SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance

An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer

The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed. However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed. A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility of the model and the developed algorithm