Network Security Through Data Analysis: From Data to Action, 2nd Edition by Michael Collins English | October 2nd, 2017 | ISBN: 1491962844 | 427 Pages | True PDF | 10.06 MB
Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to harden and defend the systems within it.
Database Systems: A Practical Approach to Design, Implementation and Management by Thomas M. Connolly, Carolyn E. Begg Language: English | 2012 | ISBN: 0321210255 | 1236 pages | PDF | 51,6 MB
This book places a strong emphasis on good design practice, allowing readers to master design methodology in an accessible, step-by-step fashion. In this book, database design methodology is explicitly divided into three phases: conceptual, logical, and physical.
Heritage Preservation: A Computational Approach by Bhabatosh Chanda English | PDF,EPUB | 2018 | 349 Pages | ISBN : 9811072205 | 38.18 MB
This book presents a unique guide to heritage preservation problems and the corresponding state-of-the-art digital techniques to achieve their plausible solutions. It covers various methods, ranging from data acquisition and digital imaging to computational methods for reconstructing the original (pre-damaged) appearance of heritage artefacts.The case studies presented here are mostly drawn from India’s tangible and non-tangible heritage, which is very rich and multi-dimensional.
Big Data Processing Using Spark in Cloud by Mamta Mittal English | PDF,EPUB | 2018 (2019 Edition) | 274 Pages | ISBN : 9811305498 | 12.88 MB
The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference.