Big Data Analytics with Applications in Insider Threat DetectionКНИГИ » ОС И БД
Название: Big Data Analytics with Applications in Insider Threat Detection Автор: Bhavani Thuraisingham, Pallabi Parveen Издательство: Auerbach Publications ISBN: 1498705472 Год: 2017 Страниц: 579 Язык: английский Формат: True PDF Размер: 74.9 MB
Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
The significant developments in data management and analytics, web services, cloud computing, and cyber security have evolved into an area called big data management and analytics (BDMA) as well as big data security and privacy (BDSP). This book will review the developments in topics both BDMA and BDSP and discuss the issues and challenges in securing big data as well as applying big data techniques to solve problems. We will focus on a specific big data analytics technique called stream data mining as well as approaches to applying this technique to insider threat detection.
This book is divided into five parts, each describing some aspect of the technology that is relevant to BDMA and BSDP. The major focus of this book will be on stream data analytics and its applications in insider threat detection. In addition, we will also discuss some of the experimental systems we have developed and provide some of the challenges involved.
Part I, consisting of six chapters, will describe supporting technologies for BDMA and BDSP including data security and privacy, data mining, cloud computing and semantic web. Part II, consisting of six chapters, provides a detailed overview of the techniques we have developed for stream data analytics. In particular, we will describe our techniques on novel class detection for data streams. Part III, consisting of nine chapters, will discuss the applications of stream analytics for insider threat detection. Part IV, consisting of six chapters, will discuss some of the experimental systems we have developed based on BDMA and BDSP. These include secure query processing for big data as well as social media analysis. Part V, consisting of seven chapters, discusses some of the challenges for BDMA and BDSP. In particular, securing the Internet of Things as well as our plans for developing experimental infrastructures for BDMA and BDSP are also discussed.
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