A survey of network anomaly detection techniques

TL;DR: A novel Random Neural Network based Intrusion Detection System (RNN-IDS) is proposed and its performance was evaluated by the detection of novel attacks while obtaining an accuracy of 94.50%.

TL;DR: The experimental results show that SSAD outperforms the Naive Bayes methods in terms of Detection Rate and False Positive Rate.

TL;DR: A one-class collective anomaly detection model based on neural network learning that is capable to detect collective anomaly efficiently and evaluated on a time series version of the KDD 1999 dataset is proposed.

TL;DR: The proposed anomaly detection approach is validated by experiments on synthetic and real social network datasets and outperforms other related approaches in terms of some statistical performance measures, especially applied to binary normal-abnormal classification test.

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.