Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing

• Published in: Multimedia tools and applications Vol. 81; no. 25; pp. 36685 - 36698
• Main Authors: Kashyap, Gautam Siddharth, Malik, Karan, Wazir, Samar, Khan, Rijwan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2022; Springer Nature B.V
• Subjects: 1219: Multimedia Security Based on Quantum Cryptography and Blockchain; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Machine learning; Multimedia; Multimedia Information Systems; Security; Special Purpose and Application-Based Systems; Wireless communications; Multimedia; Random forest algorithm; XGBoost; Machine learning; Fuzzing; Cybersecurity; Vulnerability; Gradient boosting

There have been considerable advancements in multimedia technologies over the past 5 years. It has been observed that state-of-the-art multimedia systems face three broad categories of challenges: (1) Dependency on continuous network connections, (2) Data-sharing applications & collaboration, an...