Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach

Yi, Xiuwen; Yang, Xiaodu; Huang, Yanyong; Ke, Songyu; Zhang, Junbo; Li, Tianrui; Zheng, Yu

doi:10.1109/tkde.2021.3062707

Cited by 7 publications

(16 citation statements)

References 29 publications

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“…Parameters are set as [18] suggests. -SVOC [27]: which uses the temperature deformation method to find normal users, and trains OCSVM with them. Unstable users are ranked by the probability the trained OCSVM predicts.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…We conduct experiments on three real-world datasets [27] collected by three branches of a gas group, denoted by companies A, B, and C for short. In total, there are 3,035 users while only 11 users are labeled as gas thefts.…”

Section: Datasetsmentioning

confidence: 99%

“…For gas scenarios, two studies put forward solutions to detect gas-theft suspects. Yi et al obtained abnormal users through the negative correlation between temperature and gas consumption, and then use One-Class Support Vector Machine (OCSVM) to divide abnormal users into theft suspects and irregular users [27]. Yang et al detect the stability of gas use mode to obtain normal users, and then combine normal users with gas-theft labels into positive-negative sample pairs to obtain theft suspects through Ranknet [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection

Pan

Chen

et al. 2023

Hum-Cent Intell Syst

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Some boiler room users steal natural gas by refitting equipment without permission in winter, resulting in gas safety hazards and social problems. Instead of random manual on-site inspection, it is crucial to discover gas-theft suspects timely and automatically by analyzing the gas consumption data. Unfortunately, gas-theft behaviors are complex and various, while the caught gas thefts by gas companies are limited. In this paper, we propose a neural clustering and ranking approach to detect gas theft suspects under the positive-unlabeled learning framework. Our approach contains two modules: joint clustering for normal user identification and triplet ranking for suspicious user detection. The former module considers the regular behaviors to distinguish between normal and unstable users by integrating representation learning and clustering. Then, considering the identified normal samples and the labeled gas thefts, the later module excavates the behavior correlations to discover suspects among unstable users through triplet relation ranking. Thus, normal user identification and suspicious user detection are seamlessly connected to overcome the label scarcity problem. We conduct extensive experiments on three real-world datasets, and the results demonstrate the advantages of our approach over various baselines.

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Section: Baseline Methodsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection

Pan

Chen

et al. 2023

Hum-Cent Intell Syst

Self Cite

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“…Unsupervised methods of outlier detection have been studied widely because they require no additional labels. Representative algorithms include proximity- [3,7,36,44], statistics- [53,61], cluster- [21,33,47,55], OC-SVM- [13,14,54], and reconstruction-based [32,45,59, 60] models. Proximity-based models assume that outliers are points that are far from the other data, and can be identiied by measuring their distance or density.…”

Section: Common Methods Of Outlier Detectionmentioning

confidence: 99%

Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies

Sun

Liu

et al. 2022

ACM Trans. Knowl. Discov. Data

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Outlier detection is an important task in data mining, and many technologies for it have been explored in various applications. However, owing to the default assumption that outliers are not concentrated, unsupervised outlier detection may not correctly identify group anomalies with higher levels of density. Although high detection rates and optimal parameters can usually be achieved by using supervised outlier detection, obtaining a sufficient number of correct labels is a time-consuming task. To solve these problems, we focus on semi-supervised outlier detection with few identified anomalies and a large amount of unlabeled data. The task of semi-supervised outlier detection is first decomposed into the detection of discrete anomalies and that of partially identified group anomalies, and a distribution construction sub-module and a data augmentation sub-module are then proposed to identify them, respectively. In this way, the dual multiple generative adversarial networks (Dual-MGAN) that combines the two sub-modules can identify discrete as well as partially identified group anomalies. In addition, in view of the difficulty of determining the stop node of training, two evaluation indicators are introduced to evaluate the training status of the sub-GANs. Extensive experiments on synthetic and real-world data show that the proposed Dual-MGAN can significantly improve the accuracy of outlier detection, and the proposed evaluation indicators can reflect the training status of the sub-GANs.

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“…The abnormal gas consumption behavior of gas users is one of the most concerned issues of gas companies. In recent years, some users have stolen gas from time to time by modifying gas facilities without permission [1]. The theft of gas not only seriously affects the normal gas operation order and damages the economic interests of enterprises, but also poses a serious threat to public safety and creates hidden dangers of accidents.…”

Section: Introductionmentioning

confidence: 99%

Research on abnormal detection of gas load based on LSTM-WGAN

Meng

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Traditional anomaly detection methods cannot take into account the influence of external factors. Therefore, it is difficult to obtain an accurate detection effect when performing gas anomaly detection. In this paper, we propose an anomaly detection model based on LSTM-WGAN. The multi-layer LSTM network captures temporal dependencies and introduces a self-attention layer to integrate embedded weather, holidays and other influencing factor data. Embed different features into the WGAN framework and make the detection results more accurate by fused models. This paper conducts experiments on the real gas load data set. The experimental results show that the model in this paper has obvious performance advantages in precision, recall and F1 value compared with traditional time series anomaly detection methods.

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Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach

Cited by 7 publications

References 29 publications

Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection

Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection

Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies

Research on abnormal detection of gas load based on LSTM-WGAN

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