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Simon Parkinson

Bio: Simon Parkinson is an academic researcher from University of Huddersfield. The author has contributed to research in topics: Machine tool & Measurement uncertainty. The author has an hindex of 11, co-authored 64 publications receiving 742 citations.


Papers
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Journal ArticleDOI
TL;DR: A large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed to minimize future cyber security risks in the connected and autonomous vehicle sector.
Abstract: Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed. This review highlighted that the majority of studies are reactive and vulnerabilities are often discovered by friendly adversaries (white-hat hackers). Many gaps in the knowledge base were identified. Priority should be given to address these knowledge gaps to minimize future cyber security risks in the connected and autonomous vehicle sector.

340 citations

Journal ArticleDOI
01 Dec 2017
TL;DR: The impact of security issues and possible solutions are determined, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.
Abstract: Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

267 citations

Journal ArticleDOI
TL;DR: It is recommended that mitigating the cyber security and privacy risks embedded in CAVs require inter-institutional cooperation, awareness campaigns and trials for trust-building purposes, mandatory educational training for manufacturers and perhaps more importantly for end-users, balanced and fair responsibility-sharing, two-way dynamic communication channels and a clear consensus on what constitutes threats and solutions.
Abstract: Connected and Autonomous Vehicles (CAVs) constitute an automotive development carrying paradigm-shifting potential that may soon be embedded into a dynamically changing urban mobility landscape. The complex machine-led dynamics of CAVs make them more prone to data exploitation and vulnerable to cyber attacks than any of their predecessors increasing the risks of privacy breaches and cyber security violations for their users. This can adversely affect the public acceptability of CAVs, give them a bad reputation at this embryonic stage of their development, create barriers to their adoption and increased use, and complicate the business models of their future operations. Therefore, it is vital to identify and create an in-depth understanding of the cyber security and privacy issues associated with CAVs, and of the way these can be prioritised and addressed. This work employs 36 semi-structured elite interviews to explore the diverse dimensions of user acceptance through the lens of the well-informed CAV experts that already anticipate problems and look for their solutions. Our international interviewee sample represents academia, industry and policy-making so that all the key stakeholder voices are heard. Thematic analysis was used to identify and contextualise the factors that reflect and affect CAV acceptance in relation to the privacy and cyber security agendas. Six core themes emerged: awareness, user and vendor education, safety, responsibility, legislation, and trust. Each of these themes has diverse and distinctive dimensions and are discussed as sub-themes. We recommend that mitigating the cyber security and privacy risks embedded in CAVs require inter-institutional cooperation, awareness campaigns and trials for trust-building purposes, mandatory educational training for manufacturers and perhaps more importantly for end-users, balanced and fair responsibility-sharing, two-way dynamic communication channels and a clear consensus on what constitutes threats and solutions.

48 citations

Journal ArticleDOI
Saad Khan1, Simon Parkinson1, Liam Grant1, Na Liu1, Stephen Mcguire1 
TL;DR: A systematic review of health-based IoT data collected from wearable IoT technology explores existing work in computer security using these data sources, identifying key themes of work, key limitations, and challenges.
Abstract: Health data are being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also present opportunities within computer security, specifically with biometric systems used for identification and authentication purposes. This article performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations, and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work.

38 citations

Journal ArticleDOI
TL;DR: A novel method of modelling file system permissions which can be used by association rule mining techniques to identify irregular permissions is presented and results in the creation of object-centric model as a by-product.
Abstract: Modelling Microsoft's New Technology File System permissions for analysis.Using association rule mining to identify irregular file system permissions.Development of a two-stage tool for auditing Microsoft's New Technology File System.Accuracy greater than 90% for both real-world and synthetic directory structures. Identifying irregular file system permissions in large, multi-user systems is challenging due to the complexity of gaining structural understanding from large volumes of permission information. This challenge is exacerbated when file systems permissions are allocated in an ad-hoc manner when new access rights are required, and when access rights become redundant as users change job roles or terminate employment. These factors make it challenging to identify what can be classed as an irregular file system permission, as well as identifying if they are irregular and exposing a vulnerability. The current way of finding such irregularities is by performing an exhaustive audit of the permission distribution; however, this requires expert knowledge and a significant amount of time. In this paper a novel method of modelling file system permissions which can be used by association rule mining techniques to identify irregular permissions is presented. This results in the creation of object-centric model as a by-product. This technique is then implemented and tested on Microsoft's New Technology File System permissions (NTFS). Empirical observations are derived by making comparisons with expert knowledge to determine the effectiveness of the proposed technique on five diverse real-world directory structures extracted from different organisations. The results demonstrate that the technique is able to correctly identify irregularities with an average accuracy rate of 91%, minimising the reliance on expert knowledge. Experiments are also performed on synthetic directory structures which demonstrate an accuracy rate of 95% when the number of irregular permissions constitutes 1% of the total number. This is a significant contribution as it creates the possibility of identifying vulnerabilities without prior knowledge of how to file systems permissions are implemented within a directory structure.

35 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2016
TL;DR: This application applied longitudinal data analysis modeling change and event occurrence will help people to enjoy a good book with a cup of coffee in the afternoon instead of facing with some infectious virus inside their computer.
Abstract: Thank you very much for downloading applied longitudinal data analysis modeling change and event occurrence. As you may know, people have look hundreds times for their favorite novels like this applied longitudinal data analysis modeling change and event occurrence, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.

2,102 citations

01 Jan 2007
TL;DR: A translation apparatus is provided which comprises an inputting section for inputting a source document in a natural language and a layout analyzing section for analyzing layout information.
Abstract: A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

740 citations

Journal ArticleDOI
08 Apr 2018
TL;DR: The state-of-the-art of fog computing and its integration with the IoT is presented by highlighting the benefits and implementation challenges and the architecture of the fog and emerging IoT applications that will be improved by using the fog model are focused on.
Abstract: With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

410 citations