Sharif Digital Repository

Although multiple techniques have been proposed with the goal of minimizing the semantic gap in virtual machine introspection, most concentrate on passive observation of the internal state, while there are also a number of proposals with which active modification of the VM's internal state is made possible. However there are issues when modifications are applied, such as keeping a consistent kernel state and avoiding a crash. In this paper we propose Oxpecker, a VMI platform for transactional modification. The out-of-VM read access allows an introspector to detect malware in the guest OS (e.g., rootkit) and the transactional write access allows Oxpecker to reliably neutralize the detected... 

Although multiple techniques have been proposed with the goal of minimizing the semantic gap in virtual machine introspection, most concentrate on passive observation of the internal state, while there are also a number of proposals with which active modification of the VM's internal state is made possible. However there are issues when modifications are applied, such as keeping a consistent kernel state and avoiding a crash. In this paper we propose Oxpecker, a VMI platform for transactional modification. The out-of-VM read access allows an introspector to detect malware in the guest OS (e.g., rootkit) and the transactional write access allows Oxpecker to reliably neutralize the detected... 

A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple and 2) computationally efficient. In this article, a content-based image retrieval and annotation architecture is proposed. Its attitude is decreasing the semantic gap by partitioning the image to its semantic regions and using... 

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval (CBIR) systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube (HC) in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the...