M. S. Ramaiah Institute of Technology

About: M. S. Ramaiah Institute of Technology is a based out in . It is known for research contribution in the topics: Feature extraction & Photoluminescence. The organization has 2853 authors who have published 2434 publications receiving 23507 citations.

Intrusion detection system for grid computing using SNORT

Abstract: Because of distributed nature, grid computing environments are easy targets for intruders looking for possible vulnerabilities to exploit [1]. By impersonating legitimate users, the intruders can use a service's abundant resources maliciously. To combat attackers, intrusion-detection systems (IDSs) can offer additional security measures for these environments by investigating configurations, logs, network traffic, and user actions to identify typical attack behavior. However, IDS must be distributed to work in a grid computing environment. It must monitor each node and, when an attack occurs, alert other nodes in the environment. This kind of communication requires compatibility between heterogeneous hosts, various communication mechanisms, and permission control over system maintenance and updates. We present the problem of grid intrusion; analyze the requirements of a system to detect them. In this paper we are discussing how IDS can be implemented for grid computing environment.

Ear detection using active contour model

Abstract: Ear detection in face images, which is invariant to background and pose, is very challenging. This paper proposes a novel solution to this problem with the use of Snakes as active contour model. The proposed method encompasses two stages, namely, Snake-based Background Removal (SBR) and Snake-based Ear Localization (SEL). SBR is used to remove background from a face image and thereafter, SEL is used to localize the ear. A feedback is provided to this system with the use of Support Vector Machines (SVM) which classifies the region of the image localized by SEL as an ear or non-ear entity. Experimental results obtained by applying the proposed algorithm on two benchmark face databases, namely CMU-PIE and ColorFERET, show a significant improvement in Localization Rate, and a substantial reduction in the computation time.