This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transforme...

One of the central challenges with computer security is determining the difference between normal and potentially harmful activity. For decades, developers have protected their systems using classical methods. However, the growth and complexity of computer systems or networks to protect require the development of automated and adaptive defensives tools. Promising solutions are emerging with bio...

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by t...

In this paper, we describe our approach to track 2 of the KDD Cup 2011. The task was to predict which 3 out of 6 candidate songs were positively rated – instead of not rated at all – by a user. The candidate items were not sampled uniformly, but according to their general popularity. We develop an adapted version of the Bayesian Personalized Ranking (BPR) optimization criterion [9] that takes t...

We develop an adapted version of the Bayesian Personalized Ranking (BPR) optimization criterion (Rendle et al., 2009) that takes the non-uniform sampling of negative test items — as in track 2 of the KDD Cup 2011 — into account. Furthermore, we present a modified version of the generic BPR learning algorithm that maximizes the new criterion. We use it to train ranking matrix factorization model...

One of the main problems in Network Intrusion Detection comes from constant rise of new attacks, so that not enough labeled examples are available for the new classes of attacks. Traditional Machine Learning approaches hardly address such problem. This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Lea...

Intrusion detection system has become a core component in computer network era. It is expanding day by day. That is why, there is a need for security from attackers, spammers and criminal enterprises as they are growing up with the expansion of Internet. An Intrusion Detection System is integrated with neural network using layered framework to build an effective computer network. This existing ...