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A key component to the structuring effects of neoliberalism is its impact on the subjectivity of citizens (Brown, 2003). Individuals are increasingly induced to act as self-disciplining, self-transforming entrepreneurial citizens, fully responsible for their own successes and failures (Harvey, 2005). Viewed in tandem with the changing landscape of employment under post-Fordism, women have come to be positioned as ideal neoliberal citizens and workers (Adkins, 1999, Gill and Scharff, 2011, Harris, 2006, Morini, 2010). Women possess a cultural legacy of self- transformation and self-discipline, and their 'natural' feminine characteristics align with expectations of contemporary affective and emotional labour. Within this context, a narrative abounds that women can make-over work, to better suit their passions and interests. Etsy.com is the largest digital craft-selling platform worldwide and promises its largely female workforce a means towards pleasurable, self-actualising business ownership. Through an analysis of twenty-one semi-structured interviews, this thesis explores the lived realities of women's experiences of such freelance employment, via the Etsy.com platform. I will argue that women engaged in Etsy-based work must reconcile a complex web of competing cultural, economic, and social demands, which centre on contradictory notions of ideal femininity. Moreover, Etsy.com itself operates as a structuring force, that iterates contradictory gendered ideals of 'successful' femininity. How such contradictions are navigated is dependent not only on individual women's entrepreneurial capacities but access to, and capacity to utilise, wider forms of capital.

Due to the rapid development of the Internet, modern daily behaviour has become more efficient and convenient. The Internet has become an indispensable element in our daily life, providing significant resources to people whether for play, work or education. In addition, with the increased universality of mobile devices, a magnitude of services is at our fingertips, the efficiency of our life or work has improved. However, the negative side of this is the increase in cybercrimes, with large losses for both individuals and enterprises. Phishing is currently defined as a criminal mechanism employing both social engineering and technical subterfuge to gather any useful information such as user personal data or financial account credentials. Phishing threats have been in existence for many years, since the establishment of the Internet, and they have continuously evolved and increased in application. So far, phishing attacks have accounted for a large proportion of all malicious attacks, and they are a globally growing threat with an increasing frequency of known attacks. Phishing attacks are a major current cyber threat as they are always cheap to produce and easy to deploy, in particular, due to the development of E-commerce, either to an individual user or organization. For the individual, sensitive credentials are always of interest to phishers due to the development of E-commerce. For an enterprise, a successful phishing attack, such as a subdomain takeover attack, may affect their organization's reputation as well as cause financial loss. Currently, most security vendors have been using different approaches to prevent phishing attacks. However, these solutions cannot keep up with the constant updating of phishing websites. In this thesis, web phishing attack types are classified into three different categories, from the shallower to the deeper. They are General Phishing Attack, Advanced Phishing Attack and Subdomain Takeover Attacks. The purpose of this thesis is to present an effective mitigation to defend against these phishing threats. From the shallower approach to a deeper, more complex approach, according to our defined categories of phishing threats, the specific mitigations and contributions are presented.

This thesis considers information leakage in cloud virtually isolated networks. Virtual Network (VN) Isolation is a core element of cloud security yet research literature shows that no experimental work, to date, has been conducted to test, discover and evaluate VN isolation data leakage. Consequently, this research focussed on that gap. Deep Dives of the cloud infrastructures were performed, followed by (Kali) penetration tests to detect any leakage. This data was compared to information gathered in the Deep Dive, to determine the level of cloud network infrastructure being exposed. As a major contribution to research, this is the first empirical work to use a Deep Dive approach and a penetration testing methodology applied to both CloudStack and OpenStack to demonstrate cloud network isolation vulnerabilities. The outcomes indicated that Cloud manufacturers need to test their isolation mechanisms more fully and enhance them with available solutions. However, this field needs more industrial data to confirm if the found issues are applicable to non-open source cloud technologies. If the problems revealed are widespread then this is a major issue for cloud security. Due to the time constraints, only two cloud testbeds were built and analysed, but many potential future works are listed for analysing more complicated VN, analysing leveraged VN plugins and testing if system complexity will cause more leakage or protect the VN. This research is one of the first empirical building blocks in the field and gives future researchers the basis for building their research on top of the presented methodology and results and for proposing more effective solutions.

This thesis presents the Name-based Mobile Ad-hoc Network (nMANET) approach to content distribution that ensure and enables responsible research on applying named data networking protocol in mobile ad-hoc networks. The test framework of the nMANET approach allows reproducibility of experiments and validation of expected results based on analysis of experimental data. The area of application for nMANETs is the distribution of humanitarian information in emergency scenarios. Named-Data Networking (NDN) and ad-hoc mobile communication allow exchange of emergency information in situations where central services such as cellular towers and electric systems are disrupted. The implemented prototype enables researchers to reproduce experiments on content distribution that consider constraints on mobile resources, such as the remaining power of mobile devices and available network bandwidth. The nMANET framework validates a set of experiments by measuring network traffic and energy consumption from both real mobile devices and those in a simulated environment. Additionally, this thesis presents results from experiments in which the nMANET forwarding strategies and traditional wireless services, such as hotpost, are analysed and compared. This experimental data represents the evidence that supports and validates the methodology presented in this thesis. The design and implementation of an nMANET prototype, the Java NDN Forwarder Daemon (JNFD) is presented as a testing framework, which follows the principles of continuous integration, continuous testing and continuous deployment. This testing framework is used to validate JNFD and IP-based technologies, such as HTTP in a MANET using the OLSR routing protocol, as well as traditional wireless infrastructure mode wireless. The set of experiments executed, in a small network of Android smart-phones connected in ad-hoc mode and in a virtual ad-hoc network simulator show the advantages of reproducibility using nMANET features. JNFD is open source, all experiments are scripted, they are repeatable and scalable. Additionally, JNFD utilises real GPS traces to simulate mobility of nodes during experiments. This thesis provides experimental evidence to show that nMANET allows reproducibility and validation of a wide range of future experiments applying NDN on MANETs.

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model's accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold.

With the recent spate of data loss breaches from industry and commerce, especially with the large number of Advanced Persistent Threats, companies are increasing their network boundary security. As network defences are enhanced through the use of Data Loss Prevention systems (DLP), attackers seek new ways of exploiting and extracting confidential data. This is often done by internal parties in large-scale organisations through the use of steganography. The successful utilisation of steganography makes the exportation of confidential data hard to detect, equipped with the ability of escaping even the most sophisticated DLP systems. This thesis provides two effective solutions to prevent data loss from effective LSB image steganographic behaviour, with the potential to be applied in industrial DLP systems.