Majdi Ben Saad

ABSTRACT With the emergence of High Speed Network (HSN), the manual intrusion alert detection become an extremely laborious and time-consuming task since it requires an experienced skilled staff in security fields and need a deep analysis. In addition, the batch model of alert management is no longer adequate given that labeling is a continuous time process since incoming intrusion alerts are often collected continuously in time. Furthermore, the static model is no longer appropriate due to the fluctuation nature of the number of alerts incurred by Internet traffic fluctuation nature. This paper proposes an efficient real time adaptive intrusion detection alert classifier dedicated for high speed network. Our classifier is based an online self-trained SVM algorithm with several learning strategies and execution modes. We evaluate our classifier against three different data-sets and the performance study shows an excellent results in term of accuracy and efficiency. The predictive local learning strategy presents a good tradeoff between accuracy and time processing. In addition, it does not involve a human intervention which make it an excellent solution that satisfy high speed network alert management challenges.

Abstract This paper studies the evolution of high performance computing (HPC) and its trends. It exposes the different architectures used in HPC, the common high-speed networks, the programming models, the communications models, and the communication libraries. ...

... Akhlaq, M., Alserhani, F., Subhan, A., Awan, IU, Mellor, J., & Mirchandani, P. (2010). High speed NIDS using dynamic cluster and comparator logic. In Proceedings of the IEEE 10th International Con-ference on Computer and Information Technology (pp. 575-81). Ammar, A., & ...