scholarly journals Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1622
Author(s):  
Luis de-Marcos ◽  
José-Javier Martínez-Herráiz ◽  
Javier Junquera-Sánchez ◽  
Carlos Cilleruelo ◽  
Carmen Pages-Arévalo
Keyword(s):  
Machine Learning ◽  
Ensemble Methods ◽  
Classification Problem ◽  
Probabilistic Methods ◽  
Mobile Environment ◽  
User Interactions ◽  
Machine Learning Classifiers ◽  
Continuous Authentication ◽  
Learning Classifiers ◽  
Ensemble Algorithms

Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.

scholarly journals Credit Card Fraud Detection in Payment Using Machine Learning Classifiers

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Maad M. Mijwil ◽  
Israa Ezzat Salem
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Credit Card ◽  
Fraud Detection ◽  
Classification Problem ◽  
Recall Rate ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Precision Recall Curve ◽  
Bagging Ensemble

The fraud detection in payment is a classification problem that aims to identify fraudulent transactions based individually on the information it contains and on the basis that a fraudster's behaviour patterns differ significantly from that of the actual customer. In this context, the authors propose to implement machine learning classifiers (Naïve Bayes, C4.5 decision trees, and Bagging Ensemble Learner) to predict the outcome of regular transactions and fraudulent transactions. The performance of these classifiers is judged by the following ways: precision, recall rate, and precision-recall curve (PRC) area rate. The dataset includes more than 297K transactions via credit cards in September 2013 and November 2017 that have been collected from Kaggle platform, of which 3293 are frauds. The performance PRC ratio of machine learning classifiers is between 99.9% and 100%, which confirms that these classifiers are very good at identifying binary classes 0 in the dataset. The results of the tests have proved that the best classifier is C4.5 decision trees. This classifier has the best accuracy of 94.12% in prediction of fraudulent transactions.

Toward False Event Detection and Quarry Blast versus Earthquake Discrimination in an Operational Setting Using Semiautomated Machine Learning

2021 ◽  
Author(s):  
Alexandra Renouard ◽  
Alessia Maggi ◽  
Marc Grunberg ◽  
Cécile Doubre ◽  
Clément Hibert
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
High Signal ◽  
Small Magnitude ◽  
Magnitude Distribution ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Natural Seismicity

Abstract Small-magnitude earthquakes shed light on the spatial and magnitude distribution of natural seismicity, as well as its rate and occurrence, especially in stable continental regions where natural seismicity remains difficult to explain under slow strain-rate conditions. However, capturing them in catalogs is strongly hindered by signal-to-noise ratio issues, resulting in high rates of false and man-made events also being detected. Accurate and robust discrimination of these events is critical for optimally detecting small earthquakes. This requires uncovering recurrent salient features that can rapidly distinguish first false events from real events, then earthquakes from man-made events (mainly quarry blasts), despite high signal variability and noise content. In this study, we combined the complementary strengths of human and interpretable rule-based machine-learning algorithms for solving this classification problem. We used human expert knowledge to co-create two reliable machine-learning classifiers through human-assisted selection of classification features and review of events with uncertain classifier predictions. The two classifiers are integrated into the SeisComP3 operational monitoring system. The first one discards false events from the set of events obtained with a low short-term average/long-term average threshold; the second one labels the remaining events as either earthquakes or quarry blasts. When run in an operational setting, the first classifier correctly detected more than 99% of false events and just over 93% of earthquakes; the second classifier correctly labeled 95% of quarry blasts and 96% of earthquakes. After a manual review of the second classifier low-confidence outputs, the final catalog contained fewer than 2% of misclassified events. These results confirm that machine learning strengthens the quality of earthquake catalogs and that the performance of machine-learning classifiers can be improved through human expertise. Our study promotes a broader implication of hybrid intelligence monitoring within seismological observatories.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals A review of infant cry analysis and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Ji ◽  
Thosini Bamunu Mudiyanselage ◽  
Yutong Gao ◽  
Yi Pan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Signal Analysis ◽  
Future Research ◽  
Prosodic Features ◽  
Infant Cry ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Processing Techniques

AbstractThis paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

Sign in / Sign up

Export Citation Format

Share Document