Novel information sharing syntax for data sharing between police and community partners, using role-based security.

The exchange of information between the police and community partners forms a central aspect of effective community service provision. In the context of policing, a robust and timely communications mechanism is required between police agencies and community partner domains, including: Primary healthcare (such as a Family Physician or a General Practitioner); Secondary healthcare (such as hospitals); Social Services; Education; and Fire and Rescue services. Investigations into high-profile cases such as the Victoria Climbié murder in 2000, the murders of Holly Wells and Jessica Chapman in 2002, and, more recently, the death of baby Peter Connelly through child abuse in 2007, highlight the requirement for a robust information-sharing framework. This paper presents a novel syntax that supports information-sharing requests, within strict data-sharing policy definitions. Such requests may form the basis for any information-sharing agreement that can exist between the police and their community partners. It defines a role-based architecture, with partner domains, with a syntax for the effective and efficient information sharing, using SPoC (Single Point-of-Contact) agents to control in-formation exchange. The application of policy definitions using rules within these SPoCs is inspired by network firewall rules and thus define information exchange permissions. These rules can be imple-mented by software filtering agents that act as information gateways between partner domains. Roles are exposed from each domain to give the rights to exchange information as defined within the policy definition. This work involves collaboration with the Scottish Police, as part of the Scottish Institute for Policing Research (SIPR), and aims to improve the safety of individuals by reducing risks to the community using enhanced information-sharing mechanisms.

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.