This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link:http://openaccess.city.ac.uk/3734/ Link to published version: http://dx.doi.org/10.1109/ COMST.2014.2320093 Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. City Research Online: http://openaccess.city.ac.uk/ publications@city.ac.ukCity Research Online  Abstract-The electricity industry is now at the verge of a new era. An era that promises, through the evolution of the existing electrical grids to Smart Grids, more efficient and effective power management, better reliability, reduced production costs and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the Smart Grid and the Smart Home, which we present as an integral part of the Smart Grid. Based on several scenarios we aim to present some of the most representative threats to the Smart Home / Smart Grid environment. The threats detected are categorized according to specific security goals set for the Smart Home/Smart Grid environment and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes the paper. Index Terms-Smart Grids, Smart Homes, Security, Countermeasures, Challenges I. INTRODUCTIONThe electric power infrastructure as we know it today has managed to serve our needs successfully, almost unchanged, for nearly a century; revolutionizing almost every aspect of our lives. However, as this infrastructure is inevitably aging it becomes increasingly less efficient, repeatedly running up against its limitations and constantly straining to keep up with our ever-increasing requirements. Needs for reliability, scalability, manageability, environmentally friendly energy generation, interoperability and cost effectiveness, bring forward the necessity for a modernized and intelligent grid for tomorrow; a new, reliable, efficient, flexible and secure energy infrastructure, known as the Smart Grid [1].Through the incorporation of advanced power system electronics, networking and communication technologies the Smart Grid is envisioned to significantly enhance the existing electric grid. Allowing for more accurate real-time monitoring, ensuring the optimization of power flows and enabling for two-way communication between the utility and customer sides while pointing th...

The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface and, consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method to discuss the real-life implications of our findings.

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Abstract-The challenge for fast and low-cost deployment of ubiquitous personalized e-Health services has prompted us to propose a new framework architecture for such services. We have studied the operational features and the environment of e-Health services and we led to a framework structure that extends the ETSI/Parlay architecture, which is used for the deployment of standardized services over the next generation IP networks. We expanded the ETSI/Parlay architecture with new service capability features as well as sensor, profiling and security mechanisms. The proposed framework assists the seamless integration, within the e-Health service structure, of diverse facilities provided by both the underlying communication and computing infrastructure as well as the patient's bio and context sensor networks. Finally, we demonstrate the deployment of a tele-monitoring service in smart home environment based on the proposed framework architecture.Index Terms-ETSI/Parlay architecture, personalized healthcare services, profiling mechanisms, sensor networks mechanisms, security mechanisms. I. INTRODUCTIONY its nature, e-Health domain is a multidisciplinary area that is strongly influenced by many different scientific and technology fields. In last decade, many of the key visions of the medical world have taken shape thanks to advances in Information and Communications Technology (ICT). The provision of healthcare services everywhere and at any time, known as ubiquitous healthcare, is becoming a reality, as the ICT concepts of personalized services, service mobility and Spyros L. Fengos is with the Department of Cardiology, General Hospital "Agia Olga" in Athens, Greece (e-mail: sfengos@gmail.com).Nikolaos G. Lazarou is with the Department of Emergency Medicine, Rion University Hospital, Greece (e-mail: lazarou.nikolaos@gmail.com).context awareness have been adopted by the modern medical practice [1], [2]. In many cases, the establishment of ubiquitous healthcare meets the rising demand for personalized healthcare services with low costs and patient's efficient monitoring.In fact, ubiquitous healthcare has shifted the conventional healthcare provision paradigm to a new one, hereafter referred as Next Generation e-Health (NGeH) paradigm. NGeH emphasizes on the individual's disease prevention, proactive actions, life quality improvement and, under concrete circumstances, on-spot (out-hospital) provision of emergency assistance by delivering personalized healthcare services at the right time, right place and right manner without limitations on time and location [3]. NGeH paradigm encourages individuals to have a normal life regardless of any health problem. For this reason, it encompasses innovative medical practices that are concordant with the real individual's needs, habits, preferences, perspectives, living conditions, or any peculiarity, as they are depicted in individual's profile.Moving towards such medical practices, it is obvious that conventional frameworks for healthcare provision are not able to support efficient...

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

Botnet use is on the rise, with a growing number of botmasters now switching to the HTTP-based C&C infrastructure. This offers them more stealth by allowing them to blend in with benign web traffic. Several works have been carried out aimed at characterising or detecting HTTP-based bots, many of which use network communication features as identifiers of botnet behaviour. In this paper, we present a survey of these approaches and the network features they use in order to highlight how botnet traffic is currently differentiated from normal traffic. We classify papers by traffic types, and provide a breakdown of features by protocol. In doing so, we hope to highlight the relationships between features at the application, transport and network layers.

Abstract-Electronic devices we use on a daily basis collect sensitive information without preserving user's privacy. In this paper, we propose the lord of the sense (LotS), a privacy preserving reputation system for participatory sensing applications. Our system maintains the privacy and anonymity of information with the use of cryptographic techniques and combines voting approaches to support users' reputation. Furthermore, LotS maintains accountability by tracing back a misbehaving user while maintaining k-anonymity. A detailed security analysis is presented with the current advantages and disadvantages of our system.