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Data science for cyber-security

By: Heard, Nick.
Contributor(s): Rubin-Delanchy, Patrick | Adams,Niall | Turcotte, Melissa.
Material type: materialTypeLabelBookPublisher: New Jersey : World Scientific, 2018Description: x, 294 p. : ill. ; 24 cm.ISBN: 9781786345639.Subject(s): Data protection | Internet | Security measures | Data processing | Statistical methodsDDC classification: 005.8 Summary: 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
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Books 005.8 HEA (Browse shelf) Available 031866

Includes bibliographical references and index.

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

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