Heard N., Adams N., Rubin-Delanchy P., Turcotte M. (Eds.) Data Science for Cyber-Security

World Scientific, 2019. — 305 p. — (Security Science and Technology 03). — ISBN: 9781786345639.Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.
The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.
This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.Unified Host and Network Data Set
Computational Statistics and Mathematics for Cyber-Security
Bayesian Activity Modelling for Network Flow Data
Towards Generalisable Network Threat Detection
Feature Trade-Off Analysis for Reconnaissance Detection
Anomaly Detection on User-Agent Strings
Discovery of the Twitter Bursty Botnet
Stochastic Block Models as an Unsupervised Approach to Detect Botnet-Infected Clusters in Networked Data
Classification of Red Team Authentication Events in an Enterprise Network
Weakly Supervised Learning: How to Engineer Labels for Machine Learning in Cyber-Security
Large-scale Analogue Measurements and Analysis for Cyber-Security
Fraud Detection by Stacking Cost-Sensitive Decision Trees
Data-Driven Decision Making for Cyber-Security