Clustering-XGB Based Dynamic Time Series Prediction

Sun, Haoxuan; Zhang, Kun; Wang, Tingting; Ma, Wanfeng; Zhao, Qinjun

doi:10.1007/978-3-030-94182-6_25

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is often used as it provides advantages such as easy model construction, strong universality and fast speed. Besides, it shows good performance comparable with or better than classical machine learning methods [25,38,42,43] with better efficiency [7].…”

Section: Introductionmentioning

confidence: 99%

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

Sun

Geng

Zhang

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving to the 6G networks, which leads to dramatically raised security issues. Using machine learning, including deep learning, to find out malicious network traffic is one of practical ways. Though much work has been done in this direction, we found little investigating the effect of using fused network conversation datasets to train and test models. Thus, this work proposes to check conversation dataset characteristics and find suitable ones to fuse into one dataset in order to improve the capability of malicious traffic and malware detection performance. The experiments using real data show that conditioned combination of datasets can be used to enhance algorithm performance and improve detection results. For this reason, it is recommended to profile datasets and conduct conditional fusion of network conversation datasets before using machine learning or deep learning. As the characterization is done using general statistical calculation, it is promising to be used for other domains too.

show abstract

Section: Introductionmentioning

confidence: 99%