I film therefore I am: process and participation, networks and knowledge – examples from Scottish community media projects.

This chapter examines community media projects in Scotland as social processes that nurture knowledge through participation in production. A visual and media anthropology framework (Ginsburg, 2005) with an emphasis on the social context of media production informs the analysis of community media. Drawing on community media projects in the Govan area of Glasgow and the Isle of Bute, the techniques of production foreground “the relational aspects of filmmaking” (Grimshaw and Ravetz, 2005: 7) and act as a catalyst for knowledge and networks of relations embedded in time and place. Community media is defined here as a creative social process, characterised by an approach to production that is multi-authored, collaborative and informed by the lives of participants, and which recognises the relevance of networks of relations to that practice (Caines, 2007: 2). As a networked process, community media production is recognised as existing in collaboration between a director or producer, such as myself, and organisations, institutions and participants, who are connected through a range of identities, practices and place. These relations born of the production process reflect a complex area of practice and participation that brings together “parallel and overlapping public spheres” (Meadows et al., 2002: 3). This relates to broader concerns with networks (Carpentier, Servaes and Lie, 2003; Rodríguez, 2001), both revealed during the process of production and enhanced by it, and how they can be described with reference to the knowledge practice of community media.

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.