This data-oriented approach to studying cyber threats shows 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.
This data-oriented approach to studying cyber threats shows 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.
Vandana Janeja is Professor and Chair of the Information Systems department at the University of Maryland, Baltimore County. Most recently, she also served as an expert at the National Science Foundation supporting data science activities in the Directorate for Computer and Information Science and Engineering (CISE) (2018-2021). Her research interests include discovering knowledge in presence of data heterogeneity. Her research projects include anomaly detection in network communication data, human behavior analytics in heterogeneous device environments, geo spatial context for IP reputation scoring, spatio-temporal analysis across heterogeneous data, ethical thinking in data science. She has been funded through state, federal and private organizations.
Inhaltsangabe
Preface 1. Introduction 2. Understanding sources of cybersecurity data 3. Introduction to data mining: clustering, classification and association rule mining 4. Big data analytics and its need for cybersecurity: advanced DM and complex data types from cybersecurity perspective 5. Types of Cyber Attacks 6. Anomaly Detection for cyber security 7. Anomaly Detection 8. Cybersecurity through Time Series and Spatial data 9. Cybersecurity through Network and Graph Data 10. Human Centered Data Analytics for Cyber security 11. Future directions in Data Analytics for Cybersecurity References Index
Preface 1. Introduction 2. Understanding sources of cybersecurity data 3. Introduction to data mining: clustering, classification and association rule mining 4. Big data analytics and its need for cybersecurity: advanced DM and complex data types from cybersecurity perspective 5. Types of Cyber Attacks 6. Anomaly Detection for cyber security 7. Anomaly Detection 8. Cybersecurity through Time Series and Spatial data 9. Cybersecurity through Network and Graph Data 10. Human Centered Data Analytics for Cyber security 11. Future directions in Data Analytics for Cybersecurity References Index
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