Student attitudes to plagiarism and collusion within computer science

There is a widespread perception among staff in Computer Science that plagiarism is a major problem particularly in the form of collusion in programming exercises. While departments often make use of electronic detection measures, the time consumed prosecuting plagiarism offences remains a problem. As a result departments continue to seek ways to reduce the amount of plagiarism and collusion that occurs. This paper reports the findings of a questionnaire based study which attempted to assess the students' attitudes to the issues involved in the hope that such an understanding might result in practical measures for minimizing the problem. The study revealed that while students did understand the definition of plagiarism in its most extreme cases they were often confused about less clear-cut situations. Changes in the previous experience of incoming students meeting modules originally designed on the assumption that students already had some programming background and were equipped for self-directed study would also appear to be a contributory factor in the extent of collusion in programming exercises.

Real-time alert correlation with type graphs

The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be combined. These algorithms can be classified in to two broad categories namely scenario-graph approaches, which create an attack model starting from a vulnerability assessment and type-graph approaches which rely on an abstract model of the relations between attack types. Some research in to improving the efficiency of type-graph correlation has been carried out but this research has ignored the hypothesizing of missing alerts. Our work is to present a novel type-graph algorithm which unifies correlation and hypothesizing in to a single operation. Our experimental results indicate that the approach is extremely efficient in the face of intensive alerts and produces compact output graphs comparable to other techniques.