A survey of network anomaly detection techniques

TL;DR: In this article , the authors present a systematic literature review (SLR) that focuses on using anomaly detection to identify cyberattacks in smart home environments, including an adapted taxonomy that classifies existing anomaly detection methods and a critical analysis of the current state of knowledge and future research challenges.

TL;DR: In this paper , the authors develop a mathematical framework which utilizes a dynamic threshold for an anomaly classification algorithm in order to maximize the safety of a trip, and solve the resulting POMDP model using the asynchronous advantage actor critic (A3C) deep reinforcement learning algorithm.

TL;DR: It is concluded that deep learning is a good solution for medical imaging analysis, however, its benefits are yet to be realized in other domains and researchers are pursuing to explore its effectiveness to solve problems in these domains.

TL;DR: It is demonstrated how user profiles and historical records can be organised into a logical structure based on Bayesian networks to recognise the trustworthy people without the need to build trust relationships in OSNs.

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.