Nowadays, smartphones are considered as the most useful and essential devices of our daily life, in which individuals' around the world communicate with one another for various purposes. Around 96.8% people in the current world use mobile devices, and this coverage even increases up to 100% in many developed countries [1]. Recently, with the rapid advances in context-aware mobile technologies and increasing popularity of data science research, data-driven personalized mobile services and systems are emerging as an important technology for designing and developing user-centric smart mobile

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

Although ransomware has been around since the early days of personal computers, its sophistication and aggression have increased substantially over the years. Ransomware, as a type of malware to extort ransom payments from victims, has evolved to deliver payloads in different attack vectors and on multiple platforms, and creating repeated disruptions and financial loss to many victims. Many studies have performed ransomware analysis and/or presented detection, defense, or prevention techniques for ransomware. However, because the ransomware landscape has evolved aggressively, many of those studies have become less relevant or even outdated. Previous surveys on anti-ransomware studies have compared the methods and results of the studies they surveyed, but none of those surveys has attempted to critique on the internal or external validity of those studies. In this survey, we first examined the up-to-date concept of ransomware, and listed the inadequacies in current ransomware research. We then proposed a set of unified metrics to evaluate published studies on ransomware mitigation, and applied the metrics to 118 such studies to comprehensively compare and contrast their pros and cons, with the attempt to evaluate their relative strengths and weaknesses. Finally, we forecast the future trends of ransomware evolution, and propose future research directions.

Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general purpose vs application specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s). INDEX TERMS Distributed ledger technology, blockchain, immutability, DLT platforms.

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.