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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Do Thu Ha Nguyen Xuan Hoang Nguyen Viet Hoang Nguyen Huu Du Truong Thu Huong Kim Phuc Tran Dong A Univ Int Res Inst Artificial Intelligence & Data Sci Danang Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Univ Lille ENSAIT ULR 2461 GEMTEX Genie & Mat Text F-59000 Lille France

Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a "black box" that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an lstm-based autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset. Copyright (C) 2022 The Authors.

关键词： XAI lstm autoencoder Anomaly Detection ICS Gradient SHAP

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A Manipulated Overlapped Voltage Attack Detection Mechanism for Voltage-Based Vehicle Intrusion Detection System 5th

A Manipulated Overlapped Voltage Attack Detection Mechanism ...

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5th International Conference on Frontiers in Cyber Security (FCS)

作者： Yin, Long Xu, Jian Chai, Heqiu Wang, Chen Northeastern Univ Software Coll Shenyang 110169 Peoples R China

ISBN: (纸本)9789811984440;9789811984457

To evade being detected by the content-based or frequency-based IDS, the attack model in the automotive CAN has shifted from the traditional packet flooding and payload modification attacks to stealth attacks such as shutdown attacks. These new types of stealth attacks are difficult to be effectively detected by content-based IDS and frequency-based IDS. The CAN bus physical voltage-based IDS can identify the source of each message and detect these stealth attacks effectively. However, the state of art research has discovered a novel masquerade attack called DUET, which can tamper with the existing voltage-based IDS by generating overlapping voltage signals with an accomplice to distort the fingerprint of the specified ECU. We propose a detection mechanism to prevent the manipulated voltage attacks of overlapping voltage signal samples, which is based on anomaly detection by applying the lstm autoencoder model. By filtering the overlapped signal and rectifying the voltage fingerprint instance of the original voltage signal, the improved voltage-based IDS can effectively resist the DUET attack. Experiments demonstrated the proposed detection mechanism can authenticate the victim ECU and the accomplice ECU before and after the DUET two-stage attack, and prevent the receiver ECU from being deceived by the forged messages generated by the attacker and accomplice ECUs.

关键词： Voltage-based IDS DUET attack Manipulated overlapped voltage attack lstm autoencoder

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Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning

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SENSORS 2024年第14期24卷 4593页

作者： Maudsley-Barton, Sean Yap, Moi Hoon Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England

Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an lstm autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.

关键词： anomaly detection computer-aided diagnosis falls risk lstm autoencoder representational model markerless motion capture

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Driving Maneuver Classification Using Domain Specific Knowledge and Transfer Learning

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IEEE ACCESS 2021年 9卷 86590-86606页

作者： Sarker, Supriya Haque, Md Mokammel Dewan, M. Ali Akber Chittagong Univ Engn & Technol Dept Comp Sci & Engn Chattogram 4349 Bangladesh Athabasca Univ Fac Sci & Technol Sch Comp & Informat Syst Athabasca AB T9S 3A3 Canada

With the increasing number of vehicles, the usage of technology has also been increased in the transportation system. Although automobile companies are using advanced technologies to develop high performing transports, traffic safety still remains to be a concerning issue. Drivers' driving behavior is considered as one of the key factors of the traffic safety, which could be monitored from their individual driving maneuvers. In this paper, we present a supervised learning model and a semi-supervised transfer learning model for the classification of driving maneuvers from the sensor fusion time series data. The semi-supervised model consists of an unsupervised long-short term memory (lstm) autoencoder and a supervised lstm classifier. The supervised model consists of a supervised lstm model. Because of using lstm, both of the models can analyze time-series data. In the semi-supervised model, the lstm encoder learns from unlabeled data as a compressed low dimensional feature vector, which then transfers the learning to the supervised lstm classifier to classify the driving maneuvers. With the proposed models, we use domain specific knowledge data of the driving environment, such as data changing rules of various driving maneuvers as well as the temporal features over time. We use class functions for seven driving maneuver types and convert those into binary feature vector to use with the lstm models. We present a comparative analysis of the per class accuracy of the proposed semi-supervised and supervised models with and without using domain-specific knowledge, where the models with the domain specific knowledge outperform. Our proposed semi-supervised and supervised models are compared with the other existing approaches, where our models trained with the domain specific knowledge provided better performance. We also compared the per class accuracy for both the supervised and semi-supervised models, where all the maneuver class accuracy for supervised model was above

关键词： Data models Accelerometers Vehicles Smart phones Time series analysis Safety Gyroscopes Driving maneuver classification domain specific knowledge lstm autoencoder semi-supervised learning transfer learning

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Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark

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IEEE ACCESS 2021年 9卷 16866-16877页

作者： Uddin, Md Azher Joolee, Joolekha Bibi Sohn, Kyung-Ah Ajou Univ Dept Artificial Intelligence Suwon 16499 South Korea Kyung Hee Univ Dept Comp Sci & Engn Global Campus Yongin 17104 South Korea Ajou Univ Dept Software & Comp Engn Suwon 16499 South Korea

Facial expressions are the most common medium for expressing human emotions. Due to the wide range of real-world applications, facial expression understanding has received extensive attention from researchers. One of the most vital issues of facial expression recognition is the extraction and modeling of the temporal dynamics of facial emotions from videos. Additionally, the rapid growth of video data from various multimedia sources is becoming a serious concern. Therefore, to address these issues, in this paper, we introduce a novel approach on top of Spark for facial expression understanding from videos. First, we propose a new dynamic feature descriptor, namely, the local directional structural pattern from three orthogonal planes (LDSP-TOP), which analyzes the structural aspects of the local dynamic texture. Second, we design a 1-D convolutional neural network (CNN) to capture additional discriminative features. Third, a long short-term memory (lstm) autoencoder is employed to learn the spatiotemporal features. Finally, an extensive experimental investigation is carried out to demonstrate the performance and scalability of the proposed framework.

关键词： Facial expression understanding local directional structural pattern from three orthogonal planes 1-D CNN lstm autoencoder

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Detecting CAN overlapped voltage attacks with an improved voltage-based in-vehicle intrusion detection system

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JOURNAL OF SYSTEMS ARCHITECTURE 2023年第1期143卷

作者： Yin, Long Xu, Jian Wang, Chen Wang, Qiang Zhou, Fucai Northeastern Univ Software Coll Shenyang 110000 Liaoning Peoples R China Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing 100093 Peoples R China

The in-vehicle voltage-based intrusion detection system (VIDS) can recognize the identity of the ECUs and detect the physical layer attacks that cannot be detected by the content-based or frequency-based IDS. However, recent research in VIDS has discovered an overlapped voltage attack that can bypass the existing VIDS by generating overlapped voltage signals from two compromised ECUs to distort the fingerprint of the target ECU. The existing solutions need to change the communication protocol or network topology to mitigate the attack, which may involve additional development costs or scheduling mechanisms to coordinate the tasks. In this paper, motivated by not changing the network topology and protocols, we propose an improved VIDS that can efficiently detect the overlapped voltage attack of the in-vehicle CAN network. Before retraining the VIDS classifier model, we add the mechanism for detecting and filtering overlapped signals in the voltage samples by applying the Long Short-Term Memory (lstm) autoencoder network to preserve the fingerprint characteristics of the benign waveform in the original samples. First, we establish an lstm autoencoder model with benign voltages collected in a usual scenario and then apply the automatic thresholding approach to obtain the threshold to separate the overlapped and benign voltages. Next, we filter the overlapped voltages and extract the fingerprint features from the filtered voltage samples to predict the classification of the masquerade CAN packet during the attack. Finally, we evaluate the performance and attack success rate of the proposed VIDS on a CAN bus testbench. The results show that the proposed VIDS can effectively detect the overlapped voltage attack and identify the compromised ECU mimicking the distorted voltage fingerprints with an accuracy of 99.4%. The attack success rate of the proposed VIDS drops to 0 similar to 18%, which is about 60 similar to 95% for the existing VIDS.

关键词： Voltage-based IDS DUET attack Manipulated overlapped voltage attack lstm autoencoder

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SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

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ENERGIES 2022年第18期15卷 6659-6659页

作者： Alkhayat, Ghadah Hasan, Syed Hamid Mehmood, Rashid King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Informat Syst Jeddah 21589 Saudi Arabia King Abdulaziz Univ High Performance Comp Ctr Jeddah 21589 Saudi Arabia

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (lstm-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The lstm-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution,

关键词： solar energy forecasting generalizability long short-term memory (lstm) gated recurrent unit (GRU) convolutional neural network (CNN) hybrid CNN-bidirectional lstm lstm autoencoder

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Improving performance of one-class classifiers applied to anomaly detection in oil wells

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期218卷

作者： Machado, Andre Paulo Ferreira Vargas, Ricardo Emanuel Vaz Ciarelli, Patrick Marques Munaro, Celso Jose Univ Fed Espirito Santo Dept Engn Elect Vitoria ES Brazil Petr Brasileiro SA Regencia ES Brazil

The prompt detection and diagnosis of anomalies in oil wells are fundamental to reduce production losses, maintenance costs and to avoid environmental damage. In this paper, a proposal for detecting such anomalies using Long Short-Term Memory (lstm) autoencoder and one-class Support Vector Machine (SVM) is presented. A public dataset with instances of eight types of common anomalies characterized by eight process variables is used for training and performance evaluation. A methodology is proposed for training and performance evaluation. Faulty data are used as the target class for one-class classifiers. A time-shift of labels is proposed to improve the discrimination of normal and faulty data during the training phase. Criteria for selecting the time-shifting of the labels are proposed as well. A performance metric is proposed by computing the average of Specificity, F1, and instance identification performance. The detection time compared to the transient time of the fault was also computed. The analyses are performed with emphasis on two classes of anomalies, with slow and fast dynamics. For the fault with fast dynamic, performance was increased by 14% for lstm with no improvement for SVM. For the slow fault, the performance was increased by 41% for lstm and 21% for SVM. A noticeable reduction of detection time was observed for the slow fault, from 100% to 59.34% for lstm and 87.7% to 64.51% for SVM (100% corresponds to the transient time of the fault). A comparison with random forest and decision tree classifiers presented in the literature and applied to the same classes of anomalies shows the superiority of the proposed approach and methods. The special attention given to the metrics used for evaluating classifier performance and the time to detect the anomalies in the instances show that apparently good results measured by F1 and accuracy sometimes hide some weakness.

关键词： lstm autoencoder One-class SVM Fault detection and classification Oil well monitoring Multivariate time series classification

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Anomaly Detection in Electricity Consumption Data using Deep Learning 21

Anomaly Detection in Electricity Consumption Data using Deep...

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21st IEEE International Conference on Environment and Electrical Engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

作者： Kardi, Mohammad AlSkaif, Tarek Tekinerdogan, Bedir Catalao, Joao P. S. Tishreen Univ Fac Informat Latakia Syria Wageningen Univ & Res Informat Technol Grp Wageningen Netherlands Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal

ISBN: (纸本)9781665436137

Anomaly detection in electricity consumption data is one of the most important methods to identify anomalous events in buildings and electric assets, such as energy theft, metering defect, cyber attacks and technical losses. In this paper, a novel deep learning based approach is presented to detect anomalies in electricity consumption data one hour ahead of time. We address this challenge in two stages. First, we build an Long Short-Term Memory (lstm) based neural network model to predict the next hour sample. Second, we use another lstm autoencoder to learn the features of normal consumption. The output of the first stage is used as an input to the lstm autoencoder. The lstm autoencoder will learn the features of normal consumption and the input will be similar to output when applied. For anomalies, the input and output will be significantly different. The Exponential Moving Average (EMA) is used as a threshold and two types of anomalies are distinguished, local and global anomalies. Several weather features are considered in this study, such as pressure, cloud cover, humidity, temperature, wind direction and wind speed in addition to temporal and lag features. A feature selection method to find the optimal features that achieve good results is also implemented. We validate the proposed approach by comparing the detected anomalous consumption and the normal consumption within the same period, and the results demonstrate a drastic increase in electricity consumption during the anomalous periods. The results also show that the temporal and lag features have improved the efficiency and performance of the proposed method.

关键词： lstm autoencoder Deep Learning Anomaly detection Electricity consumption Anomalous consumption

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autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines 8

Autoencoder-based Condition Monitoring and Anomaly Detection...

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8th IEEE International Conference on Big Data (Big Data)

作者： Ahmad, Sabtain Styp-Rekowski, Kevin Nedelkoski, Sasho Kao, Odej TU Berlin Distributed & Operating Syst Berlin Germany

ISBN: (纸本)9781728162515

Rotating machines like engines, pumps, or turbines are ubiquitous in modern day societies. Their mechanical parts such as electrical engines, rotors, or bearings are the major components and any failure in them may result in their total shutdown. Anomaly detection in such critical systems is very important to monitor the system's health. As the requirement to obtain a dataset from rotating machines where all possible faults are explicitly labeled is difficult to satisfy, we propose a method that focuses on the normal behavior of the machine instead. We propose an autoencoder model-based method for condition monitoring of rotating machines by using an anomaly detection approach. The method learns the characteristics of a rotating machine using the normal vibration signals to model the healthy state of the machine. A threshold-based approach is then applied to the reconstruction error of unseen data, thus enabling the detection of unseen anomalies. The proposed method can directly extract the salient features from raw vibration signals and eliminate the need for manually engineered features. We demonstrate the effectiveness of the proposed method by employing two rotating machine datasets and the quality of the automatically learned features is compared with a set of handcrafted features by training an Isolation Forest model on either of these two sets. Experimental results on two real-world datasets indicate that our proposed solution gives promising results, achieving an average F1-score of 99.6%.

关键词： lstm autoencoder feature extraction anomaly detection condition monitoring rotating machines

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