Learn More
How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition(More)
In this paper we introduce the idea of using behavioral biometrics in intrusion detection applications. We present a new biometrics-based technique, which can be used to detect intrusion without the need for any special hardware implementation and without forcing the user to perform any special actions. The technique is based on using " keystroke dynamics "(More)
  • Xiaomin Wu, B Eng, Margaret-Anne Storey, Hausi A Müller, Supervisor, Daniel M Germán +3 others
  • 2003
Version control is an important activity related to many phases of the software development lifecycle. Many version control systems have been developed to manage both software version history and associated human activities with the intent of producing higher quality software and supporting collaborative development. However, the vast information these(More)
Digital fingerprinting is an important but still challenging aspect of network forensics. This paper introduces an effective way to identify an attacker based on a strong behavioral biometric. We introduce a new passive digital fingerprinting technique based on keystroke dynamics biometrics. The technique is based on free text detection and analysis of(More)
BACKGROUND In 1986, the Government of Mali launched its Expanded Program on Immunization (EPI) with the goal of vaccinating, within five years, 80% of all children under the age of five against six target diseases: diphtheria, tetanus, pertussis, poliomyelitis, tuberculosis, and measles. The Demographic and Health Survey carried out in 2001 revealed that,(More)
In this paper, we propose a new unsupervised anomaly detection framework for network intrusions. The framework consists of a new clustering algorithm named I-means and new anomalousness metrics named IP Weights. I-means is an evolutionary extension of k-means algorithm that is composed by a revised k-means algorithm and an evolutionary approach to mixture(More)