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The 3D Finite-Difference Time-Domain (FDTD) method simulates structures in the time-domain using a direct form of Maxwell's curl equations. This method has the advantage over other simulation methods in that it does not use empirical approximations. Unfortunately, it requires large amounts of memory and long simulation times. This thesis applies parallel processing to the method so that simulation times are greatly reduced. Parallel processing, though, has the disadvantage in that simulation programs require to be segmented so that each processor processes a separate part of the simulation. Another disadvantage of parallel processing is that each processor communicates with neighbouring processors to report their conditions. For large processor arrays this can result in a large overhead in simulation time. Two main methods of parallel processing discussed: Transputer arrays and clustered workstations over a local area network (LAN). These have been chosen because of their relatively cheapness to use, and their widespread availability. The results presented apply to the simulation of a microstrip antenna and to propagation of electrical signals in a printed circuit board (PCB). Microstrip antennas are relatively difficult to simulate in the time-domain because they have resonant pulses. Methods that reduce this problem are discussed in the thesis. The thesis contains a novel analysis of the parallel processing showing, using equations, tables and graphs, the optimum array size for a given inter-processor communication speed and for a given iteration time. This can be easily applied to any processing system. Background material on the 3D FDTD method and microstrip antennas is also provided. From the work on the parallel processing of the 3D FDTD a novel technique for the simulation of the Finite-element (FE) method is also discussed.

Data sharing domains have expanded over several, both trusted and insecure environments. At the same time, the data security boundaries have shrunk from internal network perimeters down to a single identity and a piece of information. Since new EU GDPR regulations, the personally identifiable information sharing requires data governance in favour of a data subject. Existing enterprise grade IRM solutions fail to follow open standards and lack of data sharing frameworks that could efficiently integrate with existing identity management and authentication infrastructures. IRM services that stood against cloud demands often offer a very limited access control functionality allowing an individual to store a document online giving a read or read-write permission to other individual identified by email address. Unfortunately, such limited information sharing controls are often introduced as the only safeguards in large enterprises, healthcare institutions and other organizations that should provide the highest possible personal data protection standards. The IRM suffers from a systems architecture vulnerability where IRM application installed on a semi-trusted client truly only guarantees none or full access enforcement. Since no single authority is contacted to verify each committed change the adversary having an advantage of possessing data-encrypting and key-encrypting keys could change and re-encrypt the amended content despite that read only access has been granted. Finally, the two evaluated IRM products, have either the algorithm security lifecycle (ASL) relatively short to protect the shared data, or the solution construct highly restrained secure key-encrypting key distribution and exposes a symmetric data-encrypting key over the network. Presented here sticky policy with identity-based encryption (SPIBE) solution was designed for secure cloud data sharing. SPIBE challenges are to deliver simple standardized construct that would easily integrate with popular OOXML-like document formats and provide simple access rights enforcement over protected content. It leverages a sticky policy construct using XACML access policy language to express access conditions across different cloud data sharing boundaries. XACML is a cloud-ready standard designed for a global multi-jurisdictional use. Unlike other raw ABAC implementations, the XACML offers a standardised schema and authorisation protocols hence it simplifies interoperability. The IBE is a cryptographic scheme protecting the shared document using an identified policy as an asymmetric key-encrypting a symmetric data-encrypting key. Unlike ciphertext-policy attribute-based access control (CP-ABE), the SPIBE policy contains not only access preferences but global document identifier and unique version identifier what makes each policy uniquely identifiable in relation to the protected document. In IBE scheme the public key-encrypting key is known and could be shared between the parties although the data-encrypting key is never sent over the network. Finally, the SPIBE as a framework should have a potential to protect data in case of new threats where ASL of a used cryptographic primitive is too short, when algorithm should be replaced with a new updated cryptographic primitive. The IBE like a cryptographic protocol could be implemented with different cryptographic primitives. The identity-based encryption over isogenous pairing groups (IBE-IPG) is a post-quantum ready construct that leverages the initial IBE Boneh-Franklin (IBE-BF) approach. Existing IBE implementations could be updated to IBE-IPG without major system amendments. Finally, by applying the one document versioning blockchain-like construct could verify changes authenticity and approve only legitimate document updates, where other IRM solutions fail to operate delivering the one single authority for non-repudiation and authenticity assurance.

Scaling big data infrastructure using multi-cloud environment has led to the demand for highly secure, resilient and reliable data sharing method. Several variants of secret sharing scheme have been proposed but there remains a gap in knowledge on the evaluation of these methods in relation to scalability, resilience and key management as volume of files generated increase and cloud outages persist. In line with these, this thesis presents an evaluation of a method that combines data fragmentation with Shamir's secret sharing scheme known as Fragmented Secret Share System (FSSS). It applies data fragmentation using a calculated optimum fragment size and encrypts each fragment using a 256-bit AES key length before dispersal to cloudlets, the encryption key is managed using secret sharing methods as used in cryptography. Four experiments were performed to measure the scalability, resilience and reliability in key management. The first and second experiments evaluated scalability using defined fragment blocks and an optimum fragment size. These fragment types were used to break file of varied sizes into fragments, and then encrypted and dispersed to the cloud, and recovered when required. Both were used in combination of different secret sharing policies for key management. The third experiment tested file recovery during cloud failures, while the fourth experiment focused on efficient key management. The contributions of this thesis are of two ways: development of evaluation frameworks to measure scalability and resilience of data sharing methods; and the provision of information on relationships between file sizes and share policies combinations. While the first aimed at providing platform to measure scalability from the point of continuous production as file size and volume increase, and resilience as the potential to continue operation despite cloud outages; the second provides experimental frameworks on the effects of file sizes and share policies on overall system performance. The results of evaluation of FSSS with similar methods showed that the fragmentation method has less overhead costs irrespective of file sizes and the share policy combination. That the inherent challenges in secret sharing scheme can only be solved through alternative means such as combining secret sharing with other data fragmentation method. In all, the system is less of any erasure coding technique, making it difficult to detect corrupt or lost fragment during file recovery.

The heart is a fundamental aspect of the human body. Significant work has been undertaken to better understand the characteristics and mechanisms of this organ in past research. Greater understanding of the heart not only provides advances in medicine but also enables practitioners to better assess the health risk of patients. This thesis approaches the study of the heart from a health informatics perspective. The questions posed in this thesis is whether research is capable of describing and modelling heart data from a statistical perspective, along with exploring techniques to improve the accuracy of clinical risk assessment algorithms that rely on this data. The contributions of this thesis may be grouped into two main areas: statistical analysis, modelling and simulation of heart data; and improved risk assessment accuracy of the Early Warning Score (EWS) algorithm using a quartile-based technique. Statistical analysis of heart data, namely RR intervals, contributes to more informed understanding of the underlying characteristics of the heart and is achieved using null-hypothesis testing through the Anderson-Darling (AD) test statistic. The modelling process of heart data demonstrates methodologies for simulation of this data type, namely individual distribution modelling and normal mixture modelling, and contributes to assessing techniques that are most capable of modelling this type of data. For improved accuracy on the EWS algorithms, a quartiles technique, inspired by anomaly-based intrusion detection systems, is presented which enables customisation of risk score thresholds for each patient defined during a training phase. Simulated heart data is used to evaluate the standard EWS algorithm against the quartile-based approach. The defined metric of accuracy ratio provides quantitative evidence on the accuracy of the standard EWS algorithm in comparison with the proposed quartile based technique. Statistical analysis in this thesis demonstrates that samples of heart data can be described using normal, Weibull, logistic and gamma distribution within the scope of two minute data samples. When there is strong evidence to suggest that RR intervals analysed fits a particular distribution, individual modelling technique is the ideal candidate whilst normal mixture modelling is better suited for long-term modelling, i.e. greater than two minutes of heart data. In comparative evaluation of the standard EWS algorithm and the quartile-based technique using modelled heart data, greater accuracy is demonstrated in the quartiles-based technique for patients whose heart rate is healthy, but outside the normal ranges of the general population.

Affiliate advertising is changing the way that people do business online. Retailers are now offering incentives to third-party publishers for advertising goods and services on their behalf in order to capture more of the market. Online advertising spending has already over taken that of traditional advertising in all other channels in the UK and is slated to do so worldwide as well [1]. In this highly competitive industry, the livelihood of a publisher is intrinsically linked to their web site performance. Understanding the strengths and weaknesses of a web site is fundamental to improving its quality and performance. However, the definition of performance may vary between different business sectors or even different sites in the same sector. In the affiliate advertising industry, the measure of performance is generally linked to the fulfilment of advertising campaign goals, which often equates to the ability to generate revenue or brand awareness for the retailer. This thesis aims to explore the correlation of web site evaluation metrics to the business performance of a company within an affiliate advertising programme. In order to explore this correlation, an automated evaluation framework was built to examine a set of web sites from an active online advertising campaign. A purpose-built web crawler examined over 4,000 sites from the advertising campaign in approximately 260 hours gathering data to be used in the examination of URL similarity, URL relevance, search engine visibility, broken links, broken images and presence on a blacklist. The gathered data was used to calculate a score for each of the features which were then combined to create an overall HealthScore for each publishers. The evaluated metrics focus on the categories of domain and content analysis. From the performance data available, it was possible to calculate the business performance for the 234 active publishers using the number of sales and click-throughs they achieved. When the HealthScores and performance data were compared, the HealthScore was able to predict the publisher's performance with 59% accuracy.

The current processes involved in the acquisition of investigative data from third parties, such as banks, Internet Service Providers (ISPs) and employers, by the public authorities can breach the rights of the individuals under investigation. This is mainly caused by the necessity to identify the records of interest, and thus the potential suspects, to the dataholders. Conversely, the public authorities often put pressure on legislators to provide a more direct access to the third party data, mainly in order to improve on turnaround times for enquiries and to limit the likelihood of compromising the investigations. This thesis presents a novel methodology for improving privacy and the performance of the investigative data acquisition process. The thesis shows that it is possible to adapt Symmetric Private Information Retrieval (SPIR) protocols for use in the acquisition process, and that it is possible to dynamically adjust the balance between the privacy and performance based on the notion of k-anonymity. In order to evaluate the findings an Investigative Data Acquisition Platform (IDAP) is formalised, as a cryptographic privacy-preserving enhancement to the current data acquisition process. SPIR protocols are often computationally intensive, and therefore, they are generally unsuitable to retrieve records from large datasets, such as the ISP databases containing records of the network traffic data. This thesis shows that, despite the fact that many potential sources of investigative data exist, in most cases the data acquisition process can be treated as a single-database SPIR. Thanks to this observation, the notion of k-anonymity, developed for privacy-preserving statistical data-mining protocols, can be applied to the investigative scenarios, and used to narrow down the number of records that need to be processed by a SPIR protocol. This novel approach makes the application of SPIR protocols in the retrieval of investigative data feasible. The dilution factor is defined, by this thesis, as a parameter that expresses the range of records used to hide a single identity of a suspect. Interestingly, the value of this parameter does not need to be large in order to protect privacy, if the enquiries to a given dataholder are frequent. Therefore, IDAP is capable of retrieving an interesting record from a dataholder in a matter of seconds, while an ordinary SPIR protocol could take days to complete retrieval of a record from a large dataset. This thesis introduces into the investigative scenario a semi-trusted third party, which is a watchdog organisation that could proxy the requests for investigative data from all public authorities. This party verifies the requests for data and hides the requesting party from the dataholder. This limits the dataholders ability to judge the nature of the enquiry. Moreover, the semi-trusted party would filter the SPIR responses from the dataholders, by securely discarding the records unrelated to enquiries. This would prevent the requesting party from using a large computational power to decrypt the diluting records in the future, and would allow the watchdog organisation to verify retrieved data in court, if such a need arises. Therefore, this thesis demonstrates a new use for the semi-trusted third parties in SPIR protocols. Traditionally used to improve on the complexity of SPIR protocols, such party can potentially improve the perception of the cryptographic trapdoor-based privacy- preserving information retrieval systems, by introducing policy-based controls. The final contribution to knowledge of this thesis is definition of the process of privacy-preserving matching records from different datasets based on multiple selection criteria. This allows for the retrieval of records based on parameters other than the identifier of the interesting record. Thus, it is capable of adding a degree of fuzzy matching to the SPIR protocols that traditionally require a perfect match of the request to the records being retrieved. This allows for searching datasets based on circumstantial knowledge and suspect profiles, thus, extends the notion of SPIR to more complex scenarios. The constructed IDAP is thus a platform for investigative data acquisition employing the Private Equi-join (PE) protocol – a commutative cryptography SPIR protocol. The thesis shows that the use of commutative cryptography in enquiries where multiple records need to be matched and then retrieved (m-out-of-n enquiries) is beneficial to the computational performance. However, the above customisations can be applied to other SPIR protocols in order to make them suitable for the investigative data acquisition process. These customisations, together with the findings of the literature review and the analysis of the field presented in this thesis, contribute to knowledge and can improve privacy in the investigative enquiries.

Nowadays every organisation is connected to the Internet and more and more of the world population have access to the Internet. The development of Internet permits to simplify the communication between the people. Now it is easy to have a conversation with people from everywhere in the world. This popularity of Internet brings also new threats like viruses, worm, Trojan, or denial of services. Because of this, companies start to develop new security systems, which help in the protection of networks. The most common security tools used by companies or even by personal users at home are firewalls, antivirus and now even Intrusion Detection System (IDS). Nevertheless, this is not enough so a new security system has been created as Intrusion Prevention Systems, which are getting more popular with the time .This could be defining as the blend between a firewall and an IDS. The IPS is using the detection capability of the IDS and the response capability of a firewall. Two main types of IPS exist, Network-based Intrusion Prevention System (NIPS) and Host-based Intrusion Prevention System (HIPS). The thirst should be set-up in front of critical resources as a web server while the second is set-up inside the host and so protect only this host. Different methodologies are used to evaluate IPSs but all of them have been produced by constructors or by organisms specialised in the evaluation of security devices. This means that no standard methodology in the evaluation of IPS exists. The utilisation of such methodology permits to benchmark system in an objective way and so it will be possible to compare the results with other systems. This thesis reviews different evaluation methodologies for IPS. Because of the lack of documentation around them the analysis of IDS evaluation methodology will be also done. This will permit to help in the creation of an IPS evaluation methodology. The evaluation of such security system is vast; this is why this thesis will only focus on a particular type of threat: Distributed Denial of Service (DDoS). The evaluation methodology will be around the capacity of an IPS to handle such threat. The produced methodology is capable of generating realistic background traffic along with attacking traffic, which are DDoS attacks. Four different DDoS attacks will be used to carry out the evaluation of a chosen IPS. The evaluation metrics are the packet lost that will be evaluated on two different ways because of the selected IPS. The other metrics are the time to respond to the attack, the available bandwidth, the latency, the reliability, the CPU load, and memory load. All experiment have been done in a real environment to ensure that the results are the more realistic possible. The selected IPS to carry out the evaluation of the methodology is the most popular and open-source Snort, which has been set-up in a Linux machine. The results shows that system is effective to handle a DDoS attack but when the rate of 6 000 pps of malicious traffic is reach Snort start to dropped malicious and legitimate packets without any differences. It also shows that the IPS could only handle traffic lower than 1Mbps. The conclusion shows that the produces methodology permits to evaluate the mitigation capability of an IPS. The limitations of the methodology are also explained. One of the key limitations is the impossibility to aggregate the background traffic with the attacking traffic. Furthermore, the thesis shows interesting future work that could be done as the automation of the evaluation procedure to simply the evaluation of IPSs.

Computer network security is key to the daily operations of an organisation, its growth and its future. It is unrealistic for an organisation to devote all of its resources to computer network security, but equally an organisation must be able to determine whether its security policy is achievable and under which criteria. Yet, it is not often possible for an organisation: to define its security policy, especially to fully comply with the laws of the land; ensure the actual implementation on network devices; and finally audit the overall system for compliance. This thesis argues that one of the obstacles to the complete realisation of such an Integrated Security Framework is the lack of deep understanding, in particular in terms of dynamic performance, of the network devices on which the security policy will be deployed. Thus, one novelty of this research is a Dynamic Evaluation Environment for Network Security that allows the identification of the strengths and weaknesses of networked security devices, such as in network firewalls. In turn, it enables organisations to model the dynamic performance impact of security policies deployed on these devices, as well as identifying the benefit of various implementation choices, or prioritisations. Hence, this novel evaluation environment allows the creation of instances of a network firewall dynamic performance model, and this modelling is part of the Integrated Security Framework, thus enabling it to highlight when particular security requirements cannot be met by the underlying systems, or how best to achieve the objectives. More importantly, perhaps, the evaluation environment enables organisations to comply with up-coming legislation that increases an organisation's legal cover, which demands consistent and scientific evidence of fitness prior to security incidents. Dynamic evaluations produce a large amount of raw data and this often does not allow for a comprehensive analysis and interpretation of the results obtained. Along with this, it is necessary to relate the data collected to a dynamic firewall performance model. To overcome this, this research proposes a unique formalisation of the inputs and outputs of the proposed model, and this, in turn, allows for performance analysis from multiple view-points, such as: the increase security requirements in the form of larger rule-set sizes; effects of changes in terms of the underlying network equipment; or the complexity of filtering. These view-points are considered as evaluation scenarios and also have unique formalisations. Evaluations focused on two types of network firewalls and key findings include the fact that strong security policy overhead can be kept acceptable on embedded firewalls provided that out-going filtering is used. Along with this, dynamic evaluation allows the identification of the additional performance impact of unoptimised configurations, and such findings complement work that focuses on the logical properties of network firewalls. Also, these evaluations demonstrate the need for scientific rigour as the data show that the embedded and software network firewalls evaluated have different areas of strengths and weaknesses. Indeed, it appears that software firewalls are not as affected as embedded firewalls by the complexity of filtering. On the other hand, the number of rules software firewalls enforce is the main performance factor, especially for high network speeds.

Recent advances in technology provided the ground for highly dynamic, mobile, infrastructure-less networks, namely, ad-hoc networks. Despite their enormous benefits, the full potential cannot be reached unless certain issues are resolved. These mainly involve routing, as the lack of an infrastructure imposes a heavy burden on mobile devices that must maintain location information and route data packets in a multi-hop fashion. Specifically, typical adhoc routing devices, such as Personal Digital Assistants (PDAs), are limited in respect to the available throughput, life-time, and performance, that these may provide, as routing elements. Thus, there is a need for metric-driven ad-hoc routing, that is, devices should be utilised for routing according to their fitness, as different device types significantly vary in terms of routing fitness. In addition, a concrete agent-based approach can provide a set of advantages over a non-agent-based one, which includes: better design practice; and automatic reconfigurability. This research work aims to investigate the applicability of stationary and mobile agent technology in multi-hop ad-hoc routing. Specifically, this research proposes a novel hybrid, metric-driven, agent-based routing protocol for multi-hop ad-hoc networks that will enhance current routing schemes. The novelties that are expected to be achieved include: maximum network performance, increased scalability, dynamic adaptation, Quality of Service (QoS), energy conservation, reconfigurability, and security. The underlying idea is based on the fact that stationary and mobile agents can be ideal candidates for such dynamic environments due to their advanced characteristics, and thus offer state of the art support in terms of organising the otherwise disoriented network into an efficient and flexible hierarchical structure, classifying the routing fitness of participating devices, and therefore allow intelligent routing decisions to be taken on that basis.

Security is one of the major issues in any network and on the Internet. It encapsulates many different areas, such as protecting individual users against intruders, protecting corporate systems against damage, and protecting data from intrusion. It is obviously impossible to make a network totally secure, as there are so many areas that must be protected. This thesis includes an evaluation of current techniques for internal misuse of computer systems, and tries to propose a new way of dealing with this problem. This thesis proposes that it is impossible to fully protect a computer network from intrusion, and shows how different methods are applied at differing levels of the OSI model. Most systems are now protected at the network and transport layer, with systems such as firewalls and secure sockets. A weakness, though, exists in the session layer that is responsible for user logon and their associated password. It is thus important for any highly secure system to be able to continually monitor a user, even after they have successfully logged into the system. This is because once an intruder has successfully logged into a system, they can use it as a stepping-stone to gain full access (often right up to the system administrator level). This type of login identifies another weakness of current intrusion detection systems, in that they are mainly focused on detecting external intrusion, whereas a great deal of research identifies that one of the main problems is from internal intruders, and from staff within an organisation. Fraudulent activities can often he identified by changes in user behaviour. While this type of behaviour monitoring might not be suited to most networks, it could be applied to high secure installations, such as in government, and military organisations. Computer networks are now one of the most rapidly changing and vulnerable systems, where security is now a major issue. A dynamic approach, with the capacity to deal with and adapt to abrupt changes, and be simple, will provide an effective modelling toolkit. Analysts must be able to understand how it works and be able to apply it without the aid of an expert. Such models do exist in the statistical world, and it is the purpose of this thesis to introduce them and to explain their basic notions and structure. One weakness identified is the centralisation and complex implementation of intrusion detection. The thesis proposes an agent-based approach to monitor the user behaviour of each user. It also proposes that many intrusion detection systems cannot cope with new types of intrusion. It thus applies Bayesian statistics to evaluate user behaviour, and predict the future behaviour of the user. The model developed is a unique application of Bayesian statistics, and the results show that it can improve future behaviour prediction than existing ARIMA models. The thesis argues that the accuracy of long-term forecasting questionable, especially in systems that have a rapid and often unexpected evolution and behaviour. Many of the existing models for prediction use long-term forecasting, which may not be the optimal type for intrusion detection systems. The experiments conducted have varied the number of users and the time interval used for monitoring user behaviour. These results have been compared with ARIMA, and an increased accuracy has been observed. The thesis also shows that the new model can better predict changes in user behaviour, which is a key factor in identifying intrusion detection. The thesis concludes with recommendations for future work, including how the statistical model could be improved. This includes research into changing the specification of the design vector for Bayesian. Another interesting area is the integration of standard agent communication agents, which will make the security agents more social in their approach and be able to gather information from other agents