AI-based Static Application Security Testing Guide: Improved Threat Modeling for Detection, Classification & Prevention of Leading to Vulnerability Code Smells - Malik Shah Jahan
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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 se ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Malik Shah Jahan |
|---|---|
| Julkaisija | LAP LAMBERT Academic Publishing |
| Julkaisuvuosi | 2024 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9786207997046 |