Moving to manage: A mixed methods study of later life relocation into supported housing.

This study describes relocation experiences of older people moving to supported housing in Scotland focusing on the nature of support. Using mixed methods, Phase one involved a Scottish cross-sectional survey of all people aged 65 and over moving into Coburg (Scotland) Housing Association supported accommodation during the first six months of 2008. A total of 122 respondents were included in the survey (59% response rate). People moved locally at advanced ages with moderate disability levels to achieve more manageable housing and support, suggesting ‘assistance migration’. Expectations were high, with many seeing it as a new start in life and generally positive views of moving were reported. In Phase two, five in-depth multiple-perspective longitudinal case studies were conducted to explore the experience of relocation into supported housing. In each case an older person, primary carer and the housing manager - all women – were interviewed over six months following relocation. Analysis was undertaken using a thematic framework approach (Ritchie et al., 2003). Findings suggested older women acted with agency to adapt to their new lives; recreating ‘normality’ through organising space and routines. It is argued that returning to normality formed the overarching objective of the older women as they sought to feel ‘in place’. Responsibilities for meeting assistance needs were often implicit, contested and shifting, leading to fragile, uncertain and transitory arrangements. Drawing on recent advances in developmental psychology it is argued ‘longings’ of older people, and others, to achieve an optimal life can relate and motivate towards actions such as relocation. Yet, personal ‘longings’ can be prioritised differently and may result in disputes over goal setting and ways needs are met. Further, utopian ideals must be reconciled with the reality of daily life. Policy and practitioners could adopt broader, dignity based objectives to assist older people to identify ways of aiding such reconciliation.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.