Existing anti-malware products usually use signature-based techniques as their main detection engine. Although these methods are very fast, they are unable to provide effective protection against newly discovered malware or mutated variant of old malware. Heuristic approaches are the next generation of detection techniques to mitigate the problem. These approaches aim to improve the detection rate by extracting more behavioral characteristics of malware. Although these approaches cover the disadvantages of signature-based techniques, they usually have a high false positive, and evasion is still possible from these approaches. In this paper, we propose an effective and efficient heuristic technique based on static analysis that not only detect malware with a very high accuracy, but also is robust against common evasion techniques such as junk injection and packing. Our proposed system is able to extract behavioral features from a unique structure in portable executable, which is called dynamic-link library dependency tree, without actually executing the application
Role Based Access Control (RBAC) is the most widely used model for access control due to the ease of administration as well as economic benefits it provides. In order to deploy an RBAC system, one requires to first identify a complete set of roles. This process, known as role engineering, has been identified as one of the costliest tasks in migrating to RBAC. In this paper, we propose a top-down role engineering approach and take the first steps towards using natural language processing techniques to extract policies from unrestricted natural language documents. Most organizations have high-level requirement specifications that include a set of access control policies which describes allowable operations for the system. However, it is very time consuming, labor-intensive, and errorprone to manually sift through these natural language documents to identify and extract access control policies. Our goal is to automate this process to reduce manual efforts and human errors. We apply natural language processing techniques, more specifically semantic role labeling to automatically extract access control policies from unrestricted natural language documents, define roles, and build an RBAC model. Our preliminary results are promising and by applying semantic role labeling to automatically identify predicate-argument structure, and a set of predefined rules on the extracted arguments, we were able correctly identify access control policies with a precision of 75%, recall of 88%, and F1 score of 80%.
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