Intrusion Detection Systems (IDSs) are security tools widely used in computer networks. While they seem to be promising technologies, they pose some serious drawbacks: When utilized in large and high traffic networks, IDSs generate high volumes of low-level alerts which are hardly manageable. Accordingly, there emerged a recent track of security research, focused on alert correlation, which ext...

With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of ...

This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision-making units (DMUs). Using a Malmquist index, DEA deter...

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

Data is mainly available in relational formats, so relational data mining receives a lot of interest. Propositionalisation consists in changing the representation of relational data in order to apply usual attribute-value learning systems. Data mining practitioners are not necessarily aware of existing works and try to propositionalise by hand. Unfortunately there exists some tempting pitfalls....

Automated techniques have proven useful for improving models of student learning even beyond the best human-generated models. There has been concern among the EDM community about whether small prediction improvements matter. We argue that they can be quite significant when they are interpretable and actionable, but the importance of generating meaningful, validated, and generalizable interpreta...

Two of the major goals in Educational Data Mining are determining students’ state of knowledge and determining whether students are affectively engaged with the task and in positive affective states. These two problems are usually examined separately and multiple methods have been proposed to solve each of them. However, little work has been done on tracing both of these states in parallel and ...