An QUIC Traffic Anomaly Detection Model Based on Empirical Mode Decomposition

Lei, Gang; Wu, Junyi; Gu, Keyang; Ji, Lejun; Cao, Yuanlong; Shao, Xun

doi:10.1109/hpsr54439.2022.9831335

Cited by 2 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, it can also effectively suppress or eliminate complex environments under the influence of noise. It has certain research prospects in complex neural networks [27,28], traffic prediction models [29][30][31], and network security [32][33][34].…”

Section: Eemd Algorithmmentioning

confidence: 99%

Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Cao

et al. 2023

IET Software

Self Cite

View full text Add to dashboard Cite

With the vigorous development of mobile Internet and Blockchain, the rapid and secure interaction of high-frequency data in the era of data explosion has gradually become an urgent need for people. Quick User Datagram Protocol Internet Connection Protocol (QUIC) can be perfectly integrated into the Blockchain due to its low latency and high security, providing a safe and reliable transmission service for the Blockchain system. However, QUIC-based distributed Blockchain systems are vulnerable to cyberattacks due to asymmetric encryption and timestamp, which seriously impacts security and fairness. Therefore, based on the self-similarity of QUIC network traffic, this study proposes an anomaly detection model for QUIC network traffic based on Ensemble Empirical Mode Decomposition and Approximate Entropy. The model obtains several intrinsic mode functions and a residual term by decomposing the network traffic, introducing Approximate Entropy to judge the complexity of each component, and using the Hurst parameter to determine the generation of abnormal traffic. Compared with traditional detection methods, the model can effectively overcome the problems of poor adaptive ability and low efficiency and has high accuracy, which can be used for attack detection and prevention of network nodes. K E Y W O R D S computer network security, data flow analysis, software reliability, transport protocols 1 | INTRODUCTION Blockchain technology is an integrated and innovative application of various IT technologies. It is a technical solution for storing, verifying, transmitting, and exchanging network data without relying on a third party and its distributed nodes. It has two core characteristics: data is difficult to tamper with and decentralisation. Based on these two characteristics, the information recorded by the Blockchain is more authentic and reliable, which can help solve the problem of people's mutual distrust [1, 2]. It solves the problem of data trust and security through distributed ledgers, consensus mechanisms, smart contracts, authorisation, and encryption [3]. In recent years, Blockchain technology is continuously empowering various fields. Figure 1 shows a 5G wireless network and Blockchainbased network architecture. The architecture collects real-time data through the Internet of Things and uploads the data and events to the Blockchain platform, enabling secure scheduling of terminal devices connected to the 5G network [4]. Integrating 5G and Blockchain technology will open the era of inter-chain integration, which can provide an efficient, safe, and fast service experience. With the continuous development This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract