Data Science For Cyber-security

by Nicholas A Heard, Niall M Adams, Patrick Rubin-delanchy, Mellisa Turcotte

Overview

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.

Product Details

ISBN-13:	9781786345639
Publisher:	World Scientific Publishing Europe Ltd
Publication date:	11/20/2018
Series:	Security Science And Technology , #3
Pages:	304
Product dimensions:	6.00(w) x 9.00(h) x 0.75(d)

Table of Contents

Preface v

1 Unified Host and Network Data Set Melissa J. M. Turcotte Alexander D. Kent Curtis Hash 1

2 Computational Statistics and Mathematics for Cyber-Security David J. Marchette 23

3 Bayesian Activity Modelling for Network Flow Data Henry Clausen Mark Briers Niall M. Adams 55

4 Towards Generalisable Network Threat Detection Blake Anderson Martin Vejman David McGrew Subharthi Paul 77

5 Feature Trade-Off Analysis for Reconnaissance Detection Harsha Kumara Kalutarage Siraj Ahmed Shaikh 95

6 Anomaly Detection on User-Agent Strings Eirini Spyropoulou Jordan Noble Christoforos Anagnostopoulos 127

7 Discovery of the Twitter Bursty Botnet Juan Echeverria Christoph Besel Shi Zhou 145

8 Stochastic Block Models as an Unsupervised Approach to Detect Botnet-Infected Clusters in Networked Data Mark Patrick Roeling Geoff Nicholls 161

9 Classification of Red Team Authentication Events in an Enterprise Network John M. Conroy 179

10 Weakly Supervised Learning: How to Engineer Labels for Machine Learning in Cyber-Security Christoforos Anagnostopoulos 195

11 Large-scale Analogue Measurements and Analysis for Cyber-Security George Cybenko Gil M. Raz 227

12 Fraud Detection by Stacking Cost-Sensitive Decision Trees Alejandro Correa Bahnsen Sergio Villegas Djamila Aouada Björn Ottersten 251

13 Data-Driven Decision Making for Cyber-Security Mike Fisk 267

Index 293