A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented

Advancing security information and event management frameworks in managed enterprises using geolocation

Includes bibliographical referencesSecurity Information and Event Management (SIEM) technology supports security threat detection and response through real-time and historical analysis of security events from a range of data sources. Through the retrieval of mass feedback from many components and security systems within a computing environment, SIEMs are able to correlate and analyse events with a view to incident detection. The hypothesis of this study is that existing Security Information and Event Management techniques and solutions can be complemented by location-based information provided by feeder systems. In addition, and associated with the introduction of location information, it is hypothesised that privacy-enforcing procedures on geolocation data in SIEMs and meta- systems alike are necessary and enforceable. The method for the study was to augment a SIEM, established for the collection of events in an enterprise service management environment, with geo-location data. Through introducing the location dimension, it was possible to expand the correlation rules of the SIEM with location attributes and to see how this improved security confidence. An important co-consideration is the effect on privacy, where location information of an individual or system is propagated to a SIEM. With a theoretical consideration of the current privacy directives and regulations (specifically as promulgated in the European Union), privacy supporting techniques are introduced to diminish the accuracy of the location information - while still enabling enhanced security analysis. In the context of a European Union FP7 project relating to next generation SIEMs, the results of this work have been implemented based on systems, data, techniques and resilient features of the MASSIF project. In particular, AlienVault has been used as a platform for augmentation of a SIEM and an event set of several million events, collected over a three month period, have formed the basis for the implementation and experimentation. A "brute-force attack" misuse case scenario was selected to highlight the benefits of geolocation information as an enhancement to SIEM detection (and false-positive prevention). With respect to privacy, a privacy model is introduced for SIEM frameworks. This model utilises existing privacy legislation, that is most stringent in terms of privacy, as a basis. An analysis of the implementation and testing is conducted, focusing equally on data security and privacy, that is, assessing location-based information in enhancing SIEM capability in advanced security detection, and, determining if privacy-enforcing procedures on geolocation in SIEMs and other meta-systems are achievable and enforceable. Opportunities for geolocation enhancing various security techniques are considered, specifically for solving misuse cases identified as existing problems in enterprise environments. In summary, the research shows that additional security confidence and insight can be achieved through the augmentation of SIEM event information with geo-location information. Through the use of spatial cloaking it is also possible to incorporate location information without com- promising individual privacy. Overall the research reveals that there are significant benefits for SIEMs to make use of geo-location in their analysis calculations, and that this can be effectively conducted in ways which are acceptable to privacy considerations when considered against prevailing privacy legislation and guidelines

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

Homelessness in Oxford : risks and opportunities across housing and homeless transitions

This report presents initial findings from CSI’s Homelessness in Oxford project. The project was designed by Dr. Garratt and Dr. Flaherty to be the first systematic attempt to track and understand people’s transitions into and out of different experiences of homelessness in Oxford. The project also explored the roles played by statutory and non-statutory homelessness prevention and relief services in Oxford

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Analyysi GDPR:n vaikutuksista lääkinnälliseen sovellukseen

The European General Data Protection Regulation (GDPR) came into full effect in May 2018 after a two-year transition period. The regulation aims to improve the data protection of the citizens of the European Union. The regulation also affects the rest of the world. Although not all the rules introduced by the GDPR are new, the regulation contains novel requirements both regarding data protection and information security level. One of these new requirements is the right of a natural person to be forgotten in certain circumstances. The novelty of the GDPR and in some parts the general wording of the rules contained in the regulation may create difficulties in interpretation for the entities that have to con-form to the regulation’s rules. This thesis examines through the analysis of a medical application, the impact of the regulation on data controllers and software developers dealing with data concerning health. The data protection and information security requirements presented by the GDPR are applied to the analysed application. The application is analysed against the requirements derived from the GDPR with the help of the Software product quality model of the ISO/IEC 25010 standard. Based on the conducted analysis, the application is in a good state regarding the GDPR even when some changes need to be implemented. At this stage, the impact of the GDPR on applications containing data concerning health is not significant if best practices were used to develop the application. The impact of the GDPR lies more in the general approach to managing risks directed at the software since the content and the amount of personal data should be considered in risk management. In addition to the analysis of a medical application, this thesis contains an analysis of the previously existing privacy legislations of the United States, Finland and France. The related privacy laws of these countries are compared to the GDPR so that the content and new additions of the new GDPR would be more apparent

Natural language processing (NLP) for clinical information extraction and healthcare research

Introduction: Epilepsy is a common disease with multiple comorbidities. Routinely collected health care data have been successfully used in epilepsy research, but they lack the level of detail needed for in-depth study of complex interactions between the aetiology, comorbidities, and treatment that affect patient outcomes. The aim of this work is to use natural language processing (NLP) technology to create detailed disease-specific datasets derived from the free text of clinic letters in order to enrich the information that is already available. Method: An NLP pipeline for the extraction of epilepsy clinical text (ExECT) was redeveloped to extract a wider range of variables. A gold standard annotation set for epilepsy clinic letters was created for the validation of the ExECT v2 output. A set of clinic letters from the Epi25 study was processed and the datasets produced were validated against Swansea Neurology Biobank records. A data linkage study investigating genetic influences on epilepsy outcomes using GP and hospital records was supplemented with the seizure frequency dataset produced by ExECT v2. Results: The validation of ExECT v2 produced overall precision, recall, and F1 score of 0.90, 0.86, and 0.88, respectively. A method of uploading, annotating, and linking genetic variant datasets within the SAIL databank was established. No significant differences in the genetic burden of rare and potentially damaging variants were observed between the individuals with vs without unscheduled admissions, and between individuals on monotherapy vs polytherapy. No significant difference was observed in the genetic burden between people who were seizure free for over a year and those who experienced at least one seizure a year. Conclusion: This work presents successful extraction of epilepsy clinical information and explores how this information can be used in epilepsy research. The approach taken in the development of ExECT v2, and the research linking the NLP outputs, routinely collected health care data, and genetics set the way for wider research