Understanding bystanders’ willingness to Intervene in traditional and cyberbullying scenarios.

Bullying incidents in traditional and online settings are a cause for concern to many parties. The goal of the current study was to explore the extent to which a bystander would intervene in a bullying incident and the degree to which this behavior is influenced by group size (the number of other witnesses), the setting (traditional or cyberbullying), and gender of the victim. Using an online survey method, participants were presented with eight bullying scenarios, each of which involved verbal bullying of a victim. Participants (N = 82) were asked to report how likely they would be to intervene in each of these scenarios. Results showed that female victims were more likely to be helped than male victims. Furthermore, female participants were more willing to intervene than the male participants in the cyberbullying scenarios. Altruism was a positive predictor of participants’ willingness to intervene. The present findings suggest that certain gender differences in helping behavior may depend on the context in which bullying is observed (traditional or cyberbullying).

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.