A survey of network anomaly detection techniques

TL;DR: A taxonomy of the ML-based DDoS detection schemes, focusing on the important features and mechanisms that each scheme uses to detect and mitigate the impact of these attacks, and shows that the class imbalance problem significantly impact performance.

TL;DR: A hybrid feature selection algorithm is proposed to process the two classification unbalanced data problem and multi classification problem and its results show that the area under receiver operating characteristic curve for two classifications and the accuracy rate forMulti classification problem have been improved compared with other models.

TL;DR: A defense algorithm for NIDS against adversarial samples is designed by using bidirectional generative adversarial network, which improves the robustness and accuracy of NIDS in the adversarial environment.

TL;DR: The paper aims at summarizing recent research efforts on some of the attacks and the various techniques employed in tackling them as discussed in recent literatures and research works to explain current and future directions NAN communication security could consider in terms of data privacy measures.

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.