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Data analytics for cybersecurity

Janeja, Vandana P.

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Data analytics for cybersecurity

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data analytics for cybersecurity/ Vandana P. Janeja.
作者:	Janeja, Vandana P.
出版者:	Cambridge ; New York, NY :Cambridge University Press, : 2022.,
面頁冊數:	xiii, 192 p. :ill., digital ;23 cm.
附註:	Title from publisher's bibliographic system (viewed on 10 Aug 2022).
內容註:	Introduction - data analytics for cybersecurity -- Understanding sources of cybersecurity data -- Introduction to data mining : clustering, classification and association rule mining -- Big data analytics and its need for cybersecurity -- Types of cyber attacks -- Anomaly detection for cyber security -- Anomaly detection -- Cybersecurity through time series and spatial data -- Cybersecurity through network and graph data -- Human centered data analytics for cyber security -- Future directions in data analytics for cybersecurity.
標題:	Computer security - Data processing. -
電子資源:	https://doi.org/10.1017/9781108231954
ISBN:	9781108231954

Data analytics for cybersecurity
Janeja, Vandana P.

Data analytics for cybersecurity[electronic resource] /Vandana P. Janeja. - Cambridge ; New York, NY :Cambridge University Press,2022. - xiii, 192 p. :ill., digital ;23 cm.

Title from publisher's bibliographic system (viewed on 10 Aug 2022).

Introduction - data analytics for cybersecurity -- Understanding sources of cybersecurity data -- Introduction to data mining : clustering, classification and association rule mining -- Big data analytics and its need for cybersecurity -- Types of cyber attacks -- Anomaly detection for cyber security -- Anomaly detection -- Cybersecurity through time series and spatial data -- Cybersecurity through network and graph data -- Human centered data analytics for cyber security -- Future directions in data analytics for cybersecurity.

As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.

ISBN: 9781108231954Subjects--Topical Terms:

3395890
Computer security
--Data processing.

LC Class. No.: QA76.9.A25 / J37 2022

Dewey Class. No.: 005.8

Data analytics for cybersecurity
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505 0 $a Introduction - data analytics for cybersecurity -- Understanding sources of cybersecurity data -- Introduction to data mining : clustering, classification and association rule mining -- Big data analytics and its need for cybersecurity -- Types of cyber attacks -- Anomaly detection for cyber security -- Anomaly detection -- Cybersecurity through time series and spatial data -- Cybersecurity through network and graph data -- Human centered data analytics for cyber security -- Future directions in data analytics for cybersecurity.
520 $a As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.
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