The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. From the Golem of Prague to the military robots of modernity, the debate continues as to what degree of independence such entities should have and how to make sure that they do not turn on us, its inventors. Numerous recent advancements in all aspects of research, development and deployment of intelligent systems are well publicized but safety and security issues related to AI are rarely addressed. This book is proposed to mitigate this fundamental problem. It is comprised of chapters from leading AI Safety researchers addressing different aspects of the AI control problem as it relates to the development of safe and secure artificial intelligence. The book is the first edited volume dedicated to addressing challenges of constructing safe and secure advanced machine intelligence. The chapters vary in length and technical content from broad interest opinion essays to highly formalized algorithmic approaches to specific problems. All chapters are self-contained and could be read in any order or skipped without a loss of comprehension.

Artificial Intelligence and Global Security: Future Trends, Threats and Considerations brings a much-needed perspective on the impact of the integration of Artificial Intelligence (AI) technologies in military affairs. Experts forecast that AI will shape future military operations in ways that will revolutionize warfare.

Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.

In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.

This book provides stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation results (using several analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social networking in the recent decade. Today artificial intelligence or machine learning is redefining every aspect of cyber security. From improving organizations’ ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business. The book is useful for researchers, academics, industry players, data engineers, data scientists, governmental organizations, and non-governmental organizations.

The aim of the book is to analyse and understand the impacts of artificial intelligence in the fields of national security and defense; to identify the political, geopolitical, strategic issues of AI; to analyse its place in conflicts and cyberconflicts, and more generally in the various forms of violence; to explain the appropriation of artificial intelligence by military organizations, but also law enforcement agencies and the police; to discuss the questions that the development of artificial intelligence and its use raise in armies, police, intelligence agencies, at the tactical, operational and strategic levels.

This book provides a comparison and practical guide of the data protection laws of Canada, China (Hong Kong, Macau, Taiwan), Laos, Philippines, South Korea, United States and Vietnam. The book builds on the first book Data Protection Law. A Comparative Analysis of Asia-Pacific and European Approaches, Robert Walters, Leon Trakman, Bruno Zeller. As the world comes to terms with Artificial Intelligence (AI), which now pervades the daily lives of everyone. For instance, our smart or Iphone, and smart home technology (robots, televisions, fridges and toys) access our personal data at an unprecedented level. Therefore, the security of that data is increasingly more vulnerable and can be compromised. This book examines the interface of cyber security, AI and data protection. It highlights and recommends that regulators and governments need to undertake wider research and law reform to ensure the most vulnerable in the community have their personal data protected adequately, while balancing the future benefits of the digital economy.

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions

Artificial Intelligence (AI) Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years.

A day does not go by without a news article reporting some amazing breakthrough in artificial intelligence (AI). Many philosophers, futurists, and AI researchers have conjectured that human-level AI will be developed in the next 20 to 200 years. If these predictions are correct, it raises new and sinister issues related to our future in the age of