Securing the Web: Machine Learning for CSRF Vulnerability Detection - Reddy, Pallavi
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Web Vulnerability Detection of Cross-Site Request Forgery Using Machine Learning Algorithm" is a book that focuses on the detection and prevention of Cross-Site Request Forgery (CSRF) vulnerabilities in web applications. The book presents a precise and practical approach to leveraging machine learning algorithms for identifying and mitigating these web security threats. It offers insights into the risks associated with CSRF attacks, explains the fundamentals of machine learning, and demonstrates how machine learning algorithms can be trained to detect and prevent CSRF vulnerabilities in…mehr

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Produktbeschreibung
Web Vulnerability Detection of Cross-Site Request Forgery Using Machine Learning Algorithm" is a book that focuses on the detection and prevention of Cross-Site Request Forgery (CSRF) vulnerabilities in web applications. The book presents a precise and practical approach to leveraging machine learning algorithms for identifying and mitigating these web security threats. It offers insights into the risks associated with CSRF attacks, explains the fundamentals of machine learning, and demonstrates how machine learning algorithms can be trained to detect and prevent CSRF vulnerabilities in real-time. This book is a valuable resource for web developers, security professionals, and researchers interested in fortifying web applications against CSRF attacks using advanced machine learning techniques.
Autorenporträt
R.Pallavi Reddy est professeur adjoint au département d'informatique et d'ingénierie de l'Institut de technologie et de sciences G. Narayanamma (pour les femmes), Hyderabad. Elle a 20 ans d'expérience dans l'enseignement. Elle a une bonne expérience de la recherche dans le domaine de l'apprentissage automatique et de l'intelligence artificielle.