Network Traffic Anomalies Detection Using a Fixing Method of Multifractal Dimension Jumps in a Real-Time Mode

Sheluhin, Oleg I.; Lukin, I. Yu.

doi:10.3103/s0146411618050115

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…This feature has also been proved to be the basis for judging normal flow in subsequent studies [18,19]. Sheluhin et al [20] used a multiscale analysis method combined with wavelet analysis to study the multi-fractal characteristics of network traffic and realised the generation of abnormal traffic by monitoring fractal dimension jumps. Pei et al [21] proposed a usercustomisable personalised anomaly detection framework to detect the generation of abnormal traffic in the network and also presented an anomaly detection method with long shortterm memory.…”

Section: Related Workmentioning

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

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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.

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Section: Related Workmentioning

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

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“…Data mining and machine learning have been applied for automated network traffic analysis. However, shallow learning models like support vector machines (SVMs) and random forests have limited capability in handling complex networks with dynamic behavior [3]. Deep learning has become a powerful force in many domains.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Arjunan

2024

IJRASET

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In light of the increasing sophistication of cyberattacks and the rapid growth in network traffic, it is essential to detect network traffic anomalies or intrusions as they occur. Manual inspection is inefficient due to the large volume, speed, and variety network traffic data. This paper suggests using deep learning techniques in order to build intelligent models which can detect network traffic anomalies automatically within big data environments. We present a framework for anomaly detection using long-short-term memory models (LSTM) and convolutional neural network (CNN). The models are based on data extracted from packet captures. The models are evaluated on benchmark intrusion datasets as well as a large scale real network traffic dataset. The results show that deep learning models are able to detect anomalies more effectively than traditional shallow learning methods. Models can handle high-volume streaming data with low latency and in real time. To improve detection efficiency, we also propose optimization methods such as model compression and transfer learning. This work shows the effectiveness of deep learning for real-time anomaly detection within big data environments

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“…This study aims to analyze the various techniques used for detecting network anomalies and their effectiveness in improving the security of networks. [5], [6] The KDD NSL dataset, which is a widely used research tool in network security, contains traffic data that has been simulated to attack different types of DoS attacks. This was created by NIST to support research related to intrusion detection systems.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Network Traffic using Machine Learning and Deep Learning Techniques

Rana¹

2019

TURCOMAT

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Due to the rise of sophisticated cyberattacks, network security has become an increasingly important field. One of the most common threats to the security of networks is network anomalies, which can cause system malfunctions and prevent them from working properly. Detecting such anomalies is very important to ensure the continued operation of the network. Deep learning and machine learning algorithms have demonstrated their ability to detect network anomalies, but their effectiveness is still not widely known. This paper presents an evaluation of the performance of three algorithms against the KDD-NSL dataset. This study aims to provide a comprehensive analysis of the various techniques used in deep learning and machine learning to detect network anomalies. It will also help improve the security of networks. The paper presents an evaluation of the performance of three algorithms against the KDD-NSL dataset. The three algorithms are the Support Vector Machine, the Random Forest, and the Artificial Neural Network. They will be compared with their accuracy, recall, and F1-score. The study also explores the impact of the algorithm's feature selection on its performance. The findings of the investigation will be used to inform the development of new techniques that can be utilized to enhance the security of networks. The KDD NSL dataset provides an ideal opportunity to analyze the performance of various algorithms for detecting network anomalies.

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Network Traffic Anomalies Detection Using a Fixing Method of Multifractal Dimension Jumps in a Real-Time Mode

Cited by 15 publications

References 10 publications

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

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

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Anomaly Detection in Network Traffic using Machine Learning and Deep Learning Techniques

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