Technique for Denial-of-Service Intrusion Detection Using Non-Subsampled Shearlet Transform
Lei Yang, Jing Ma · Applied Mechanics and Materials · 2015
In this paper, an efficient anomaly analysis method that is proved to be more efficient and less complex than the existing techniques has been proposed. The approach relies on monitoring the security state by using a set of accurate metrics. The Non-Subsampled Shearlet Transform (NSST) is used to decompose these metrics. Attacks are viewed as singularities that arise in some specific points of time. Therefore, the anomaly detection process is performed through processing the signals representing the metrics. Experimental results indicate that the proposed technique is effective and promising.