A comparative approach to web evaluation and website evaluation methods.

There is still a lack of an engineering approach for building Web systems, and the field of measuring the Web is not yet mature. In particular, there is an uncertainty in the selection of evaluation methods, and there are risks of standardizing inadequate evaluation practices. It is important to know whether we are evaluating the Web or specific website(s). We need a new categorization system, a different focus on evaluation methods, and an in-depth analysis that reveals the strengths and weaknesses of each method. As a contribution to the field of Web evaluation, this study proposes a novel approach to view and select evaluation methods based on the purpose and platforms of the evaluation. It has been shown that the choice of the appropriate evaluation method(s) depends greatly on the purpose of the evaluation.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Participation Space Studies: a socio-technical exploration of activist and community groups’ use of online and offline spaces to support their work

Participation Space Studies explore eParticipation in the day-to-day activities of local, citizen-led groups, working to improve their communities. The focus is the relationship between activities and contexts. The concept of a participation space is introduced in order to reify online and offline contexts where people participate in democracy. Participation spaces include websites, blogs, email, social media presences, paper media, and physical spaces. They are understood as sociotechnical systems: assemblages of heterogeneous elements, with relevant histories and trajectories of development and use. This approach enables the parallel study of diverse spaces, on and offline. Participation spaces are investigated within three case studies, centred on interviews and participant observation. Each case concerns a community or activist group, in Scotland. The participation spaces are then modelled using a Socio-Technical Interaction Network (STIN) framework (Kling, McKim and King, 2003). The participation space concept effectively supports the parallel investigation of the diverse social and technical contexts of grassroots democracy and the relationship between the case-study groups and the technologies they use to support their work. Participants’ democratic participation is supported by online technologies, especially email, and they create online communities and networks around their goals. The studies illustrate the mutual shaping relationship between technology and democracy. Participants’ choice of technologies can be understood in spatial terms: boundaries, inhabitants, access, ownership, and cost. Participation spaces and infrastructures are used together and shared with other groups. Non-public online spaces, such as Facebook groups, are vital contexts for eParticipation; further, the majority of participants’ work is non-public, on and offline. It is informational, potentially invisible, work that supports public outputs. The groups involve people and influence events through emotional and symbolic impact, as well as rational argument. Images are powerful vehicles for this and digital images become an increasingly evident and important feature of participation spaces throughout the consecutively conducted case studies. Collaboration of diverse people via social media indicates that these spaces could be understood as boundary objects (Star and Griesemer, 1989). The Participation Space Studies draw from and contribute to eParticipation, social informatics, mediation, social shaping studies, and ethnographic studies of Internet use.