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10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Aaptha, B., V Kadali, Phani Gupta, Saurabh Saju, Vinolia Continental Autonomous Mobil India Pvt Ltd Radar Percept Grp Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

In recent years, the increasing demand for technological advances has driven the usage of state-of-the-art technologies like machine learning in automated systems. In this study, a novel framework, Km-IFC, is presented that effectively estimates the course of the road using radar detections, and its results are discussed. It uses a combination of the K-means clustering algorithm for separation of data, the isolation forest algorithm for removal of outliers, and the least squares method to estimate the curve parameters of the roads' course. The estimated curve using radar data is compared with the vehicle-driven path (considered as ground truth). Km-IFC achieved favorable results in both optimal and non-optimal scenarios, with almost negligible differences between the estimated curve and ground truth. Consequently, the estimated curve had an average difference of 0.365 meters and an overall confidence coefficient of 0.88, inclusive of all scenarios. Furthermore, the study conducted takes in raw sensor detection data and estimates the roads' course without relying on any external sensor outputs, thus making it very computationally efficient.

关键词： K-means clustering algorithm isolation forest algorithm Curve fitting Radar detections Road trajectory

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Sea-Surface Floating Small Target Detection by Multifeature Detector Based on isolation forest

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 704-715页

作者： Xu, Shuwen Zhu, Jianan Jiang, Junzheng Shui, Penglang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Mcmaster Univ Hamilton ON L8S 4L8 Canada Nanjing Res Inst Elect Technol Signal Proc Dept L8S4L8 Nanjing Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

In this article, a multifeature detector based on isolation forest (iforest) algorithm is developed to detect floating small targets in sea clutter. The conventional multifeature detector can only process three features or less. The proposed detector aims to break the limitation of feature dimensions' number of the existed feature-based detectors and to improve the detection performance. It transforms the detection of floating small target into an anomaly detection problem in a high-dimensional feature space, breaking the limitation of the number of features. First, a modified isolation forest is constructed from multiple features extracted from sea clutter. Meanwhile, the relative Doppler coefficient of variation is proposed and added into the feature library. Then, taking the average path length as detection statistic, the detection threshold is obtained by Monte-Carlo technique at the given false alarm probability. Finally, the final decision is made by comparing the path length calculated from the cell under test of radar returns with the detection threshold. Detection performances are evaluated based on twenty measured IPIX radar datasets. The experiment results show that the multifeature detector based on isolation forest can obtain a significant performance improvement and has lower computation cost compared with the existed detectors.

关键词： Detectors Clutter Feature extraction Object detection Radar cross-sections forestry Feature detection floating small target detection isolation forest algorithm sea clutter

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Unveiling the Dark Patterns in E-Commerce Web Sites 5

Unveiling the Dark Patterns in E-Commerce Web Sites

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kirthiga, N. Reddy, Bommana Manideep Mahalakshmi, Boggavarapu Siri Risthitha, Bethala Teja, Dammalapa Kalasalingam Academy of Research and Education Departement of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331530013

This research study investigates the prevalence and impact of 'dark patterns' in the digital world. Dark patterns are deceptive design techniques employed by websites and applications to manipulate user behavior and induce actions that may not be in the user's best interest. This study examines the various forms of dark patterns, analyzes their impact on user autonomy and trust, and explores potential strategies to mitigate their use. By raising awareness about dark patterns and developing effective countermeasures, we can create a more ethical and trustworthy digital environment for all users. © 2024 IEEE.

关键词： Dark Patterns Data Exploration Digital Ethics isolation forest algorithm User Experience

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Detection and Tracking of Atypical Billings Aiming at Minimizing Non-Technical Losses in Distribution Companies in Brazil

Detection and Tracking of Atypical Billings Aiming at Minimi...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Bettiol, Arlan Luiz Gomides, Phablo Sullyvan Figueiredo, Douglas Barbonaglia Sathler Medeiros, Renato Neo Domino Pesquisa em Sistemas Elétricos Santa Catarina Florianópolis Brazil Companhia Hidrelétrica São Patrício Goiás Ceres Brazil Empresa Luz e Força Santa Maria Espírito Santo Colatina Brazil

ISBN: (纸本)9798350327816

The work addresses the development of a computational platform to identify atypical billings in low voltage consumer units using Artificial Intelligence techniques, specifically the isolation forest algorithm. The proposed methodology allows detecting consumer units with peculiar changes in consumption and/or evidence of fraud. The algorithm analyzes a set of historical data and identifies 'atypical' monthly billings among consumer units with the same load profile, generating a list of candidate units for subsequent inspection in the field. Preliminary tests carried out in a medium-sized distribution company showed an assertiveness rate of 91.70%, indicating the effectiveness of the proposed algorithm for identifying anomalies in the monthly billing data of consumer units. © 2023 IEEE.

关键词： Artificial intelligence Atypical Billings Combating Commercial Losses Commercial Loss Detection isolation forest algorithm

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Detecting Network Attacks on Software Configured Networks Using the Isolating forest algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2021年第8期55卷 1039-1050页

作者： Stepanov, M. D. Pavlenko, E. Yu Lavrova, D. S. Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

An approach is proposed to detect network attacks in software-defined networks. These networks are specific from a security standpoint, so the modified algorithm of the isolation forest is taken as a network security basis. The results of experimental studies are presented featuring optimal parameters for conventional and enhanced isolation forest algorithms. Based on the results, a conclusion is made about the efficiency of isolation forest to detect network attacks in software-configured networks.

关键词： software-configured networks network attacks isolation forest algorithm enhanced isolation forest algorithm software-configured network security attack detection

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Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China's Diverse Latitudes

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REMOTE SENSING 2023年第11期15卷 2951-2951页

作者： Zhang, Haiyang Zhang, Yao Gao, Tingyao Lan, Shu Tong, Fanghui Li, Minzan China Agr Univ Key Lab Smart Agr Syst Integrat Minist Educ Beijing 100083 Peoples R China

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iforest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iforest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthe

关键词： Sentinel-2 MSI Landsat 8 OLI Pearson correlation coefficient ordinary least squares regression isolation forest algorithm sensor integration

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Comprehensive Analysis for Fraud Detection of Credit Card through Machine Learning

Comprehensive Analysis for Fraud Detection of Credit Card th...

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3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Roy, Parth Rao, Prateek Gajre, Jay Katake, Kanchan Jagtap, Arvind Gajmal, Yogesh MIT ADT Univ Informat Technol Dept MIT Sch Engn Pune Maharashtra India

ISBN: (纸本)9781728185194

A credit card which remains a very widespread compensation method is accepted online & offline that provides cashless transactions. It's an easy, suitable then very common to make payments and other transactions. With the increase of developments credit card frauds are also growing. Financial deception is severely cumulative in the worldwide statement enhancement. Billion dollars are at loss due to these fraudulent acts. These actions are accomplished so gracefully that it is similar to genuine transactions. Therefore, simple design practices and other less composite methods will be nonoperating. In directive to minimalize disorder and bring order in place having a well-organized method of fraud detection has become a need for all banks. In this paper we used Machine learning, to notice Master Card fake transactions. Also, IFA and OD approaches are applied towards enhance finest answer on behalf of scam finding problems. Approaches remain proved toward diminish untrue alarm proportions also upsurge scam discovery proportion. Dataset of card dealings stays obtained since European card owners having 284,807 communications. To detect and prevent the fraudulent, slightly of these approaches can be applied on bank credit card scam detection system, to detect and prevent the scam.

关键词： isolation forest algorithm Credit card fraud Local Outlier Factor Machine Learning Logistic Regression

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Anomaly electricity detection method based on entropy weight method and isolated forest algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Jianyuan Gu, Chengcheng Liu, Kechen Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin Peoples R China

This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors.

关键词： abnormal power consumption detection isolation forest algorithm kmeans clustering algorithm entropy weight method principal component dimension 11

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Hybrid AI- and Blockchain-Powered Secure Internet Hospital Communication and Anomaly Detection in Smart Cities

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Processes 2025年第5期13卷 1466-1466页

作者： Xiaofeng Wang Xiaoguang Yue Noshina Tariq Ahthasham Sajid College of Management Shenzhen University Shenzhen 518060 China Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad 44000 Pakistan Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad 46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

Internet of Things (IoT) devices have revolutionized real-time monitoring and distant patient care in smart cities’ healthcare systems. However, this advancement has come with several issues, such as data security, scalability, operational efficiency, and fault tolerance. Previous approaches are not well suited to the real-time processing of IoT data in healthcare, given the low latency, high throughput, and effective anomaly detection needed for such a task. Given these challenges, this paper proposes a hybrid Artificial Intelligence (AI)- and blockchain-based IoT governance framework for Internet hospitals using Proof-of-Authority (PoA) in smart cities. It encompasses application of the enhanced RSA for secure data transmission, real-time anomaly detection through the isolation forest algorithm, and a private blockchain architecture designed for high scalability. It effectively detects tampering and replay attacks to minimize illegitimate and unauthorized access or manipulation of patients’ data. The proposed framework achieves relatively significant improvements over a state-of-the-art baseline model. It has cut the transaction response time by 50%, doubled the Throughput Per Second (TPS), and attained a 100% detection performance in anomalies. Comparative analysis reveals its linear scalability with increasing workload, ensuring consistent performance under varying transaction volumes. This study’s findings highlight the proposed framework’s potential to mitigate key issues in IoT-enabled Internet hospitals in smart cities.

关键词： anomaly detection blockchain distributed role-based access control electronic health records internet hospital internet of things isolation forest algorithm smart cities proof-of-authority Rivest–Shamir–Adleman (RSA)

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An integrated data-driven framework for surface water quality anomaly detection and early warning

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JOURNAL OF CLEANER PRODUCTION 2020年 251卷 119145-000页

作者： Liu, Jie Wang, Peng Jiang, Dexun Nan, Jun Zhu, Weiyu Harbin Inst Technol Sch Environm State Key Lab Urban Water Resource & Environm Harbin 150090 Peoples R China Harbin Univ Sch Informat Engn Harbin 150086 Peoples R China

The surface water quality anomaly detection for rapid early warning is essential to prevent potential harmful compounds, resulting from river environmental spills or intentional releases, from dispersing in large scale. In this study, an effective data-driven framework for surface water quality anomaly detection is developed to provide early warnings for dealing with river environmental pollution in advance. The developed framework is constructed by an integration of Bayesian autoregressive (BAR) model for water quality variation prediction and isolation forest (IF) algorithm for water quality anomaly detection. First, an autoregressive method based on Bayesian inference is used to forecast the tendencies of water quality variations. Second, an IF algorithm is applied to identify the features of water quality anomalies using the prediction residuals obtained in the previous stage. The integration framework is then applied to analyze and detect the surface water quality variations and anomalies in Potomac River of West Virginia, USA, comparing with prediction-based anomaly detection method, classification-based anomaly detection method, and different scenarios. The results demonstrate that the developed integration framework not only could enhance water quality anomaly detection accuracy, but also effectively provide early warning for emergency operations in a quick response. (C) 2019 Published by Elsevier Ltd.

关键词： Surface water quality Anomaly detection Early warning Bayesian autoregressive model isolation forest algorithm

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