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AI-based Static Application Security Testing Guide: Improved Threat Modeling for Detection, Classification & Prevention of Leading to Vulnerability Code Smells - Tapa blanda
Sinopsis
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.
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AI-based Static Application Security Testing Guide
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207997042
ISBN 13: 9786207997046
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AI-based Static Application Security Testing Guide
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ISBN 10: 6207997042
ISBN 13: 9786207997046
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AI-based Static Application Security Testing Guide
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ISBN 10: 6207997042
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786207997046
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AI-based Static Application Security Testing Guide : Improved Threat Modeling for Detection, Classification & Prevention of Leading to Vulnerability Code Smells
Publicado por
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207997042
ISBN 13: 9786207997046
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Taschenbuch. Condición: 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 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. Nº de ref. del artículo: 9786207997046
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