IEICE TRANSACTIONS on Fundamentals
Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation
Summary :
Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.1 pp.125-133
- Publication Date
- 2024/01/01
- Publicized
- 2023/06/27
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAP1161
- Type of Manuscript
- PAPER
- Category
- Communication Theory and Signals
Authors
Yitu WANG
North Minzu University
Takayuki NAKACHI
University of the Ryukyus
Keyword
Latest Issue
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Yitu WANG, Takayuki NAKACHI, "Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 1, pp. 125-133, January 2024, doi: 10.1587/transfun.2022EAP1161.
Abstract: Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1161/_p
Copy
@ARTICLE{e107-a_1_125,
author={Yitu WANG, Takayuki NAKACHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation},
year={2024},
volume={E107-A},
number={1},
pages={125-133},
abstract={Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.},
keywords={},
doi={10.1587/transfun.2022EAP1161},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 125
EP - 133
AU - Yitu WANG
AU - Takayuki NAKACHI
PY - 2024
DO - 10.1587/transfun.2022EAP1161
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2024
AB - Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.
ER -
English
