We organized the KDD cup 2009 around a marketing problem with the goal of identifying data mining techniques capable of rapidly building predictive models and scoring new entries on a large database. Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offered the opportunity to work on large marketing databases from the French Telecom company...

We organized the KDD cup 2009 around a marketing problem with the goal of identifying data mining techniques capable of rapidly building predictive models and scoring new entries on a large database. Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offered the opportunity to work on large marketing databases from the French Telecom company...

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algori...

Technology advances have made the ability to collect large amounts of data easier than ever before. These massive datasets provide both opportunities and challenges for many fields and education is no different. Understanding how to deal with extreme amounts of student data in the EDM field is a growing problem. The 2010 KDD Cup Competition, titled "Educational Data Mining Challenge", included ...

In recent fast growing internet world network security is become an essential need. Latterly one of the most popular research topics in network security is Intrusion Detection Systems (IDSs) which try to keep safe our network from intruders. This paper describes an IDS using Hybrid Ant Colony Genetic algorithm (GAAPI). It is hybridization of a special class of ACO called API and a RealCoded Gen...

Most existing network intrusion detection systems use signature-based methods which depend on labeled training data. This training data is usually expensive to produce due to cost of laboratory set up, experienced or knowledge person and non availability of ready software tool. Above all, these methods have difficulty in detecting new or unknown types of attacks. Using unsupervised anomaly dete...

The main objective of Intrusion Detection and Prevention Systems is to provide a method detecting preventing malicious behaviors in network system minimize the harm caused by attackers. In this article, survey techniques applied for identification classification attacks based on KDD Cup’99 DARPA data set discussed, from open issues new proficient called SNORT-XSS algorithm anticipated implement...

A small subset of machine learning algorithms, mostly inductive learning based, applied to the KDD 1999 Cup intrusion detection dataset resulted in dismal performance for user-to-root and remote-to-local attack categories as reported in the recent literature. The uncertainty to explore if other machine learning algorithms can demonstrate better performance compared to the ones already employed ...

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the eld of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorith...