Anomaly Detection Research Articles

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines. We collected sensor-based data from CNC/MCT machines and applied various preprocessing techniques to prepare the dataset. Our experimental results demonstrate that the 1D-CNN model achieves a higher accuracy of 91.57% compared to traditional machine learning classifiers and other deep learning models, including Random Forest (RF) at 89.71%, multi-layer perceptron (MLP) at 87.45%, XGBoost at 89.67%, logistic regression (LR) at 75.93%, support vector machine (SVM) at 75.96%, K-nearest neighbors (KNN) at 82.93%, decision tree at 88.36%, naïve Bayes at 68.31%, long short-term memory (LSTM) at 90.80%, and a hybrid 1D CNN + LSTM model at 88.51%. Moreover, our proposed 1D CNN model outperformed all other mentioned models in precision, recall, and F-1 scores, with 91.87%, 91.57%, and 91.63%, respectively. These findings highlight the efficacy of the 1D CNN model in providing optimal performance with an MCT machine’s dataset, making it particularly suitable for small manufacturing companies seeking to automate early fault detection and classification in CNC and MCT machines. This approach enhances productivity and aids in proactive maintenance and safety measures, demonstrating its potential to revolutionize the manufacturing industry.

Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection

Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in real-world environments. To address these challenges, we propose a Transformer model with a wavelet transform dynamic attention mechanism (WADT). The dynamic attention mechanism uses wavelet transform. It focuses adaptively on the most informative parts of the battery data to enhance the anomaly detection accuracy. We also developed a deep learning model with an improved Transformer architecture. This architecture is tailored for the complex dynamics of battery data time series. The model accounts for temporal dependencies and adapts to non-stationary behavior. Experiments on public battery datasets show our approach’s effectiveness. Our model significantly outperforms existing technologies with an accuracy of 0.89 and an AUC score of 0.88. These results validate our method’s innovation and effectiveness.

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification.

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of <7% and a very large dynamic range, covering cTnI concentrations over 6 orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing.

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

Satterthwaite Approximation of the Distribution of SPE Scores: An R-Simulation-Based Improvement of the R-PCA-Based Outlier Detection Method

Satterthwaite Approximation of the Distribution of SPE Scores: An R-Simulation-Based Improvement of the R-PCA-Based Outlier Detection Method

Gait Anomaly Detection With Low-Cost and Low-Resolution Infrared Sensor Arrays

Gait Anomaly Detection With Low-Cost and Low-Resolution Infrared Sensor Arrays

Bearing remaining life prediction method based on ARAD -ELN and multi-stage wiener process

Abstract Stochastic process-based models are extensively utilized in health assessments and Remaining Useful Life (RUL) predictions of bearings. Nevertheless, bearings in actual operation undergo multiple degradation stages, each characterized by its unique trend of degradation. The application of a singular stochastic process for RUL prediction falls short of achieving optimal performance. Consequently, this paper introduces a multi-stage Wiener process-based approach for the prediction of bearings' RUL. Initially, to tackle the challenge of imbalanced sample sizes across different degradation stages of bearings, an ensemble learning-based neural network (ELN) enhanced by ARIMA Residual Anomaly Detection (ARAD) for the identification of bearing degradation stages is proposed. Subsequently, taking into account the temporal, unit-to-unit, and nonlinear variabilities of the degradation process at each stage, a Wiener process-based multi-stage degradation model for bearings is developed. A method for parameter estimation and updating, utilizing Kalman filtering and Maximum Likelihood Estimation (K-M) is introduced. Finally, the proposed model undergoes validation using both simulated data and the XJTU-SY bearing dataset. Experimental results of three RUL predictions show that the proposed method leads the benchmark model with RMSE values of 3.61, 2.92 and 7.24 respectively, affirming that the proposed model can precisely classify equipment degradation stages and predict RUL with high accuracy and stability.&amp;#xD;

Machine Learning-Driven Anomaly Detection for Quality Assurance in Recycled Fiber Supply Chains

Machine Learning-Driven Anomaly Detection for Quality Assurance in Recycled Fiber Supply Chains

Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing

Anomalies and defects in the manufacturing process hinder operating efficiency and product quality. The Whale Optimization Algorithm (WOA) optimizes the XGBoost model for better anomaly identification by iteratively refining hyperparameters. Experiments using real-world manufacturing datasets prove proposed model works. Comparing the proposed model to traditional anomaly detection methods shows its superior performance in industry patent concept. The optimized XGBoost model's interpretability and anomaly detection features are also discussed. In this paper, WOA is applied in this work to optimize hyperparameters of XGBoost, a robust gradient boosting technique for accurate anomaly detection in manufacturing systems. Optimized XGBoost gained 1.00 precision value, 0.9 recall value and 0.96 f1-score for class 0.0 and gained a 0.95 precision value, 1.00 recall value, and a 0.97 f1-score for class 1.0. The proposed model gained 0.993 Train Score and 0.964 Test Score. Our findings suggest that integrating XGBoost with the WOA may uncover manufacturing process irregularities. Optimization improves detection accuracy and provides a flexible and interpretable framework, helping modern industrial processes maintain quality and efficiency. This research encourages machine learning optimization for industrial patent applications, advancing anomaly detection methods.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Exploring the association between skin microbiota and inflammatory skin diseases: a two-sample Mendelian randomization analysis.

Dysbiosis in the skin microbiome is closely associated with various inflammatory skin diseases. However, current research on the causal relationship between the skin microbiome and inflammatory skin diseases lacks comprehensive and detailed investigation. We used a two-sample Mendelian randomization (MR) approach to explore associations between the skin microbiome and seven inflammatory skin diseases, including acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria. The GWAS summary data for the skin microbiome was derived from 647 participants in two German population-based cohorts, and for the inflammatory skin diseases, they were sourced from the FinnGen consortium. Our primary MR analysis method was the inverse variance weighted (IVW) method, complemented by alternatives like MR-Egger regression, weighted median estimation, and constrained maximum likelihood. Sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-PRESSO outlier detection, were conducted to validate and stabilize our findings. We identified significant causal relationships between the skin microbiome and seven inflammatory skin diseases: acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria, with 7, 6, 9, 1, 7, 4, and 7 respective causal relationships for each disease. These relationships comprise 20 protective and 14 risk causal relationships. We applied the false discovery rate correction to these results. Sensitivity analysis revealed no significant pleiotropy or heterogeneity. Our study revealed both beneficial and detrimental causal relationships between diverse skin microbiota and inflammatory skin diseases. Additionally, the ecological niche of the skin microbiome was crucial to its functional impact. This research provided new insights into how skin microbiota impacted skin diseases and the development of therapeutic strategies.

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.

Machine learning-based outlier detection for pipeline in-line inspection data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation

Employing machine learning algorithms in the medical field has proven successful for some time now. Mostly computer vision techniques have been applied to medical images, while medical sound data has been somewhat overlooked. By using electronic stethoscopes, it is now possible to process both heartbeats and lung sounds. While some products are available for detecting anomalies in heartbeats, addressing lung-related anomalies presents a more intricate challenge. Applying a deep learning approach is hindered by insufficient data. Although some datasets do exist, the size and diversity of the data are too small for comprehensive analysis. This paper introduces a novel technique for detecting anomalies in lung sounds: first by combining two datasets, second by automatically segmenting each sound into respiratory cycles, and third by employing GFCCs as sound features.

Graph neural network-based anomaly detection for human cyber physical systems

ABSTRACT Human Cyber Physical Systems (HCPS) encompass humans, networks, and physical devices, characterized by intricate interactions and interdependencies. The complexity of HCPS networks makes them vulnerable to network issues and cyberattacks, potentially resulting in network paralysis and significantly impacting the reliable operation of HCPS. Therefore, precise anomaly detection is imperative for HCPS. To tackle this issue, we integrate HCPS with the Software Defined Networking (SDN) infrastructure layer and propose an anomaly detection strategy based on Graph Neural Networks (GNN). Our approach utilizes Graph Sample and Aggregates (GraphSAGE) to capture adjacent feature information of nodes, revealing hidden relationships within the graph. These embeddings are then combined with Graph Attention Networks (GAT), which calculate attention coefficients between a node and its neighboring nodes, representing the importance of neighboring nodes to the central node. Finally, a fully connected layer is employed to obtain anomaly node labels. To assess the performance of our method, we conduct experiments using real datasets and compare them with other advanced anomaly detection methods in terms of accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). Our method accurately detects anomalous nodes, offering a viable solution for practical applications.

Decoding skin mysteries: Unveiling the link between microbiota and keloid scars through a Mendelian randomization study.

The cause of keloids remains unclear, but studies suggest a link between skin microbiota and keloid formation. However, the causal relationship has not been confirmed. This study utilized Genome-Wide Association Studies (GWAS) data from 2 population-based German cohorts, comprising a total of 1656 skin samples. To bolster the reliability of our results, we incorporated GWAS data from 3 keloid cohorts, encompassing 2555 patients and 870,556 controls (GWAS ID: keloid1, ebi-a-GCST90018874; keloid2, bbj-a-131; keloid3, ebi-a-GCST90018654). Subsequently, we employed bidirectional 2-sample Mendelian randomization (MR) analysis to probe the causal relationship between the variables. The primary method employed was the inverse-variance weighted (IVW) method, supported by heterogeneity analysis, horizontal pleiotropy testing, outlier detection, and "leave-one-out" sensitivity analysis. By synthesizing the results from 3 groups of MR analyses, we discovered a negative causal association between a.ASV063 [Finegoldia (unc.)] located on the volar forearm and keloid disease (IVW (keloid1) odds ratio (OR): 0.939, 95% confidence interval (CI): 0.886-0.994, P = .032; IVW (keloid2) OR: 0.897, 95% CI: 0.813-0.990, P = .031; IVW (keloid3) OR: 0.900, 95% CI: 0.825-0.981, P = .017). Similarly, a negative causal relationship may also exist between the genus: Bacteroides from the antecubital fossa and keloid disease (IVW (keloid1) OR: 0.928, 95% CI: 0.884-0.973, P = .002; IVW (keloid2) OR: 0.891, 95% CI: 0.820-0.968, P = .007; IVW (keloid3) OR: 0.918, 95% CI: 0.849-0.992, P = .030). Additionally, no reverse causation was found, with all analyses showing no signs of horizontal pleiotropy or heterogeneity. This study offers new insights for the prevention and treatment of keloids.

Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction; and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method.

Isolation Forest Anomaly Detection Algorithm Based On Multi-level Sub-subspace Partition

In the research of credit loan fraud detection, the isolation forest algorithm has attracted much attention because of its ability to efficiently process large-scale data sets. However, when facing high-dimensional data, the performance of the isolation forest algorithm is easily affected, resulting in deviation of the detection results. In order to solve the above problems, this paper proposes an isolation forest anomaly detection algorithm based on multi-level sub-subspace division. Firstly, the random forest algorithm is used to evaluate the importance of each feature, and the data is divided into different subspaces according to the importance of each feature, and the corresponding weight is assigned to each subspace. Then, the isolation forest algorithm is applied in each subspace for anomaly detection, and the anomaly score of each subspace is obtained. Finally, the anomaly score and weight of each subspace were combined to obtain the final anomaly detection score. In order to evaluate the effectiveness of the algorithm, the proposed algorithm was compared with other four algorithms on the credit loan fraud data set. The results show that the AUC index, accuracy, recall rate and F1 score of the proposed algorithm are higher than those of the comparison algorithms, showing high effectiveness.