A survey of network anomaly detection techniques

TL;DR: Outlier detection is proposed as a solution to have safe SC, powered by WSN using the concepts of Internet of Things and Big Data.

TL;DR: In this article , the combination of feature selection techniques which including Pearson correlation (PC), Information Gain (IG), ExtraTreeClassifier (ET), and Chi-square tests are used to rank the features using the weighted average.

TL;DR: This paper design and implement a new anomaly detection platform - NetDetector for networked systems based on LSTM, a special kind of recurrent neural network, which achieves about 8% performance improvement as compared with systems that use traditional machine learning methods.

TL;DR: This paper attempts to present and discuss the previous works proposed to detect the anomalies on the OSN, and discusses the previous approaches which used various data mining approaches.

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.