Data Science for Cyber-Security
Nonfiction, Computers, Networking & Communications, Computer Security, Operating Systems, Application Software
| Author: | Nick Heard, Niall Adams, Patrick Rubin-Delanchy, Melissa Turcotte | ISBN: | 9781786345653 |
| Publisher: | World Scientific Publishing Company | Publication: | September 25, 2018 |
| Imprint: | WSPC (EUROPE) | Language: | English |
| Author: | Nick Heard, Niall Adams, Patrick Rubin-Delanchy, Melissa Turcotte |
| ISBN: | 9781786345653 |
| Publisher: | World Scientific Publishing Company |
| Publication: | September 25, 2018 |
| Imprint: | WSPC (EUROPE) |
| Language: | English |
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.
Contents:
- Unified Host and Network Data Set (Melissa J M Turcotte, Alexander D Kent and Curtis Hash)
- Computational Statistics and Mathematics for Cyber-Security (David J Marchette)
- Bayesian Activity Modelling for Network Flow Data (Henry Clausen, Mark Briers and Niall M Adams)
- Towards Generalisable Network Threat Detection (Blake Anderson, Martin Vejman, David McGrew and Subharthi Paul)
- Feature Trade-Off Analysis for Reconnaissance Detection (Harsha Kumara Kalutarage and Siraj Ahmed Shaikh)
- Anomaly Detection on User-Agent Strings (Eirini Spyropoulou, Jordan Noble and Christoforos Anagnostopoulos)
- Discovery of the Twitter Bursty Botnet (Juan Echeverria, Christoph Besel and Shi Zhou)
- Stochastic Block Models as an Unsupervised Approach to Detect Botnet-Infected Clusters in Networked Data (Mark Patrick Roeling and Geoff Nicholls)
- Classiffication of Red Team Authentication Events in an Enterprise Network (John M Conroy)
- Weakly Supervised Learning: How to Engineer Labels for Machine Learning in Cyber-Security (Christoforos Anagnostopoulos)
- Large-scale Analogue Measurements and Analysis for Cyber-Security (George Cybenko and Gil M Raz)
- Fraud Detection by Stacking Cost-Sensitive Decision Trees (Alejandro Correa Bahnsen, Sergio Villegas, Djamila Aouada and Björn Ottersten)
- Data-Driven Decision Making for Cyber-Security (Mike Fisk)
Readership: Researchers at all levels in cyber-security and data science.
Key Features:
- A collection of papers introducing novel methodology for cyber-data analysis
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.
Contents:
- Unified Host and Network Data Set (Melissa J M Turcotte, Alexander D Kent and Curtis Hash)
- Computational Statistics and Mathematics for Cyber-Security (David J Marchette)
- Bayesian Activity Modelling for Network Flow Data (Henry Clausen, Mark Briers and Niall M Adams)
- Towards Generalisable Network Threat Detection (Blake Anderson, Martin Vejman, David McGrew and Subharthi Paul)
- Feature Trade-Off Analysis for Reconnaissance Detection (Harsha Kumara Kalutarage and Siraj Ahmed Shaikh)
- Anomaly Detection on User-Agent Strings (Eirini Spyropoulou, Jordan Noble and Christoforos Anagnostopoulos)
- Discovery of the Twitter Bursty Botnet (Juan Echeverria, Christoph Besel and Shi Zhou)
- Stochastic Block Models as an Unsupervised Approach to Detect Botnet-Infected Clusters in Networked Data (Mark Patrick Roeling and Geoff Nicholls)
- Classiffication of Red Team Authentication Events in an Enterprise Network (John M Conroy)
- Weakly Supervised Learning: How to Engineer Labels for Machine Learning in Cyber-Security (Christoforos Anagnostopoulos)
- Large-scale Analogue Measurements and Analysis for Cyber-Security (George Cybenko and Gil M Raz)
- Fraud Detection by Stacking Cost-Sensitive Decision Trees (Alejandro Correa Bahnsen, Sergio Villegas, Djamila Aouada and Björn Ottersten)
- Data-Driven Decision Making for Cyber-Security (Mike Fisk)
Readership: Researchers at all levels in cyber-security and data science.
Key Features:
- A collection of papers introducing novel methodology for cyber-data analysis
