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Data analytics for cybersecurity
\"As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing 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\"-- Provided by publisher.
Book
Handbook of statistical analysis and data mining applications
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation.
eBook
The history of computing : a very short introduction
\"This lively Very Short Introduction reviews the central events, machines, and people that feature in established accounts of the history of computing, critically examining received perceptions and providing a fresh look at the nature and development of the modern electronic computer.\" -- Amazon.com description.
Book
Data Clustering
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process-including how to verify the quality of the underlying clusters-through supervision, human intervention, or the automated generation of alternative clusters.
eBook
Data mining : concepts and techniques
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.
eBook
Designing secure systems
Modern systems are an intertwined mesh of human process, physical security, and technology. Attackers are aware of this, commonly leveraging a weakness in one form of security to gain control over an otherwise protected operation. To expose these weaknesses, we need a single unified model that can be used to describe all aspects of the system on equal terms. Designing Secure Systems takes a theory-based approach to concepts underlying all forms of systems - from padlocks, to phishing, to enterprise software architecture. We discuss how weakness in one part of a system creates vulnerability in another, all the while applying standards and frameworks used in the cybersecurity world. Our goal: to analyze the security of the entire system - including people, processes, and technology -using a single model. We begin by describing the core concepts of access, authorization, authentication, and exploitation. We then break authorization down into five interrelated components and describe how these aspects apply to physical, human process, and cybersecurity. Lastly, we discuss how to operate a secure system based on the NIST Cybersecurity Framework (CSF) concepts of \"identify, protect, detect, respond, and recover.\" Other topics covered in this book include the NIST National Vulnerability Database (NVD), MITRE Common Vulnerability Scoring System (CVSS), Microsoft's Security Development Lifecycle (SDL), and the MITRE ATT&CK Framework.
Book
The art of memory forensics
Memory forensics provides cutting edge technology to help investigate digital attacks Memory forensics is the art of analyzing computer memory (RAM) to solve digital crimes. As a follow-up to the best seller Malware Analyst's Cookbook, experts in the fields of malware, security, and digital forensics bring you a step-by-step guide to memory forensics—now the most sought after skill in the digital forensics and incident response fields.
eBook
Embedded Systems Architecture - A Comprehensive Guide for Engineers and Programmers
This comprehensive textbook provides a broad and in-depth overview of embedded systems architecture for engineering students and embedded systems professionals. The book is well suited for undergraduate embedded systems courses in electronics/electrical engineering and engineering technology (EET) departments in universities and colleges, as well as for corporate training of employees. The book is a readable and practical guide covering embedded hardware, firmware, and applications. It clarifies all concepts with references to current embedded technology as it exists in the industry today, including many diagrams and applicable computer code. Among the topics covered in detail are hardware components, including processors, memory, buses, and I/O; system software, including device drivers and operating systems; use of assembly language and high-level languages such as C and Java; interfacing and networking; case studies of real-world embedded designs; and applicable standards grouped by system application.
eBook