A survey of network anomaly detection techniques

TL;DR: A detailed background about mobile botnets, including their lifecycle, architecture, and C&C channel is presented, which briefly overviews mobile botnets' evolution and compares PC and mobile botnets using different criteria.

TL;DR: Using real-world firewall log data from an enterprise-level organization, the end-to-end evaluation shows the effective detection and interpretation of log anomalies via the proposed process, many of which would have otherwise been missed by traditional means.

TL;DR: In this paper, the authors propose a method that uses exponential random graph modeling to integrate network topology structure statistics in anomaly detection, and demonstrate the effectiveness of their method using real-world examples as a baseline for experiments on domain name system (DNS) data exfiltration scenarios.

TL;DR: This paper proposes an approach to automatic detection of attacks on computer networks using data that combine the traffic generated with ’live’ intra-cloud virtual-machine (VM) migration using the recently introduced typicality and eccentricity data analytics (TEDA) framework.

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.