Drawing upon years of practical experience and using numerous examples and illustrative case studies, Threat Forecasting: Leveraging Big Data for Predictive Analysis discusses important topics, including the danger of using historic data as the basis for predicting future breaches, how to use security intelligence as a tool to develop threat forecasting techniques, and how to use threat data visualization techniques and threat simulation tools. Readers will gain valuable security insights into unstructured big data, along with tactics on how to use the data to their advantage to reduce risk. - Presents case studies and actual data to demonstrate threat data visualization techniques and threat simulation tools - Explores the usage of kill chain modelling to inform actionable security intelligence - Demonstrates a methodology that can be used to create a full threat forecast analysis for enterprise networks of any size

Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.

Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of “future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

This handbook examines the latest techniques and strategies that are used to unlock the risk transfer capacity of global financial and capital markets. Taking the financial crisis and global recession into account, it frames and contextualises non-traditional risk transfer tools created over the last 20 years. Featuring contributions from distinguished academics and professionals from around the world, this book covers in detail issues in securitization, financial risk management and innovation, structured finance and derivatives, life and non-life pure risk management, market and financial reinsurance, CAT risk management, crisis management, natural, environmental and man-made risks, terrorism risk, risk modelling, vulnerability and resilience. This handbook will be of interest to academics, researchers and practitioners in the field of risk transfer.

This book analyses the implications of the technical, legal, ethical and privacy challenges as well as challenges for human rights and civil liberties regarding Artificial Intelligence (AI) and National Security. It also offers solutions that can be adopted to mitigate or eradicate these challenges wherever possible. As a general-purpose, dual-use technology, AI can be deployed for both good and evil. The use of AI is increasingly becoming of paramount importance to the government’s mission to keep their nations safe. However, the design, development and use of AI for national security poses a wide range of legal, ethical, moral and privacy challenges. This book explores national security uses for Artificial Intelligence (AI) in Western Democracies and its malicious use. This book also investigates the legal, political, ethical, moral, privacy and human rights implications of the national security uses of AI in the aforementioned democracies. It illustrates how AI for national security purposes could threaten most individual fundamental rights, and how the use of AI in digital policing could undermine user human rights and privacy. In relation to its examination of the adversarial uses of AI, this book discusses how certain countries utilise AI to launch disinformation attacks by automating the creation of false or misleading information to subvert public discourse. With regards to the potential of AI for national security purposes, this book investigates how AI could be utilized in content moderation to counter violent extremism on social media platforms. It also discusses the current practices in using AI in managing Big Data Analytics demands. This book provides a reference point for researchers and advanced-level students studying or working in the fields of Cyber Security, Artificial Intelligence, Social Sciences, Network Security as well as Law and Criminology. Professionals working within these related fields and law enforcement employees will also find this book valuable as a reference.

You know by now that your company could not survive without the Internet. Not in today’s market. You are either part of the digital economy or reliant upon it. With critical information assets at risk, your company requires a state-of-the-art cybersecurity program. But how do you achieve the best possible program? Tari Schreider, in Building Effective Cybersecurity Programs: A Security Manager’s Handbook, lays out the step-by-step roadmap to follow as you build or enhance your cybersecurity program. Over 30+ years, Tari Schreider has designed and implemented cybersecurity programs throughout the world, helping hundreds of companies like yours. Building on that experience, he has created a clear roadmap that will allow the process to go more smoothly for you. Building Effective Cybersecurity Programs: A Security Manager’s Handbook is organized around the six main steps on the roadmap that will put your cybersecurity program in place: Design a Cybersecurity Program Establish a Foundation of Governance Build a Threat, Vulnerability Detection, and Intelligence Capability Build a Cyber Risk Management Capability Implement a Defense-in-Depth Strategy Apply Service Management to Cybersecurity Programs Because Schreider has researched and analyzed over 150 cybersecurity architectures, frameworks, and models, he has saved you hundreds of hours of research. He sets you up for success by talking to you directly as a friend and colleague, using practical examples. His book helps you to: Identify the proper cybersecurity program roles and responsibilities. Classify assets and identify vulnerabilities. Define an effective cybersecurity governance foundation. Evaluate the top governance frameworks and models. Automate your governance program to make it more effective. Integrate security into your application development process. Apply defense-in-depth as a multi-dimensional strategy. Implement a service management approach to implementing countermeasures. With this handbook, you can move forward confidently, trusting that Schreider is recommending the best components of a cybersecurity program for you. In addition, the book provides hundreds of citations and references allow you to dig deeper as you explore specific topics relevant to your organization or your studies.