A Fourier Series-Based Anomaly Extraction Approach to Access Network Traffic in Power Telecommunications

Zhao, Honghao; Lu, Bin; Yu, Li; Zhao, Siwen; Zeng, Lingkang; Zhang, Zhe; You, Ping

doi:10.1109/iccsec.2017.8446807

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…However, traffic detection is deemed to be a developing and challenging issue since it needs to deal with various difficulties coming from imbalanced data, increasing network traffic volume, evolving and sophisticated attacks, as well as dynamic and variable network environments [2][3][4][5]. Various techniques have been developed to detect and analyze such anomalies, ranging from statistical-based methods [6][7][8], time series analysis [9][10][11], machine-learning based methods [12][13][14], deep-learning based methods [15][16][17], ensemble methods [18][19][20], flow-based analysis [21][22][23], hybrid methods [24,25], to unsupervised clustering methods [26,27].…”

Section: Introductionmentioning

confidence: 99%

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

2023

IJACSA

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The booming of computer networks and software applications has led to an explosive growth in the potential damage caused by network attacks. Efficient detection of abnormal traffic in networks is appealing for facilely mastering the traffic tracking and locating for network usage at low resource cost. High quality abnormal traffic detection of Internet becomes particularly relevant during the automated services of multiple application situations. This paper proposes a novel abnormal traffic detection algorithm called ODFM based on the optimization of data feature and mining. Specially, we develop a feature selection strategy to reduce the feature analysis dimension, and set a peer-to-peer (P2P) traffic identification module to filter and mine the related service traffic to reduce the amount of data detection and facilitate the abnormal traffic detection. Experimental results demonstrate that the proposed algorithm greatly improves the detection accuracy, which verifies its effectiveness and competitiveness in the general tasks of abnormal network traffic detection.

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Section: Introductionmentioning

confidence: 99%

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

2023

IJACSA

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“…One of the most common methods used for FDD is signal processing techniques, such as Fourier transform (FT), S-transform (ST), and wavelet transform (WT) [6]. The Fourier analysis of current and voltage waveforms using fast Fourier transform (FFT), along with Kalman filter and ST, are the most commonly used methods for analysis in the frequency domain [7][8][9][10][11]. However, Fourier analysis or ST alone is not enough for the detection of low-magnitude transients because of their sensitivity over discontinuities.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

et al. 2021

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In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.

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Anomaly detection of power grid dispatching platform based on Isolation Forest and K-means fusion algorithm

Liang

Zhang

Fang

et al. 2020

J. Phys.: Conf. Ser.

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It is very important to detect abnormal Access IP of power grid dispatching platform accurately and rapidly to ensure the safety of power production. An Isolation Forest and K-means fusion algorithm is proposed, which not only solves the deficiency of Isolation Forest only being able to detect anomalies in binary classification, but also solves the defect of Isolation Forest threshold setting based on artificial experience or prior assumptions by designing a threshold setting strategy for Isolation Forest anomalies. The Access IP data of the southern power grid dispatching platform is taken as an example to evaluate the proposed algorithm and model. Through control experiments, the effectiveness and advancedness of the algorithm are illustrated in terms of AUC-ROC curve, accuracy, recall, and F1 score.

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A Fourier Series-Based Anomaly Extraction Approach to Access Network Traffic in Power Telecommunications

Cited by 5 publications

References 11 publications

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

Anomaly detection of power grid dispatching platform based on Isolation Forest and K-means fusion algorithm

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