.png){kind=icon}
返回
Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-11558-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Cybersecurity
Fuzzing
Gradient boosting
Multimedia
Machine learning
Vulnerability
Random forest algorithm
XGBoost
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Geologic continuous casting below continental and deep-sea detachment faults and at the striated extrusion of Sacsayhuamán, Peru.png)
Geology
GeologyIF0
PREAI
分享收藏
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection-Application to Network Intrusion Detection用于逐点和集体异常检测的随机分区森林-在网络入侵检测中的应用
OAAI
OAAI分享收藏分享收藏Effects of proton irradiation on dc characteristics of InAlN/GaN high electron mobility transistors质子辐照对InAlN/GaN高电子迁移率晶体管直流特性的影响
PREAI
分享收藏
查看更多内容