<p>Information security concerns the confidentiality, integrity, and availability of information processed by a computer system. With an emphasis on prevention, traditional information security research has focused little on the ability to survive successful attacks, which can seriously impair the integrity and availability of a system. <br> Trusted Recovery And Defensive Information Warfare uses database trusted recovery, as an example, to illustrate the principles of trusted recovery in defensive information warfare. Traditional database recovery mechanisms do not address trusted recovery, except for complete rollbacks, which undo the work of benign transactions as well as malicious ones, and compensating transactions, whose utility depends on application semantics. Database trusted recovery faces a set of unique challenges. In particular, trusted database recovery is complicated mainly by (a) the presence of benign transactions that depend, directly or indirectly on malicious transactions; and (b) the requirement by many mission-critical database applications that trusted recovery should be done on-the-fly without blocking the execution of new user transactions. <br> Trusted Recovery And Defensive Information Warfare proposes a new model and a set of innovative algorithms for database trusted recovery. Both read-write dependency based and semantics based trusted recovery algorithms are proposed. Both static and dynamic database trusted recovery algorithms are proposed. These algorithms can typically save a lot of work by innocent users and can satisfy a variety of attack recovery requirements of real world database applications. <br> Trusted Recovery And Defensive Information Warfare is suitable as a secondary text for a graduate level course in computer science, and as a reference for researchers and practitioners in information security.</p>

Trusted Recovery and Defensive Information Warfare (Advances in Information Security, 4)

Jajodia, Sushil

Peng, Liu

Reference

Springer

<p><b>Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inference</b> <h4>Key Features</h4> <ul> <li>Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examples</li> <li>Gain practical insights into the real-world applications of statistics and machine learning</li> <li>Explore the technicalities of statistics and machine learning for effective data presentation</li> <li>Purchase of the print or Kindle book includes a free PDF eBook</li> </ul> <h4>Book Description</h4> The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts.Starting with the fundamentals, you'll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you'll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You'll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career.By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You'll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects. <h4>What you will learn</h4> <ul> <li>Hone your skills in different probability distributions and hypothesis testing</li> <li>Explore the fundamentals of linear algebra and calculus</li> <li>Master crucial statistics and machine learning concepts in theory and practice</li> <li>Discover essential data processing and visualization techniques</li> <li>Engage in interactive data analysis using R</li> <li>Use R to perform statistical modeling, including Bayesian and linear regression</li> </ul> <h4>Who this book is for</h4> <p>This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.</p> ]]></p>

The Statistics and Machine Learning with R Workshop: Unlock the power of efficient data science modeling with this hands-on guide

Computers & Technology

Databases & Big Data

Packt Publishing

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