Human behavioral pattern analysis-based anomaly detection system in residential space

Choi, Sang-Haeng; Kim, Changgyun; Kang, Yong-Shin; Youm, Sekyoung

doi:10.1007/s11227-021-03641-7

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…Behavior analysis includes many applications and methods, some of which are carried out from the medical view [ 18 ], and some of which use data mining methods, including wavelet neural network [ 19 ] and recurrent neural network [ 20 ]. On the basis of various methods, behavior analysis is also widely used in other fields, such as abnormal behavior detection in Internet DNS servers [ 21 ], artificial intelligence training of automatic driving [ 22 ], and anomaly detection of user behavior [ 23 ]. Behavior analysis in the medical field is a process of causal inference, historical experience, and data analysis to detect potential diseases.…”

Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.

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Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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“…Ke et al [43] presented a multi-scale structure-aware network to achieve a leading position in the publicly available HPE benchmark. Mask R-CNN [17] was proposed to perform human detection and key point localization in a single model. The proposed model crops human features from a feature map via the differentiable RoIAlign layer.…”

Section: D Single-person Pose Estimationmentioning

confidence: 99%

Human pose, hand and mesh estimation using deep learning: a survey

Toshpulatov

Lee

et al. 2022

J Supercomput

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Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

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“…It involves either one or both of the tasks: (a) identification of the abnormal data, e.g., noise, deviations or outliers from the original dataset and (b) discovery of novel data instances based on the knowledge learned according to the original dataset. Anomaly detection can be used for various goals, [8] such as fraud detection, data quality analysis, security scanning, process and system monitoring, image/video surveillance, spam detection, malicious insider attack detection, data cleansing prior to training statistical models, human behavior analytics [9], and sensor-fault detection [10].…”

Section: Background and Related Work A Background 1) Anomaly Detectio...mentioning

confidence: 99%

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

2021

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Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.

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Human behavioral pattern analysis-based anomaly detection system in residential space

Cited by 11 publications

References 25 publications

Research on User Behavior Based on Higher-Order Dependency Network

Research on User Behavior Based on Higher-Order Dependency Network

Human pose, hand and mesh estimation using deep learning: a survey

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

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