Abstract
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, and (3) Security issues. Among these, security vulnerability poses a major threat to modern multimedia systems. Therefore, it is imperative to carefully investigate the security issues that can endanger wireless and mobile communications. At present, multimedia security research mainly focuses on wireless traffic monitoring, wireless system attacks, and wireless and mobile security. In this paper, we have used the network attack-type, “Reconnaissance”, which contains two types of malicious activities: (1) OS scanning, and (2) Fuzzing. The goal of this paper is to quantify multimedia security risks due to Fuzzing by using various types of machine learning models. The highest accuracy i.e., 96.8%, is obtained using the XGBoost classifier, which is good compared to the existing models present in the literature. This is the first paper, to the best of our knowledge, that uses machine learning methods to differentiate between benign and malignant Fuzzing attacks.
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Conceptualization: [GSK] and [KM]; Methodology: [GSK] and [KM]; Software: [GSK] and [KM]; Validation: [SW]; Experiment Analysis: [GSK] and [KM]; Investigation: [SW]; Writing—Original Draft Preparation: [GSK] and [KM]; Writing—Review and Editing: [SW]; Supervision: [SW]; Project Administration: [SW]; Funding Acquisition: NA. All authors have read and agreed to the published version of the manuscript.
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Kashyap, G.S., Malik, K., Wazir, S. et al. Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing. Multimed Tools Appl 81, 36685–36698 (2022). https://doi.org/10.1007/s11042-021-11558-9
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DOI: https://doi.org/10.1007/s11042-021-11558-9