Ai Based Static Application Security by Shah Jahan (11 results)
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Taschenbuch. Condition: Neu. AI-based Static Application Security Testing Guide | Improved Threat Modeling for Detection, Classification & Prevention of Leading to Vulnerability Code Smells | Malik Shah Jahan | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207997046 | Verantwortliche Person für die EU…: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Language: English
Published by LAP LAMBERT Academic Publishing Aug 2024, 2024
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch.
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Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Published by LAP LAMBERT Academic Publishing Aug 2024, 2024
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Code smells are usually ignored as they are neither a bug, nor a vulnerability. Quality engineers and, specially, security architects ignore them. As some of the code smells may lead towards vulnerability which may further be exploited…by the hackers, therefore, such vulnerable code smells must be considered and further mitigated by threat modelers. In order to provide a repository of such code smells to security designers, a process had been devised and experimented. During the execution, various web applications had been passed through SAST and resulting code smells had been extracted and then inserted into a new dataset via Python. Later on, the code smells deposited in the dataset had been classified into various categories. Finally, machine learning algorithms had been assessed through WEKA and the fastest as well the most accurate algorithm had been selected. Current security standards do not ensure mitigation of threats caused by leading-to-vulnerability code smells, till to date. Typically, threat modelers assess security of a system through modeling threats via CIA, STRIDE and LINDDUN standards on its DFD and various architectural / infrastructural diagrams.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Code smells are usually ignored as they are neither a bug, nor a vulnerability. Quality engineers and, specially, security architects ignore them. As some of the code smells may lead towards vulnerability which may further be exploited b…y the hackers, therefore, such vulnerable code smells must be considered and further mitigated by threat modelers. In order to provide a repository of such code smells to security designers, a process had been devised and experimented. During the execution, various web applications had been passed through SAST and resulting code smells had been extracted and then inserted into a new dataset via Python. Later on, the code smells deposited in the dataset had been classified into various categories. Finally, machine learning algorithms had been assessed through WEKA and the fastest as well the most accurate algorithm had been selected. Current security standards do not ensure mitigation of threats caused by leading-to-vulnerability code smells, till to date. Typically, threat modelers assess security of a system through modeling threats via CIA, STRIDE and LINDDUN standards on its DFD and various architectural / infrastructural diagrams.
