An Outlier Detection Approach on Credit Card Fraud Detection Using Machine Learning: A Comparative Analysis on Supervised and Unsupervised Learning

Anomaly detection using unsupervised machine learning algorithms: A simulation study

Identifying geochemical anomalies associated with tungsten polymetallic mineralization using geographical detector and unsupervised machine learning methods: Application to the Nanling metallogenic belt, China

Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of controlled experimental settings. The fact that such artifact is often readily identifiable by eye suggests that unsupervised machine learning algorithms may be a promising option that do not require manually labeled training datasets. Existing methods are often heuristic-based, not generalizable, or developed for controlled experimental settings with less artifact. In this study, we test the ability of three such unsupervised learning algorithms, isolation forests, 1-class support vector machine, and K-nearest neighbor distance, to remove heavy cautery-related artifact from electrodermal activity (EDA) data collected while six subjects underwent surgery. We first defined 12 features for each halfsecond window as inputs to the unsupervised learning methods. For each subject, we compared the best performing unsupervised learning method to four other existing methods for EDA artifact removal. For all six subjects, the unsupervised learning method was the only one successful at fully removing the artifact. This approach can easily be expanded to other modalities of physiological data in complex settings.Clinical Relevance- Robust artifact detection methods allow for the use of diverse physiological data even in complex clinical settings to inform diagnostic and therapeutic decisions.

Chapter 4 - Unsupervised machine learning: clustering algorithms

Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.

Benchmarking machine learning algorithms by inferring transportation modes from unlabeled GPS data

Abstract: We are living in the technology world in which Development of communication and e-commerce has made credit card as the most common technique of payment for both online and offline mode of purchases. As the e-commerce has increased, the buying and selling the product online is becoming very easy and comfortable to everyone in the daily life. Due to this, the online payment and online banking with credit card is increased. The fraud also happens when we lost our credit card or it get stole. Recently due to COVID-19 everything has become contactless so the use of credit card has increased. The transaction is done on online shopping and online payment is done from many places so it become difficult to recognise the real transaction and the fraud transaction. So, it becomes difficulty for the bank to stop fraud detection. In this paper it clearly explains about how fraud can be detected by using the Unsupervised Machine Learning using the algorithm and by using the algorithm technique like Isolated Forest, Local Outlier Factor and One class SVM. Keywords: Introduction, Machine learning, Supervised learning, Unsupervised learning

Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China

Credit card transactions have become common place today and so is the frauds associated with it. One of the most common modus operandi to carry out fraud is to obtain the card information illegally and use it to make online purchases. For credit card companies and merchants, it is in-feasible to detect these fraudulent transactions among thousands of normal transactions. If sufficient data is collected and made available, machine learning algorithms can be applied to solve this problem. In this work, popular supervised and unsupervised machine learning algorithms have been applied to detect credit card frauds in a highly imbalanced dataset. It was found that unsupervised machine learning algorithms can handle the skewness and give best classification results.

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.

In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious RF fingerprinting of LoRa modulated chirps. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means for sensor authentication within critical infrastructure deployment. Here, an unsupervised ML algorithm is used to rapidly train an artificial neural network (ANN) matrix creating self-organizing maps (SOMs) for each authentic transmitter and a potential rogue node. A general classifier can be trained on the SOMs for precisely profiling each transmitter as either genuine or rogue. By means of experimental validation, this methodology demonstrated cent-percent success in recognizing each transmitter, either being a real or a rogue node.

Data anomaly detection is crucial for maintaining the integrity, security, and efficiency of systems, helping to detect and respond to abnormal events promptly. In this paper, a hybrid approach of a combination of two unsupervised machine learning algorithms, Isolation Forest (IF) and One Class Support Vector Machine (OCSVM) with one supervised machine learning algorithm Random Forest Classifier as an ensemble method is used in credit card transactions system for data anomaly detection. For this, credit card fraud detection dataset from Kaggle is used for anomaly detection. As credit card transactions data is huge, varying and unlabeled, unsupervised algorithms like IF and OCSVM become suitable for it. The ability of OCSVM of defining of what constitutes the anomaly, power of isolation forest to detect outliers efficiently and use of random forest classifier as an ensemble method of decision trees, complementing both IF and OCSVM are associated to increase accuracy of a system. The result of this paper shows that this hybrid approach provides more accuracy than individual algorithm. Keywords: Anomaly detection, Isolation Forest, One Class SVM, Random forest classifier, Hybrid approach

Identity-based attack detection using received signal strength in MIMO systems

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures.

This study proposed an anomaly detection technique in an industrial control system using supervised and unsupervised machine learning algorithms. For the dataset for learning, the HIL-based Augmented ICS (HAI) dataset provided for the study on security in industrial control systems was used. For the learning model, Light Gradient Boosted Machine -- a supervised learning algorithm and One-Class Support Vector Machine and Isolation Forest as unsupervised learning algorithms were employed. The proposed technique is presented in this paper, which is organized as follows: Feature selection, Data preprocessing, Hyperparameter optimization and verification, and Experiment and analysis of results. The performance difference according to the algorithm and model configuration was exhibited through the experimental results. In addition, issues to be considered in model configuration and future study directions for anomaly detection techniques in industrial control systems were presented based on the experimental results.