An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a Visitor

Howedi, Aadel; Lotfi, Ahmad; Pourabdollah, Amir

doi:10.3390/e22080845

Cited by 25 publications

(21 citation statements)

References 57 publications

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“…Data that is contaminated with outliers could lead to poor performance by the model. The authors in [25] utilised ADL data collected with ambient sensors to detect abnormal activity instances using different entropy measures. Activities with entropy value exceeding a certain range are predicted as outliers.…”

Section: Related Workmentioning

confidence: 99%

“…a is NOT an outlier −1 a is an outlier [16] Inferred ADL data CAAB and regression model Correlation and RMSE [22] Inferred ADL data Causal association rule and Markov logic network Accuracy, precision and recall [49] Inferred ADL data OC-SVM Accuracy [2,5] Inferred ADL data IADL-C, Regression and classification model RMSE, correlation, F-Score, accuracy, sensitivity and ROC-AUC [7] Raw binary data CNN + LSTM Sensitivity, specificity [30] Raw binary and inferred ADL data ESN RMSE [24] Inferred ADL data DBSCAN Precision and recall [27] Inferred ADL data OC-SVM Error rate, F-measure and accuracy [6] Raw binary data Vanilla RNN, GRU, LSTM Accuracy [18] Inferred ADL data DBSCAN EER [20] Inferred ADL data HMM + FRBS Accuracy, precision, recall, F-measure, ROC-AUC [50] Inferred ADL and synthetic data Ensemble (OC-SVM, LOF, iForest, RCE) Accuracy [34,35] Raw binary data SOM Accuracy [25] Raw binary data Entropy measures Accuracy, precision and recall [47] Inferred ADL data HCRF & SVM Accuracy [4] ADL data (video stream) 3D-CNN + LSTM Accuracy, precision, recall, F-measre and specificity [37] ADL data (depth) Fuzzy inference system Percentage error rate [32] Inferred ADL data (ODHMAD) Precision, false alarm and false prediction rate [51] Inferred ADL data LSTM, CNN, CNN-LSTM and autoencoder Accuracy…”

Section: The Radial Basis Function (Rbf) Kernel With Spread Parametermentioning

confidence: 99%

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Detecting Anomaly and Its Sources in Activities of Daily Living

2021

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To support the independent living and improve the quality of life for the increasing ageing population, system for monitoring their daily routine and detecting anomalies in the routine is required. Existing anomaly detection systems are unable to identify the sources of the abnormalities, thereby hindering the development of adaptive monitoring systems with reduced false prediction rate. In this paper, an approach for identifying the sources of abnormalities in human activities of daily living is proposed. Anomalies are detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. Subsequent activities deviating from the baseline are then classified as outliers or anomalies. An ensemble of one-class support vector machine, isolation forest, robust covariance estimator and local outlier factor is utilised for the anomaly detection achieving an accuracy of $$98\%$$ 98 % . The proposed approach for identifying anomaly sources is based on the concept of similarity measure using distance functions. Two methods for measuring the pairwise distance of the features of the activity data termed as one vs one similarity measure and one vs all similarity measure are proposed. Experimental evaluation of the proposed approach on activities of daily living datasets has shown the credibility of the proposed approach for utilisation in an in-home monitoring system.

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Section: Related Workmentioning

confidence: 99%

Section: The Radial Basis Function (Rbf) Kernel With Spread Parametermentioning

confidence: 99%

Detecting Anomaly and Its Sources in Activities of Daily Living

2021

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“…In this study, Shannon Entropy is utilized with an online machine learning method to detect malicious traffic including DDoS attacks and Flash Event traffic. Paper [27] presented anomaly detection in activities of daily living based on entropy measures.…”

Section: Entropy-based Technologiesmentioning

confidence: 99%

“…As the measure of uncertainty, entropy can be used to summarize feature distributions in a compact form [22]. ere are many forms of entropy, but only a few have been applied to network anomaly detection [23][24][25][26][27]. On this basis, we apply a Euclidean Distance-Based Multiscale Fuzzy Entropy (EDM-Fuzzy) algorithm which we proposed to detect abnormal network traffic as a useful supplement of other approaches.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy

Zhou

Xiao

Yang

et al. 2021

Security and Communication Networks

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The prosperity of mobile networks and social networks brings revolutionary conveniences to our daily lives. However, due to the complexity and fragility of the network environment, network attacks are becoming more and more serious. Characterization of network traffic is commonly used to model and detect network anomalies and finally to raise the cybersecurity awareness capability of network administrators. As a tool to characterize system running status, entropy-based time-series complexity measurement methods such as Multiscale Entropy (MSE), Composite Multiscale Entropy (CMSE), and Fuzzy Approximate Entropy (FuzzyEn) have been widely used in anomaly detection. However, the existing methods calculate the distance between vectors solely using the two most different elements of the two vectors. Furthermore, the similarity of vectors is calculated using the Heaviside function, which has a problem of bouncing between 0 and 1. The Euclidean Distance-Based Multiscale Fuzzy Entropy (EDM-Fuzzy) algorithm was proposed to avoid the two disadvantages and to measure entropy values of system signals more precisely, accurately, and stably. In this paper, the EDM-Fuzzy is applied to analyze the characteristics of abnormal network traffic such as botnet network traffic and Distributed Denial of Service (DDoS) attack traffic. The experimental analysis shows that the EDM-Fuzzy entropy technology is able to characterize the differences between normal traffic and abnormal traffic. The EDM-Fuzzy entropy characteristics of ARP traffic discovered in this paper can be used to detect various types of network traffic anomalies including botnet and DDoS attacks.

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“…Being able to detect anomalies has many applications, including in the fields of medicine and healthcare management [ 1 , 2 ]; in data acquisition, such as filtering out anomalous readings [ 3 ]; in computer security [ 4 ]; in media monitoring [ 5 ]; and many in the realm of public safety such as identifying thermal anomalies that may precede earthquakes [ 6 ], identifying potential safety issues in bridges over time [ 7 ], detecting anomalous conditions for trains [ 8 ], system level anomaly detection among different air fleets [ 9 ], and identifying which conditions pose increased risk in aviation [ 10 ]. Given a dataset, anomaly detection is about identifying individual data that are quantitatively different from the majority of other members of the dataset.…”

Section: Introductionmentioning

confidence: 99%

Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

Lindstrom

Jung

Larocque

2020

Entropy

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We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a “point approach”), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a “Fourier approach”). We refer to these approaches as Functional Kernel Density Estimation for Anomaly Detection as they both yield functionals that can score a time series for how anomalous it is. Both methods naturally handle missing data and apply to a variety of settings, performing well when compared with an outlyingness score derived from a boxplot method for functional data, with a Principal Component Analysis approach for functional data, and with the Functional Isolation Forest method. We illustrate the use of the proposed methods with aviation safety report data from the International Air Transport Association (IATA).

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An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a Visitor

Cited by 25 publications

References 57 publications

Detecting Anomaly and Its Sources in Activities of Daily Living

Detecting Anomaly and Its Sources in Activities of Daily Living

Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy

Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

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