The Cloud Security Ecosystem: Technical, Legal, Business and Management Issues

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Drawing upon the expertise of world-renowned researchers and experts, The Cloud Security Ecosystem comprehensively discusses a range of cloud security topics from multi-disciplinary and international perspectives, aligning technical security implementations with the most recent developments in business, legal, and international environments. The book holistically discusses key research and policy advances in cloud security – putting technical and management issues together with an in-depth treaties on a multi-disciplinary and international subject. The book features contributions from key thought leaders and top researchers in the technical, legal, and business and management aspects of cloud security. The authors present the leading edge of cloud security research, covering the relationships between differing disciplines and discussing implementation and legal challenges in planning, executing, and using cloud security.Presents the most current and leading-edge research on cloud security from a multi-disciplinary standpoint, featuring a panel of top experts in the fieldFocuses on the technical, legal, and business management issues involved in implementing effective cloud security, including case examplesCovers key technical topics, including cloud trust protocols, cryptographic deployment and key management, mobile devices and BYOD security management, auditability and accountability, emergency and incident response, as well as cloud forensicsIncludes coverage of management and legal issues such as cloud data governance, mitigation and liability of international cloud deployment, legal boundaries, risk management, cloud information security management plans, economics of cloud security, and standardization efforts

Mining the Web: Discovering Knowledge from Hypertext Data (The Morgan Kaufmann Series in Data Management Systems)

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Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing-Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's work-painstaking, critical, and forward-looking-readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.

* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.
* Details the special challenges associated with analyzing unstructured and semi-structured data.
* Looks at how classical Information Retrieval techniques have been modified for use with Web data.
* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.
* Analyzes current applications for resource discovery and social network analysis.
* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.