[Show abstract][Hide abstract] ABSTRACT: As a crucial issue in computer network security, anomaly detection is receiving more and more attention from both application and theoretical point of view. In this paper, by introducing boosting technique, a novel anomaly detection scheme is proposed. On the whole, the proposed scheme is based on Ada-Boost and can be viewed as an extension of Ada-Boost in terms of both probability density estimation (PDE) and confidence area estimation (CAE). Different kinds of base learners are adopted and investigated in the proposed scheme. Systematic experimental results on DARPA 1999 dataset validate the effectiveness of the proposed scheme.
[Show abstract][Hide abstract] ABSTRACT: Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes high accurate prediction difficult in this paper, boosting is introduced into traffic prediction by considering it as a classical regression problem. A new scheme together with its adaptive version is proposed to update weight distribution. The new scheme controls the update rate by a parameter, while its adaptive version introduces no extra parameter and is adaptive to the training error of basic regressors and the current iteration number. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of our method.