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This book, written by leaders in the protection field of critical infrastructures, provides an extended overview of the technological and operative advantages together with the security problems and challenges of the new paradigm of the Internet of Things in today's industry, also known as the Industry Internet of Things (IIoT). The incorporation of the new embedded technologies and the interconnected networking advances in the automation and monitoring processes, certainly multiplies the functional complexities of the underlying control system, whilst increasing security and privacy risks. The critical nature of the application context and its relevance for the well-being of citizens and their economy, attracts the attention of multiple, advanced attackers, with stealthy abilities to evade security policies, ex-filter information or exploit vulnerabilities. Some real-life events and registers in CERTs have already clearly demonstrated how the control industry can become vulnerable to multiple types of advanced threats whose focus consists in hitting the safety and security of the control processes. This book, therefore, comprises a detailed spectrum of research papers with highly analytical content and actuation procedures to cover the relevant security and privacy issues such as data protection, awareness, response and resilience, all of them working at optimal times. Readers will be able to comprehend the construction problems of the fourth industrial revolution and are introduced to effective, lightweight protection solutions which can be integrated as part of the new IIoT-based monitoring ecosystem.

This work analyses the scope of copyright protection for computer software in the United Kingdom,and examines challenges for the future. The work presents the case for the adoption and application of infringement methodology emanating from the courts in the United States, resulting in a narrower scope of protection than is presently argued for by many UK academics, practitioners and judges alike. The work makes a careful evaluation of the efficacy of the various prevailing tests for infringement of copyright in software and their progenies, suggesting an improved formula and advocating the utility of limiting doctrines to assist in the determination of substantial similarity of particular non-literal software elements, user interfaces and screen display protection. The monograph also contains a detailed study of reverse engineering, copyright defences, permitted acts, database protection and the copyright-contract interface in the context of computer software, not omitting crucial discussions of the internet, digital dissemination and the impact of recent treaty and legislative initiatives on British copyright law. As such it will be an important resource for practitioners, lecturers and students alike.

Secure your container environment against cyberattacks and deliver robust deployments with this practical guide Key Features * Explore a variety of Kubernetes components that help you to prevent cyberattacks * Perform effective resource management and monitoring with Prometheus and built-in Kubernetes tools * Learn techniques to prevent attackers from compromising applications and accessing resources for crypto-coin mining Book Description Kubernetes is an open source orchestration platform for managing containerized applications. Despite widespread adoption of the technology, DevOps engineers might be unaware of the pitfalls of containerized environments. With this comprehensive book, you'll learn how to use the different security integrations available on the Kubernetes platform to safeguard your deployments in a variety of scenarios. Learn Kubernetes Security starts by taking you through the Kubernetes architecture and the networking model. You'll then learn about the Kubernetes threat model and get to grips with securing clusters. Throughout the book, you'll cover various security aspects such as authentication, authorization, image scanning, and resource monitoring. As you advance, you'll learn about securing cluster components (the kube-apiserver, CoreDNS, and kubelet) and pods (hardening image, security context, and PodSecurityPolicy). With the help of hands-on examples, you'll also learn how to use open source tools such as Anchore, Prometheus, OPA, and Falco to protect your deployments. By the end of this Kubernetes book, you'll have gained a solid understanding of container security and be able to protect your clusters from cyberattacks and mitigate cybersecurity threats. What you will learn * Understand the basics of Kubernetes architecture and networking * Gain insights into different security integrations provided by the Kubernetes platform * Delve into Kubernetes' threat modeling and security domains * Explore different security configurations from a variety of practical examples * Get to grips with using and deploying open source tools to protect your deployments * Discover techniques to mitigate or prevent known Kubernetes hacks Who this book is for This book is for security consultants, cloud administrators, system administrators, and DevOps engineers interested in securing their container deployments. If you're looking to secure your Kubernetes clusters and cloud-based deployments, you'll find this book useful. A basic understanding of cloud computing and containerization is necessary to make the most of this book.

This book teaches you practical use and deployment of OAuth 2 from the perspectives of a client, an authorization server, and a resource server. You'll begin with an overview of OAuth and its components and interactions. Next, you'll get hands-on and build an OAuth client, an authorization server, and a protected resource. Then you'll dig into tokens, dynamic client registration, and more advanced topics

The enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR’s promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains.

Get up to speed on the latest Group Policy tools, features, and best practices Group Policy, Fundamentals, Security, and the Managed Desktop, 3rd Edition helps you streamline Windows and Windows Server management using the latest Group Policy tools and techniques. This updated edition covers Windows 10 and Windows Server vNext, bringing you up to speed on all the newest settings, features, and best practices. Microsoft Group Policy MVP Jeremy Moskowitz teaches you the major categories of Group Policy, essential troubleshooting techniques, and how to manage your Windows desktops. This is your complete guide to the latest Group Policy features and functions for all modern Windows clients and servers, helping you manage more efficiently and effectively. Perform true desktop and server management with the Group Policy Preferences, ADMX files, and additional add-ons Use every feature of the GPMC and become a top-notch administrator Troubleshoot Group Policy using tools, enhanced logs, Resource Kit utilities, and third-party tools Manage printers, drive maps, restrict hardware, and configure Internet Explorer Deploy software to your desktops, set up roaming profiles, and configure Offline Files for all your Windows clients-and manage it all with Group Policy settings Secure your desktops and servers with AppLocker, Windows Firewall with Advanced Security, and the Security Configuration Manager This is your comprehensive resource to staying current, with expert tips, techniques, and insight.

Specifying legal requirements for software systems to ensure their compliance with the applicable regulations is a major concern of requirements engineering. Personal data which is collected by an organization is often shared with other organizations to perform certain processing activities. In such cases, the General Data Protection Regulation (GDPR) requires issuing a data processing agreement (DPA) which regulates the processing and further ensures that personal data remains protected. Violating GDPR can lead to huge fines reaching to billions of Euros. Software systems involving personal data processing must adhere to the legal obligations stipulated both at a general level in GDPR as well as the obligations outlined in DPAs highlighting specific business. In other words, a DPA is yet another source from which requirements engineers can elicit legal requirements. However, the DPA must be complete according to GDPR to ensure that the elicited requirements cover the complete set of obligations. Therefore, checking the completeness of DPAs is a prerequisite step towards developing a compliant system. Analyzing DPAs with respect to GDPR entirely manually is time consuming and requires adequate legal expertise. In this paper, we propose an automation strategy that addresses the completeness checking of DPAs against GDPR provisions as a text classification problem. Specifically, we pursue ten alternative solutions which are enabled by different technologies, namely traditional machine learning, deep learning, language modeling, and few-shot learning. The goal of our work is to empirically examine how these different technologies fare in the legal domain. We computed F2 score on a set of 30 real DPAs. Our evaluation shows that best-performing solutions yield F2 score of 86.7% and 89.7% are based on pre-trained BERT and RoBERTa language models. Our analysis further shows that other alternative solutions based on deep learning (e.g., BiLSTM) and few-shot learning (e.g., SetFit) can achieve comparable accuracy, yet are more efficient to develop.

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice. It is a challenging task for any research field to screen the literature and determine what needs to be included in a systematic review in a transparent way. A new open source machine learning framework called ASReview, which employs active learning and offers a range of machine learning models, can check the literature efficiently and systemically.

Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.