Detecting cloud virtual network isolation security for data leakage

This thesis considers information leakage in cloud virtually isolated networks. Virtual Network (VN) Isolation is a core element of cloud security yet research literature shows that no experimental work, to date, has been conducted to test, discover and evaluate VN isolation data leakage. Consequently, this research focussed on that gap. Deep Dives of the cloud infrastructures were performed, followed by (Kali) penetration tests to detect any leakage. This data was compared to information gathered in the Deep Dive, to determine the level of cloud network infrastructure being exposed. As a major contribution to research, this is the first empirical work to use a Deep Dive approach and a penetration testing methodology applied to both CloudStack and OpenStack to demonstrate cloud network isolation vulnerabilities. The outcomes indicated that Cloud manufacturers need to test their isolation mechanisms more fully and enhance them with available solutions. However, this field needs more industrial data to confirm if the found issues are applicable to non-open source cloud technologies. If the problems revealed are widespread then this is a major issue for cloud security. Due to the time constraints, only two cloud testbeds were built and analysed, but many potential future works are listed for analysing more complicated VN, analysing leveraged VN plugins and testing if system complexity will cause more leakage or protect the VN. This research is one of the first empirical building blocks in the field and gives future researchers the basis for building their research on top of the presented methodology and results and for proposing more effective solutions

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

A Model for User-centric Information Security Risk Assessment and Response

Managing and assessing information security risks in organizations is a well understood and accepted approach, with literature providing a vast array of proposed tools, methods and techniques. They are, however, tailored for organizations, with little literature supporting how these can be achieved more generally for end-users, i.e. users, who are solely responsible for their devices, data and for making their own security decisions. To protect against them, technical countermeasures alone has been found insufficient as it can be misused by users and become vulnerable to various threats. This research focuses on better understanding of human behavior which is vital for ensuring an efficient information security environment. Motivated by the fact that different users react differently to the same stimuli, identifying the reasons behind variations in security behavior and why certain users could be “at risk” more than others is a step towards developing techniques that can enhance user’s behavior and protect them against security attacks. A user survey was undertaken to explore users security behavior in several domains and to investigate the correlation between users characteristics and their risk taking behavior. Analysis of the results demonstrated that user’s characteristics do play a significant role in affecting their security behavior risk levels. Based upon these findings, this study proposed a user-centric model that is intended to provide a comprehensive framework for assessing and communicating information security risks for users of the general public with the aim of monitoring, assessing and responding to user’s behavior in a continuous, individualized and timely manner. The proposed approach is built upon two components: assessing risks and communicating them. Aside from the traditional risk assessment formula, three risk estimation models are proposed: a user-centric, system-based and an aggregated model to create an individualized risk profile. As part of its novelty, both user-centric and behavioral-related factors are considered in the assessment. This resulted in an individualized and timely risk assessment in granular form. Aside from the traditional risk communication approach of one message/one-size-fits-all, a gradual response mechanism is proposed to individually and persuasively respond to risk and educate the user of his risk-taking behavior. Two experiments and a scenario-based simulation of users with varying user-centric factors has been implemented to simulate the proposed model, how it works and to evaluate its effectiveness and usefulness. The proposed approach worked in the way it was expected to. The analysis of the experiments results provided an indication that risk could be assessed differently for the same behavior based upon a number of user-centric and behavioral-related factors resulting in an individualized granular risk score/level. This granular risk assessment, away from high, medium and low, provided a more insightful evaluation of both risk and response. The analysis of results was also useful in demonstrating how risk is not the same for all users and how the proposed model is effective in adapting to differences between users offering a novel approach to assessing information security risks

SDN-based dynamic policy specification and enforcement for provisioning SECaaS in cloud

In this paper we make use of SDN for provisioning of Security as a Service (SECaaS) to the tenant and simplify the security management in cloud. We have developed a Security Application (SA) for the SDN Controller which is used for capturing the tenant security requirements and enforcing the related security policies for securing their virtual machines (VMs). We have developed a security policy specification language for enforcing TPM, Access Control and Intrusion Detection related security policies with the SA. Finally we present the prototype implementation of our approach and some performance results