regression Forests TLDR: A novel approach to inherently interpretable contextual anomaly detection that uses quantile regression forests to model dependencies between features outperforms state-of-the-art anomaly detection methods in identifying contextual 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.