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Currently, with the growing applicability of nonlinear electrical devices, power quality disturbances (PQDs) often occur in power systems. Previous works usually extracted statistical features from electrical signals manually and constructed classifiers with traditional machine learning methods for PQD monitoring. Furthermore, noisy tags or unlabeled data (different from noisy signals) are usually ignored in the traditional training stage, and these methods fail to meet the high accuracy and automation demands of real-world scenarios. To overcome the shortcomings of existing methods, this paper proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments. First, an input voltage waveform signal is divided into several intrinsic mode functions by an empirical wavelet transform (EWT), and these functions are then aligned into columns to form a matrix. Second, tri-training is utilized for label refactoring to improve the generalization ability of the model in a noisy environment. Then, an optimized convolutional neural network (CNN) structure combined with principal component analysis (PCA) is deployed to extract and select the universal features automatically. Finally, a support vector machine (SVM) classifier is constructed to recognize PQD patterns. Several comparative experiments are performed to verify the effectiveness and accuracy of PowerCog in complex environments.
Lei Fu; Ke Yan; Tiantian Zhu. PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.
AMA StyleLei Fu, Ke Yan, Tiantian Zhu. PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.
Chicago/Turabian StyleLei Fu; Ke Yan; Tiantian Zhu. 2021. "PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.
Malicious HTTP traffic detection plays an important role in web application security. Most existing work applies machine learning and deep learning techniques to build the malicious HTTP traffic detection model. However, they still suffer from the problems of huge training data collection cost and low cross-dataset generalization ability. Aiming at these problems, this paper proposes DeepPTSD, a deep learning method for payload based malicious HTTP traffic detection. First, it treats the malicious HTTP traffic detection as a text classification problem and trains the initial detection model using TextCNN on a public dataset, and then adapts the initial detection model to the target dataset based on a transfer learning algorithm. Second, in the transfer learning procedure, it uses a semi-supervised learning algorithm to accomplish the model adaptation task. The semi-supervised learning algorithm enhances the target dataset based on a HTTP payload data augmentation mechanism to exploit both the labeled and unlabeled data. We evaluate DeepPTSD on two real HTTP traffic datasets. The results show that DeepPTSD has competitive performance under the small data condition.
Tieming Chen; Yunpeng Chen; Mingqi Lv; Gongxun He; Tiantian Zhu; Ting Wang; Zhengqiu Weng. A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning. Applied Sciences 2021, 11, 7188 .
AMA StyleTieming Chen, Yunpeng Chen, Mingqi Lv, Gongxun He, Tiantian Zhu, Ting Wang, Zhengqiu Weng. A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning. Applied Sciences. 2021; 11 (16):7188.
Chicago/Turabian StyleTieming Chen; Yunpeng Chen; Mingqi Lv; Gongxun He; Tiantian Zhu; Ting Wang; Zhengqiu Weng. 2021. "A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning." Applied Sciences 11, no. 16: 7188.
Trajectory prediction for mobile phone users is a cornerstone component to support many higher-level applications in LBSs (Location-Based Services). Most existing methods are designed based on the assumption that the explicit location information of the trajectories is available (e.g., GPS trajectories). However, collecting such kind of trajectories lays a heavy burden on the mobile phones and incurs privacy concerns. In this paper, we study the problem of trajectory prediction based on cell-id trajectories without explicit location information and propose a deep learning framework (called DeepCTP) to solve this problem. Specifically, we use a multi-graph embedding method to learn the latent spatial correlations between cell towers by exploiting handoff patterns. Then, we design a novel spatial-aware loss function for the encoder-decoder network to generate cell-id trajectory predictions. We conducted extensive experiments on real datasets. The experiment results show that DeepCTP outperforms the state-of-the-art cell-id trajectory prediction methods in terms of prediction error.
Mingqi Lv; Dajian Zeng; Ling Chen; Tieming Chen; Tiantian Zhu; Shouling Ji. Private Cell-ID Trajectory Prediction Using Multi-Graph Embedding and Encoder-Decoder Network. IEEE Transactions on Mobile Computing 2021, PP, 1 -1.
AMA StyleMingqi Lv, Dajian Zeng, Ling Chen, Tieming Chen, Tiantian Zhu, Shouling Ji. Private Cell-ID Trajectory Prediction Using Multi-Graph Embedding and Encoder-Decoder Network. IEEE Transactions on Mobile Computing. 2021; PP (99):1-1.
Chicago/Turabian StyleMingqi Lv; Dajian Zeng; Ling Chen; Tieming Chen; Tiantian Zhu; Shouling Ji. 2021. "Private Cell-ID Trajectory Prediction Using Multi-Graph Embedding and Encoder-Decoder Network." IEEE Transactions on Mobile Computing PP, no. 99: 1-1.
Web shell is a malicious script file that can harm web servers. Web shell is often used by intruders to perform a series of malicious operations on website servers, such as privilege escalation and sensitive information leakage. Existing web shell detection methods have some shortcomings, such as viewing a single network traffic behavior, using simple signature comparisons, and adopting easily bypassed regex matches. In view of the above deficiencies, a web shell detection method based on multiview feature fusion is proposed based on the PHP language web shell. Firstly, lexical features, syntactic features, and abstract features that can effectively represent the internal meaning of web shells from multiple levels are integrated and extracted. Secondly, the Fisher score is utilized to rank and filter the most representative features, according to the importance of each feature. Finally, an optimized support vector machine (SVM) is used to establish a model that can effectively distinguish between web shell and normal script. In large-scale experiments, the final classification accuracy of the model on 1056 web shells and 1056 benign web scripts reached 92.18%. The results also surpassed well-known web shell detection tools such as VirusTotal, ClamAV, LOKI, and CloudWalker, as well as the state-of-the-art web shell detectionmethods.
Tiantian Zhu; Zhengqiu Weng; Lei Fu; Linqi Ruan. A Web Shell Detection Method Based on Multiview Feature Fusion. Applied Sciences 2020, 10, 6274 .
AMA StyleTiantian Zhu, Zhengqiu Weng, Lei Fu, Linqi Ruan. A Web Shell Detection Method Based on Multiview Feature Fusion. Applied Sciences. 2020; 10 (18):6274.
Chicago/Turabian StyleTiantian Zhu; Zhengqiu Weng; Lei Fu; Linqi Ruan. 2020. "A Web Shell Detection Method Based on Multiview Feature Fusion." Applied Sciences 10, no. 18: 6274.
Mobile authentication is a fundamental factor in the protection of user's private resources. In recent years, motion sensor-based biometric authentication has been widely used for privacy-preserving. However, it faces with the problems including low data collection efficiency, insufficient authentication scenario coverage rate, weak de-noising ability, and poor robustness of models, rendering existing methods difficult to meet the security, privacy, and usability requirements jointly in the real-world scenario. To overcome these difficulties, we propose a system called ESPIALCOG, which is able to 1) collect the sensor data embedded in mobile devices self-adaptively, unobtrusively and efficiently through the evolutionary stable participation game mechanism (ESPGM) with a high scenario coverage rate, 2) minimize noise from collected data by analyzing three types of abnormalities, and 3) authenticate the ownership of mobile devices in real-time by adopting optimized LSTM model with an enhanced stochastic gradient descent (SGD) algorithm. The simulation experiment on 6000 users shows that the efficiency and coverage rates increase dramatically by deploying our ESPGM. Moreover, we conduct experiments on a large-scale real-world noisy dataset with 1513 users and two other small pure real-world datasets. The experimental results show the high accuracy and favorable robustness of ESPIALCOG in the noisy environment.
Tiantian Zhu; Zhengqiu Weng; Qijie Song; Yuan Chen; Qiang Liu; Mingqi Lv; Tieming Chen. ESPIALCOG: General, Efficient and Robust Mobile User Implicit Authentication in Noisy Environment. IEEE Transactions on Mobile Computing 2020, PP, 1 -1.
AMA StyleTiantian Zhu, Zhengqiu Weng, Qijie Song, Yuan Chen, Qiang Liu, Mingqi Lv, Tieming Chen. ESPIALCOG: General, Efficient and Robust Mobile User Implicit Authentication in Noisy Environment. IEEE Transactions on Mobile Computing. 2020; PP (99):1-1.
Chicago/Turabian StyleTiantian Zhu; Zhengqiu Weng; Qijie Song; Yuan Chen; Qiang Liu; Mingqi Lv; Tieming Chen. 2020. "ESPIALCOG: General, Efficient and Robust Mobile User Implicit Authentication in Noisy Environment." IEEE Transactions on Mobile Computing PP, no. 99: 1-1.
With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.
Tiantian Zhu; Zhengqiu Weng; Guolang Chen; Lei Fu. A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors. Sensors 2020, 20, 3876 .
AMA StyleTiantian Zhu, Zhengqiu Weng, Guolang Chen, Lei Fu. A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors. Sensors. 2020; 20 (14):3876.
Chicago/Turabian StyleTiantian Zhu; Zhengqiu Weng; Guolang Chen; Lei Fu. 2020. "A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors." Sensors 20, no. 14: 3876.
Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.
Lei Fu; Tiantian Zhu; Guobing Pan; Sihan Chen; Qi Zhong; Yanding Wei. Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection. Applied Sciences 2019, 9, 4901 .
AMA StyleLei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong, Yanding Wei. Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection. Applied Sciences. 2019; 9 (22):4901.
Chicago/Turabian StyleLei Fu; Tiantian Zhu; Guobing Pan; Sihan Chen; Qi Zhong; Yanding Wei. 2019. "Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection." Applied Sciences 9, no. 22: 4901.
Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.
Lei Fu; Yiling Yang; Xiaolong Yao; Xufen Jiao; Tiantian Zhu; Fu; Yang; Yao; Jiao; Zhu. A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion. Energies 2019, 12, 3817 .
AMA StyleLei Fu, Yiling Yang, Xiaolong Yao, Xufen Jiao, Tiantian Zhu, Fu, Yang, Yao, Jiao, Zhu. A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion. Energies. 2019; 12 (20):3817.
Chicago/Turabian StyleLei Fu; Yiling Yang; Xiaolong Yao; Xufen Jiao; Tiantian Zhu; Fu; Yang; Yao; Jiao; Zhu. 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion." Energies 12, no. 20: 3817.
Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.
Lei Fu; Tiantian Zhu; Kai Zhu; Yiling Yang. Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy. Energies 2019, 12, 3085 .
AMA StyleLei Fu, Tiantian Zhu, Kai Zhu, Yiling Yang. Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy. Energies. 2019; 12 (16):3085.
Chicago/Turabian StyleLei Fu; Tiantian Zhu; Kai Zhu; Yiling Yang. 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy." Energies 12, no. 16: 3085.