Learn More
In this paper, we propose a new image retrieval method consisting of shape feature data. In this approach we assume the images are classified into single objects through other known classification methods such as K-means and SVM algorithms. From collected binary object images, we develop a new algorithm that has less computation but equal efficiency as(More)
Anomaly detection provides automated detection of unauthorized intrusion into a computer system by creating a normal profile of the system's behavior, then raising an alert when the system's behavior does not fit the system's normal profile. Approaches to anomaly detection that focus on investigating user's behavior typically assume that a user's command(More)
In this paper, we propose an image reconstruction method utilizing an optimized adaptive interpolation kernel along with a 2D M-channel perfect reconstruction filter bank(M-ch PR-FB) structure. In particular, the proposed approach leads to sharper reconstructed images than a direct conversion, still preserving high frequency components of the original image(More)
Masquerade detection discovers suspicious activities in a computer system by creating userspsila normal profiles, then raising an alert when the audited behavior does not fit. We propose to apply the SVM algorithm to the concurrently employed patterns that have been weighted according to their frequencies in order to identify masquerading attacks. Our(More)
Our research addresses constructing a dynamic normal profile for anomaly detection systems without requiring time-consuming retraining. We propose to continuously update normal profiles by keeping the most recently employed patterns whose amount is dynamically determined. Active window adjustment through a simplified concept drift algorithm helps to keep(More)
  • 1