This research proposes a novel automatic method (termed Auto-Sign) for extracting unique signatures of malware executables to be used by high-speed malware filtering devices based on deep-packet inspection and operating in real-time. Contrary to extant string and tokenbased signature generation methods, we implemented Auto-Sign an automatic signature generation method that can be used on large-size malware by disregarding signature candidates which appear in benign executables. Results from experimental evaluation of the proposed method suggest that picking a collection of executables which closely represents commonly used code, plays a key role in achieving highly specific signatures which yield low false positives.
KNAVE-II is an intelligent interface to a distributed web-based architecture that enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Based on prior studies, we have defined a set of requirements for provision of a service for interactive exploration of time oriented clinical data. The main requirements include the visualization, interactive exploration and explanation of both raw data and multiple levels of concepts abstracted from these data; the exploration of clinical data at different levels of temporal granularity along both absolute (calendar-based) and relative (clinically meaningful) time-lines; the exploration and dynamic visualization of the effects of simulated hypothetical modifications of raw data on the derived concepts; and the provision of generic services (such as statistics, documentation, fast search and retrieval of clinically significant concepts, amongst others). KNAVE-II has been implemented and is currently evaluated by expert clinicians in several medical domains, such as oncology, involving monitoring of chronic patients.
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