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We present a publicly available, OWL-based ontology of information security which models assets, threats, vulnerabilities, countermeasures and their relations. The ontology can be used as a general vocabulary, roadmap, and extensible dictionary of the domain of information security. With its help, users can agree on a common language and definition of terms and relationships. In addition to browsing for information, the ontology is also useful for reasoning about relationships between its entities, for example, threats and countermeasures. The ontology helps answer questions like: Which countermeasures detect or prevent the violation of integrity of data? Which assets are protected by SSH? Which countermeasures thwart buffer overflow attacks? At the moment, the ontology comprises 88 threat classes, 79 asset classes, 133 countermeasure classes and 34 relations between those classes. We provide the means for extending the ontology, and provide examples of the extendibility with the countermeasure classes ‘memory protection’ and ‘source code analysis’. This article describes the content of the ontology as well as its usages, potential for extension, technical implementation and tools for working with it.

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Rapidly gaining information superiority is vital when fighting an enemy, but current computer forensics tools, which require file headers or a working file system to function, do not enable us to quickly map out the contents of corrupted hard disks or other fragmented storage media found at crime scenes. The lack of proper tools slows down the hunt for information, which would otherwise help in gaining the upper hand against IT based perpetrators. To address this problem, this paper presents an algorithm which allows categorization of data fragments based solely on their structure, without the need for any meta data. The algorithm is based on measuring the rate of change of the byte contents of digital media and extends the byte frequency distribution based Oscar method presented in an earlier paper. The evaluation of the new method shows a detection rate of 99.2 %, without generating any false positives, when used to scan for JPEG data. The slowest implementation of the algorithm scans a 72.2 MB file in approximately 2.5 seconds and scales linearly.

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