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Jumping Language Models
Ošmera, Lubomír ; Kocman, Radim (referee) ; Meduna, Alexandr (advisor)
The main goal of this master thesis is introduction and investigation of extended version of jumping automata and grammars. New versions are primarily focused on bioinformatic applications - DNA computing. This thesis examine their power and other properties of new models and makes comparison with existing computer science models. Then thesis demontrates practical applications, specifically amino acid and protein detections inside DNA sequence and makes comparision with existing tools in DNA computing for example Mark´s probabilistic models.
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New Approaches Towards Automated XSS Flaw Detection
Steinhauser, Antonín ; Tůma, Petr (advisor) ; Vorobyov, Kostyantyn (referee) ; Bureš, Miroslav (referee)
Cross-site scripting (XSS) flaws are a class of security flaws particular to web applications. XSS flaws generally allow an attacker to supply affected web application with a malicious input that is then included in an output page without being properly encoded (sanitized). Recent advances in web applica- tion technologies and web browsers introduced various prevention mechanisms, narrowing down the scope of possible XSS attacks, but those mechanisms are usually selective and prevent only a subset of XSS flaws. Among the types of XSS flaws that are largely omitted are the context- sensitive XSS flaws. A context-sensitive XSS flaw occurs when the potentially malicious input is sanitized by the affected web application before being included in the output page, but the sanitization is not appropriate for the browser con- text of the sanitized value. Another type of XSS flaws, which is already better known, but still insufficiently prevented, are the stored XSS flaws. Applica- tions affected by the stored XSS flaws store the unsafe client input in persistent storage and return it in another HTTP response to (possibly) another client. Our work is focused on advancing state-of-the-art automated detection of those two types of XSS flaws using various analysis techniques ranging from purely static analysis to dynamic graybox analysis.
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Jumping Language Models
Ošmera, Lubomír ; Kocman, Radim (referee) ; Meduna, Alexandr (advisor)
The main goal of this master thesis is introduction and investigation of extended version of jumping automata and grammars. New versions are primarily focused on bioinformatic applications - DNA computing. This thesis examine their power and other properties of new models and makes comparison with existing computer science models. Then thesis demontrates practical applications, specifically amino acid and protein detections inside DNA sequence and makes comparision with existing tools in DNA computing for example Mark´s probabilistic models.
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