A survey of network anomaly detection techniques

TL;DR: This paper compared and evaluated two intrusion detection and prevention systems named Suricata and Snort to equate, among the two security systems the one suitable to secure mobile voting application called XaP, while casting a vote and results indicated thatSuricata is more effective, reliable, accurate and secure than Snort when comes to protecting X aP.

TL;DR: The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour and is more effective than traditional tactics at ensuring high levels of privacy.

TL;DR: In this paper, the Neyman-Pearson (NP) classification framework is suitable for binary classification problems under asymmetric error costs, since it minimizes type II error and keeps type I error below a user-specified threshold.

TL;DR: A novel semi-supervised anomaly detection approach based on deep generative models with Transformers for identifying unusual (abnormal) images from normal ones based on the combination of autoencoder and generative adversarial networks.

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.