The development of entrepreneurial culture in a transition economy: an empirical model discussion.

This conceptual paper will focus on the presentation of the model developed from empirical, qualitative research covering 20 years of analysis on the relationship between culture and entrepreneurship in Poland. It is aimed at proposing a comprehensive framework that describes the development of entrepreneurial culture. In this empirical model culture is understood as a set of values and beliefs held by a social group that endorse and are conducive to entrepreneurial behaviour; while entrepreneurial behaviour is treated as an expected outcome and narrowed down to opening the company. The model proves that the differentiation between entrepreneurship (behaviour) and entrepreneurs (who demonstrate this behaviour) needs to be recognised in future research. The case of Poland offers a historical example, which can shed more light on the process of cultural change and the role of entrepreneurship and entrepreneurs in the development of entrepreneurial culture. In the case presented, the behaviour of entrepreneurs has been identified as the key factor leading to further development.

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.