Anomalous Data Research Articles

Anomalous data detection in cognitive IoT sensor network

Anomalous data detection in cognitive IoT sensor network

Intelligent scheduling and optimization of microenergy grid: the application and development of computer technology

Abstract For the intelligent management of micro-energy grid, the limitations of traditional scheduling optimization methods have begun to be highlighted, and computer technology has become a new generation of power system support means. This paper proposes a state estimation method based on smart grid measurement technology, and for the characteristics of micro-energy grid anomaly data, adopts the sampling value detection anti-anomaly data method based on amplitude comparison to estimate the grid dynamic process. Based on the grid state estimation dataset, a smart grid scheduling strategy based on cloud computing is constructed. The conditional value-at-risk of the penalty function of generation cost, grid cost, and motion cost is used as the objective function of the dispatch modeling, and the solution method for the optimization of the value-at-risk model is provided. Finally, an example has been built for this dispatch model to access a simulated micro-energy grid system that contains multiple nodes. The results show that the error between the voltage amplitude obtained from the dynamic state estimation of the micro-energy grid using the method of sampled-value detection against anomalous data and the real value is no more than 0.5%, and the data scheduling error rate of the cloud computing does not fluctuate significantly when dealing with a large number of scheduling data tasks, all of which are controlled to be below 0.0015. The scheduling strategy based on cloud computing has a certain degree of generalization when dealing with random and variable source and load scenarios, which can effectively improve the level of power grid business applications and complete the corresponding intelligent scheduling.

Application of Intelligent Optimization Algorithms to the Design of Automatic Generation of Software Tests for Data Anomaly Identification

Abstract This paper firstly constructs a system for monitoring precursor observation data and automatic identification of anomalies, designs the functions of downloading and format conversion of precursor data files and downloading and decompression of compressed files, and adopts the interpolation method to pre-process the precursor observation data. Secondly, a method based on the SURF intelligent optimization algorithm is adopted to automatically recognize and classify anomalous data from seismic big data. Finally, experiments were carried out to predict short-acuity earthquakes, and the experimental results were analyzed and contrasted. The results show that the accuracy of 2-day prediction and 4-day prediction gradually stabilizes at 0.728~0.785 when the training reaches 100 steps and gradually decreases and reaches about 0.65 with the increase of the prediction time period, which verifies the feasibility and practicality of the system. The research in this paper provides a new idea and method for earthquake precursor data anomaly identification, which has certain research and application value.

IoT-based automatic deep learning model generation and the application on Empty-dish Recycling Robots

As a modern computer vision technology, deep learning (DL) revolutionizes our lives and reshapes our world with its high performance. Usually, to accomplish each task, researchers need to collect the dataset and modify the basic DL model to optimize the model. In addition, collecting the dataset is time-consuming, and building separate Artificial Intelligence (AI) models for each task is significantly ineffective Therefore, AI research faces challenges in realizing effective data collection and DL model generation. To solve these problems, we design an Internet of Things (IoT)-based automatic DL model generation strategy which also includes an IoT-based data collection method. In detail, we design an Android application that captures images and sends them to a cloud server to create a dataset. Additionally, we apply a two-level checking function to filter out anomalous data and ensure the dataset’s correctness. Next, the cloud server uses the dataset to train and generate a DL model automatically. We have prepared the state-of-the-art DL components in the cloud server and propose an automatic DL model creation process for model generation. We have applied the proposal to Empty-dish Recycling Robots for demonstration and evaluation purposes to easily understand the proposal and measure the performance. The experimental results show that the system successfully collects the dataset and automatically generates the DL model. Furthermore, the checking function deployed on Android devices requires only 0.84 MB and achieves 99.86% accuracy. During training, the time spent on each automatic DL model generation is evidently decreased by about 9.00% to 28.00%.

ELM-KL-LSTM: a robust and general incremental learning method for efficient classification of time series data.

Efficiently analyzing and classifying dynamically changing time series data remains a challenge. The main issue lies in the significant differences in feature distribution that occur between old and new datasets generated constantly due to varying degrees of concept drift, anomalous data, erroneous data, high noise, and other factors. Taking into account the need to balance accuracy and efficiency when the distribution of the dataset changes, we proposed a new robust, generalized incremental learning (IL) model ELM-KL-LSTM. Extreme learning machine (ELM) is used as a lightweight pre-processing model which is updated using the new designed evaluation metrics based on Kullback-Leibler (KL) divergence values to measure the difference in feature distribution within sliding windows. Finally, we implemented efficient processing and classification analysis of dynamically changing time series data based on ELM lightweight pre-processing model, model update strategy and long short-term memory networks (LSTM) classification model. We conducted extensive experiments and comparation analysis based on the proposed method and benchmark methods in several different real application scenarios. Experimental results show that, compared with the benchmark methods, the proposed method exhibits good robustness and generalization in a number of different real-world application scenarios, and can successfully perform model updates and efficient classification analysis of incremental data with varying degrees improvement of classification accuracy. This provides and extends a new means for efficient analysis of dynamically changing time-series data.

Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations

Driver maneuver interaction learning (DMIL) refers to the classification task with the goal of identifying different driver-vehicle maneuver interactions (e.g., left/right turns). Existing conventional studies largely focused on the centralized collection of sensor data from the drivers' smartphones (say, inertial measurement units or IMUs, like accelerometer and gyroscope). Such a centralized mechanism might be precluded by data regulatory constraints. Furthermore, how to enable an adaptive and accurate DMIL framework remains challenging due to (i) complexity in heterogeneous driver maneuver patterns, and (ii) impacts of anomalous driver maneuvers due to, for instance, aggressive driving styles and behaviors. To overcome the above challenges, we propose AF-DMIL, an Anomaly-aware Federated Driver Maneuver Interaction Learning system. We focus on the real-world IMU sensor datasets (e.g., collected by smartphones) for our pilot case study. In particular, we have designed three heterogeneous representations for AF-DMIL regarding spectral, time series, and statistical features that are derived from the IMU sensor readings. We have designed a novel heterogeneous representation attention network (HetRANet) based on spectral channel attention, temporal sequence attention, and statistical feature learning mechanisms, jointly capturing and identifying the complex patterns within driver maneuver behaviors. Furthermore, we have designed a densely-connected convolutional neural network in HetRANet to enable the complex feature extraction and enhance the computational efficiency of HetRANet. In addition, we have designed within AF-DMIL a novel anomaly-aware federated learning approach for decentralized DMIL in response to anomalous maneuver data. To ease extraction of the maneuver patterns and evaluation of their mutual differences, we have designed an embedding projection network that projects the high-dimensional driver maneuver features into low-dimensional space, and further derives the exemplars that represent the driver maneuver patterns for mutual comparison. Then, AF-DMIL further leverages the mutual differences of the exemplars to identify those that exhibit anomalous patterns and deviate from others, and mitigates their impacts upon the federated DMIL. We have conducted extensive driver data analytics and experimental studies on three real-world datasets (one is harvested on our own) to evaluate the prototype of AF-DMIL, demonstrating AF-DMIL's accuracy and effectiveness compared to the state-of-the-art DMIL baselines (on average by more than 13% improvement in terms of DMIL accuracy), as well as fewer communication rounds (on average 29.20% fewer than existing distributed learning mechanisms).

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

A comparative analysis of the methods of evaluating dependability using Big Data of complex system operation

Aim. The paper aims to research the methods of evaluating dependability indicators based on the analysis of Big Data obtained in the course of monitoring the operation of technical systems and their components. A comparative analysis is performed of the efficiency of robust statistical methods of evaluating the dependability indicators of complex technical systems based on operation data.Methods. The paper uses methods of mathematical statistics, specifically robust methods of evaluating the translation parameter of a noisy sample and numerical methods of statistical modeling. The authors consider five methods of evaluating the translation parameter: sample mean as a nonrobust method used for comparison; sample median as the simplest robust method of evaluating the translation parameter; two-stage evaluation procedure with truncation of outliers according to the three sigma rule; two-stage evaluation procedure with truncation of outliers using Tukey’s box-and-whisker plot; Huber’s robust method. The comparative analysis of the methods of evaluating system dependability indicators was conducted by means of statistical modeling in the R statistical computation package. Five distribution laws for generating an element’s time-to-failure and recovery time samples were considered: exponential distribution, Weibull distribution, log-normal distribution, gamma distribution and uniform distribution.Results. Statistical analysis of Big Data associated with the operation of technical systems is complicated by the heterogeneity and noisiness of such data, as well as the presence of errors and outliers of varied nature. That is primarily due to the varied loads and operating conditions of each object. Herein this problem is examined as regards the problem of evaluating the dependability indicators of a structurally complex monotonic system with independent element recovery. The paper examines methods of rejecting anomalous data and robust evaluation of the sample position parameter and performs a comparative analysis of the efficiency of such methods for various distribution laws. It is shown that robust methods of evaluation enable significantly higher accuracy as compared to the standard sample mean. The two-stage procedure based on the truncation of outliers and Tukey’s box-and-whisker plot proved to be the most efficient.Conclusions. The paper’s findings allow improving the accuracy of evaluation of dependability indicators based on complex technical system operation data. They can be used in Big Data processing and complex system dependability theory.

Enhanced abnormal data detection hybrid strategy based on heuristic and stochastic approaches for efficient patients rehabilitation

Over the last few years, substantial research has been conducted towards developing efficient abnormal detection techniques while considering efficiency, accuracy, high-dimensional data, distributed environments, and others. Researchers increasingly deal with “abnormalities” in clinical patient data to derive relevant clinical knowledge for making informed decisions. However, data collection for clinically relevant research is often guided by patient conditions and administrative or clinical requirements rather than a regular schedule. Therefore, clinical data is frequently obtained in an unreliable form, characterized by data outliers and inconsistencies, incomplete information, and an unstructured format that varies based on patient types and data structures. In this research study, an enhanced hybrid AD strategy is developed based on heuristic and stochastic methods to cope with abnormalities in the clinical data of patients. The proposed hybrid strategy employs optimal k-means clustering as a heuristic method to cluster the clinical data based on the patient’s routine exercise characteristics to cope with abnormalities efficiently. Next, an interquartile range-based stochastic approach is employed as a statistical method to detect and eliminate abnormal data points by providing only reliable and effectual data to medical practitioners. The main objective of this research article is to facilitate healthcare and research practitioners by dealing with a high dimensional massive amount of inconsistent and incomplete clinical data of patients to detect and discard anomalous data points for providing only efficacious information. Furthermore, the AutoML paradigm is employed to develop an optimal regression model for analyzing the impact of the proposed hybrid strategy for abnormal pattern detection. In addition, different statistical error estimation measures are used to evaluate the empirical effectiveness of the proposed hybrid strategy using AutoML. The experiment results show a noteworthy improvement in terms of the R2 score for predicting healthcare indicators compared to the existing state-of-the-art regression models. Our optimal regression model performed efficiently regarding the R2 score and MAPE; it achieved an R2 score of 0.9855 and 0.9850 for predicting the Borg RPE and TUG, respectively. Similarly, our model achieved a low prediction error in terms of MAPE for predicting both health functional indicators; it achieved a MAPE of 6.57% and 5.19% for Borg RPE and TUG prediction. Our contribution signifies that the performance of the AutoML improves and outperforms traditional regression models while applying our proposed hybrid abnormal detection model to the patient’s rehabilitation data for accurately dealing with anomalous data.

P2 random walk: self-supervised anomaly detection with pixel-point random walk

In the domain of intelligent manufacturing, automatic anomaly detection plays a pivotal role and holds great significance for improving production efficiency and product quality. However, the scarcity and uncertainty of anomalous data pose significant challenges in this field. Data augmentation methods, such as Cutout, which are widely adopted in existing methodologies, tend to generate patterned data, leading to biased data and compromised detection performance. To deal with this issue, we propose an approach termed self-supervised anomaly detection with pixel-point random walk (P2 Random Walk), which combines data augmentation and Siamese neural networks. We develop a pixel-level data augmentation technique to enhance the randomness of generated data and establish a two-stage anomaly classification framework. The effectiveness of the P2 Random Walk method has been demonstrated on the MVTec dataset, achieving an AUROC of 96.2% and 96.3% for classification and segmentation, respectively, by using only data augmentation-based techniques. Specifically, our method outperforms other state-of-the-art methods in several categories, improving the AUROC for classification and segmentation by 0.5% and 0.3%, respectively, which demonstrates the high performance and strong academic value of our method in anomaly detection tasks.

Applied Bayesian structural health monitoring: Inclinometer data anomaly detection and forecasting

AbstractInclinometer probes are devices that can be used to measure deformations within earthwork slopes. This paper demonstrates a novel application of Bayesian techniques to real‐world inclinometer data, providing both anomaly detection and forecasting. Specifically, this paper details an analysis of data collected from across the entire UK rail network.Inclinometers are a standard tool of geotechnical site monitoring. Data from these instruments is often used in risk analysis and decision‐making. However, discerning anomalous data points and forecasting future behaviour from inclinometer data requires significant ‘engineering judgement’ (subjective appraisal). This is because the observational data is derived from complex physical phenomena and contains complex spatio‐temporal correlations. Additionally, the practical demands of data collected from remote sites over several years tends to introduce systematic errors. These issues make the interpretation of inclinometer data challenging.Practitioners have effectively two goals when processing monitoring data. The first is to identify any anomalous or dangerous movements, and the second is to predict potential future adverse scenarios by forecasting. In this paper we apply Uncertainty Quantification (UQ) techniques by implementing a Bayesian approach to anomaly detection and forecasting for inclinometer data. Subsequently, both costs and risks may be minimised by quantifying and evaluating the appropriate uncertainties. This framework may then act as an enabler for enhanced decision making and risk analysis.We show that inclinometer data can be described by a latent autocorrelated Markov process derived from measurements. This can be used as the transition model of a non‐linear Bayesian filter. This allows for the prediction of system states. This learnt latent model also allows for the detection of anomalies: observations that are far from their expected value may be considered to have ‘high surprisal’, that is they have a high information content relative to the model encoding represented by the learnt latent model.We successfully apply the forecasting and anomaly detection techniques to a large real‐world data set in a computationally efficient manner. Although this paper studies inclinometers in particular, the techniques are broadly applicable to all areas of engineering UQ and Structural Health Monitoring (SHM).

Anomaly detection via gating highway connection for retinal fundus images

Since the labels for medical images are challenging to collect in real scenarios, especially for rare diseases, fully supervised methods cannot achieve robust performance for clinical anomaly detection. Recent research tried to tackle this problem by training the anomaly detection framework using only normal data. Reconstruction-based methods, e.g., auto-encoder, achieved impressive performances in the anomaly detection task. However, most existing methods adopted the straightforward backbone architecture (i.e., encoder-and-decoder) for image reconstruction. The design of a skip connection, which can directly transfer information between the encoder and decoder, is rarely used. Since the existing U-Net has demonstrated the effectiveness of skip connections for image reconstruction tasks, in this paper, we first use the dynamic gating strategy to achieve the usage of skip connections in existing reconstruction-based anomaly detection methods and then propose a novel gating highway connection module to adaptively integrate skip connections into the framework and boost its anomaly detection performance, namely GatingAno. Furthermore, we formulate an auxiliary task, namely histograms of oriented gradients (HOG) prediction, to encourage the framework to exploit contextual information from fundus images in a self-driven manner, which increases the robustness of feature representation extracted from the healthy samples. Last but not least, to improve the model generalization for anomalous data, we introduce an adversarial strategy for the training of our multi-task framework. Experimental results on the publicly available datasets, i.e., IDRiD and ADAM, validate the superiority of our method for detecting abnormalities in retinal fundus images. The source code is available at https://github.com/WentianZhang-ML/GatingAno.

Enhancing anomaly detectors with LatentOut

LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} acting as a one-class classifier and we experiment the combination of LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.

Scaling the Timing-Based Detection of Anomalies in Real-World Aircraft Trajectories

The insecurity of ADS-B and GPS is a well-known problem in air traffic control. In this work, we introduce a statistical location verification method for crowdsourced aircraft data. We implement this method on the backend of the OpenSky Network and evaluate it on 8 months of data collected from Radarcape sensors. We thus demonstrate the effectiveness of our method in finding anomalous flight data in faster than real time. While we do not find clear evidence of widespread malicious spoofing attacks captured by OpenSky sensors, many sensor and transponder issues are unearthed. Finally, we simulate ADS-B and GNSS attacks, which were detected successfully with few false positives.

Online detection method for variable load conditions and anomalous sound of hydro turbines using correlation analysis and PCA-adaptive-K-means

The hydro turbine is the critical component of hydropower stations. Sound analysis provides a versatile non-contact approach for detecting anomalies or faults of hydro turbines. However, sound analysis methods such as Fourier transform and AI-based pattern recognition algorithms have limitations in achieving online detection due to potential fault frequency aliasing, non-obvious fault characteristics, lack of anomalous samples, and irregular occurrence of anomalous sounds. To overcome these challenges, the proposed method initially employs online correlation analysis to distinguish variable load conditions, followed by the detection of anomalous sounds under stable load conditions using principal component analysis (PCA) and adaptive K-means clustering (K-means). In the method, time–frequency analysis and wavelet packet decomposition are incorporated to extract signal features, with PCA employed to highlight anomalous features. Subsequently, feature correlation analysis and online adaptive clustering are used to accurately detect variable load conditions and anomalous sounds. The proposed method does not rely on prior knowledge of anomalous data, making it flexible and applicable to most practical hydro turbines. Additionally, the method aims to establish a robust foundation for fault diagnosis utilizing sound signals in hydro turbines, providing real-time detection of variable load conditions or anomalous sounds every second. To validate the effectiveness of the proposed method, detection experiments are conducted in practical hydropower station.

Developing Information Gateway for Intelligent Decision-Making and Stability in Solar Power Generation

Abstract This study has developed a specialized information gateway for inverters, utilizing relays, data converters, and single-board computers, among other components. Upon receiving data from the gateway, the server processes it to generate an intelligent solar monitoring system. The platform utilizes deep learning RNN and LSTM algorithms to forecast power generation at solar power plants, allowing for real-time monitoring of weather and power generation. By comparing actual and expected power generation data, the system can adjust equipment maintenance and cleaning schedules. It is designed to automatically alert staff members to take appropriate action when anomalous power generation data is continuously transmitted. Additionally, the system sends an alert for on-site inspection and removal of any abnormal situations to increase the stability of solar power generation. This study employs deep learning and IoT data collection to provide the knowledge necessary for intelligent decision-making and increased stability in solar power generation. JEL classification numbers: C43, F68, H41. Keywords: Solar energy, IoT monitoring, RNN, LSTN, Abnormal return.

Unsupervised anomaly detection in images using attentional normalizing flows

Image-based anomaly detection has been widely used in practice, but it is still a challenging task due to the irregularity of anomalies. Existing representation-based methods have achieved high accuracy metrics in image-based anomaly detection, but they are weak in capturing anomalous regions, resulting in small inter-class variance between the latent distributions of normal and anomalous data. Furthermore, most current neural networks have low parameter efficiency, which limits their inference performance. To address these issues, we propose a novel normalizing flow called AFlow. Our network represents multi-scale features with multi-scale convolutions instead of inputs of different resolutions, significantly reducing the network parameters and improving the fine-grained representation in features. Then, an attention module is introduced into the normalizing flow to further select the multi-scale features along feature channels, making the network focus on relevant regions with more object details to refine the objective of maximum likelihood estimation. Experiments on two public datasets show that the proposed method can achieve more accurate prediction results and higher parameter efficiency than previous detection models: the average AUROC of the proposed method reaches 99.75% and 97.2% on MVTec AD and BTAD datasets, respectively.

Short-term wind power prediction based on anomalous data cleaning and optimized LSTM network

Wind power prediction values are often unstable. The purpose of this study is to provide theoretical support for large-scale grid integration of power systems by analyzing units from three different regions in China and using neural networks to improve power prediction accuracy. The variables that have the greatest impact on power are screened out using the Pearson correlation coefficient. Optimize LSTM with Lion Swarm Algorithm (LSO) and add GCT attention module for optimization. Short-term predictions of actual power are made for Gansu (Northwest China), Hebei (Central Plains), and Zhejiang (Coastal China). The results show that the mean absolute percentage error (MAPE) of the nine units ranges from 9.156% to 16.38% and the root mean square error (RMSE) ranges from 1.028 to 1.546 MW for power prediction for the next 12 h. The MAPE of the units ranges from 11.36% to 18.58% and the RMSE ranges from 2.065 to 2.538 MW for the next 24 h. Furthermore, the LSTM is optimized by adding the GCT attention module to optimize the LSTM. 2.538 MW. In addition, compared with the model before data cleaning, the 12 h prediction error MAPE and RMSE are improved by an average of 34.82% and 38.10%, respectively; and the 24 h prediction error values are improved by an average of 26.32% and 20.69%, which proves the necessity of data cleaning and the generalizability of the model. The subsequent research content was also identified.

ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.