Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

This book constitutes the refereed proceedings of the International Workshop on Intelligence and Security Informatics, WISI 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining. The 32 papers presented together with the abstract of the keynote talk were carefully reviewed. The papers are organized in sections on Web and text mining for terrorism informatics, cybercrime analysis, network security, and crime data mining.

This book is a collection of peer-reviewed best selected research papers presented at the First International Conference on Machine Intelligence and Smart Systems 2020 (MISS 2020), organized during September 24–25, 2020, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation.

Presents theories and models associated with information privacy and safeguard practices to help anchor and guide the development of technologies, standards, and best practices. Provides recent, comprehensive coverage of all issues related to information security and ethics, as well as the opportunities, future challenges, and emerging trends related to this subject.

Applying Computational Intelligence for Social Good: Track, Understand and Build a Better World, Volume 132 presents views on how Computational Intelligent and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental issues, mobility of the disabled, and problems in social safety. Sample chapters in this release include Why is implementing Computational Intelligence for social good so challenging? Principles and its Application, Smart crisis management system for road accidents using Geo-Spacial Machine Learning Techniques, Residential Energy Management System (REMS) Using Machine Learning, Text-Based Personality Prediction using XLNet, and much more. - Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation for solving socially relevant problems - Focuses on Forecasting applications, Human Behavior and Critics response analysis in social forums, Healthcare monitoring Systems, Disaster Management, Industrial management, and most recently, Epidemics and Outbreaks - Brings together many different aspects of the current research on intelligence technologies, such as neural networks, support vector machines, fuzzy logic, and evolutionary computation

The 9/11 terrorist attacks have sparked a wildfire of debates. There are several issues that serve as the source of these debates but they are all based on one of two common concerns: either the balance of power between the people and the U.S. government, or the efficiency of the nation's security resources. How far should the government be able to infringe upon the people's constitutional rights to expression, privacy, religious worship etc. to ensure the safety of its people? And how far will the people be willing to let those rights be infringed upon, if they are even aware that they exist. It is a strange dichotomy that is ironic when one considers that the war on terrorism is being fought in the name of freedom. The other concern was born from questions of whether or not 9/11 could have been prevented and if more lives could have been saved during the tragedy if the nation's security infrastructure was better organised. This book examines these various issues and debates.

A lot of companies have fallen prey to data breaches involving customers’ credit and debit accounts. Private businesses also are affected and are victims of cybercrime. All sectors including governments, healthcare, finance, enforcement, academia etc. need information security professionals who can safeguard their data and knowledge. But the current state is that there’s a critical shortage of qualified cyber security and knowledge security professionals. That is why we created this book to offer all of you a summary of the growing field of cyber and information security along with the various opportunities which will be available to you with professional cyber security degrees. This book may be a quick read; crammed with plenty of information about industry trends, career paths and certifications to advance your career. We all hope you’ll find this book helpful as you begin your career and develop new skills in the cyber security field. “The cyber threat to critical infrastructure continues to grow and represents one of the most serious national security challenges we must confront. The national and economic security of the United States depends on the reliable functioning of the nation’s critical infrastructure in the face of such threats.” -Presidential Executive Order, 2013 (Improving Critical Infrastructure Cybersecurity)

This book offers the latest research results in security and privacy for Intelligent Edge Computing Systems. It presents state-of-the art content and provides an in-depth overview of the basic background in this related field. Practical areas in both security and risk analysis are addressed as well as connections directly linked to Edge Computing paradigms. This book also offers an excellent foundation on the fundamental concepts and principles of security, privacy and risk analysis in Edge Computation infrastructures. It guides the reader through the core ideas with relevant ease. Edge Computing has burst onto the computational scene offering key technologies for allowing more flexibility at the edge of networks. As Edge Computing has evolved as well as the need for more in-depth solutions in security, privacy and risk analysis at the edge. This book includes various case studies and applications on Edge Computing. It includes the Internet of Things related areas, such as smart cities, blockchain, mobile networks, federated learning, cryptography and cybersecurity. This book is one of the first reference books covering security and risk analysis in Edge Computing Systems. Researchers and advanced-level students studying or working in Edge Computing and related security fields will find this book useful as a reference. Decision makers, managers and professionals working within these fields will want to purchase this book as well.