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Background: The main purpose of this thesis is in electronic commerce, reliable anomaly detection systems are essential for maintaining security and improving user experiences, especially in the electronics industry. With the goal of filling in the gaps in the current anomaly detection methods, this study examines the efficacy of SVM as a fundamental algorithmic framework for anomaly detection in retail electronics online. The goal of the research is to better understand user interfaces and security protocols one-commerce platforms by spotting anomalies within user behavioral events. Objectives: To evaluate the effectiveness of SVM in identifying anomalies in user activity patterns, a rigorous experimental design comprising feature extraction, preprocessing, and model evaluation is used in the technique. Methods: The study establishes the foundation for analysis and model creation by utilizing data from the REES46 platform, which records a broad range of user interactions over an extended period of time. Utilizing this extensive dataset, the study explores the subtle aspects of user behavior and offers insights into SVM algorithm-based anomaly detection methods. The methodology’s rigorous data preprocessing and feature extraction ensured the dataset’s integrity, contributing to the model’s ef-fectiveness. Metrics including precision, recall, and F1-score were used to train and assess the SVM model after a thorough normalization of the dataset using Stan-dard Scaler. With an F1 score, a precision, and a recall. The model’s accuracy was further confirmed by a low Mean Squared Error (MSE), Prediction scatter plots and other visualizations. Results: The findings highlight the considerable potential of SVM-based anomaly detection systems to improve user experiences and strengthen security protocols in online retail settings. higher scores for the classification metrics. The model performed well, obtaining an F1 score of 0.93, a precision of 99per, and a recall of 1.00, d

Background: The main purpose of this thesis is in electronic commerce, reliable anomaly detection systems are essential for maintaining security and improving user experiences, especially in the electronics industry. With the goal of filling in the gaps in the current anomaly detection methods, this study examines the efficacy of SVM as a fundamental algorithmic framework for anomaly detection in retail electronics online. The goal of the research is to better understand user interfaces and security protocols one-commerce platforms by spotting anomalies within user behavioral events. Objectives: To evaluate the effectiveness of SVM in identifying anomalies in user activity patterns, a rigorous experimental design comprising feature extraction, preprocessing, and model evaluation is used in the technique. Methods: The study establishes the foundation for analysis and model creation by utilizing data from the REES46 platform, which records a broad range of user interactions over an extended period of time. Utilizing this extensive dataset, the study explores the subtle aspects of user behavior and offers insights into SVM algorithm-based anomaly detection methods. The methodology’s rigorous data preprocessing and feature extraction ensured the dataset’s integrity, contributing to the model’s ef-fectiveness. Metrics including precision, recall, and F1-score were used to train and assess the SVM model after a thorough normalization of the dataset using Stan-dard Scaler. With an F1 score, a precision, and a recall. The model’s accuracy was further confirmed by a low Mean Squared Error (MSE), Prediction scatter plots and other visualizations. Results: The findings highlight the considerable potential of SVM-based anomaly detection systems to improve user experiences and strengthen security protocols in online retail settings. higher scores for the classification metrics. The model performed well, obtaining an F1 score of 0.93, a precision of 99per, and a recall of 1.00, d

Background: The main purpose of this thesis is in electronic commerce, reliable anomaly detection systems are essential for maintaining security and improving user experiences, especially in the electronics industry. With the goal of filling in the gaps in the current anomaly detection methods, this study examines the efficacy of SVM as a fundamental algorithmic framework for anomaly detection in retail electronics online. The goal of the research is to better understand user interfaces and security protocols one-commerce platforms by spotting anomalies within user behavioral events. Objectives: To evaluate the effectiveness of SVM in identifying anomalies in user activity patterns, a rigorous experimental design comprising feature extraction, preprocessing, and model evaluation is used in the technique. Methods: The study establishes the foundation for analysis and model creation by utilizing data from the REES46 platform, which records a broad range of user interactions over an extended period of time. Utilizing this extensive dataset, the study explores the subtle aspects of user behavior and offers insights into SVM algorithm-based anomaly detection methods. The methodology’s rigorous data preprocessing and feature extraction ensured the dataset’s integrity, contributing to the model’s ef-fectiveness. Metrics including precision, recall, and F1-score were used to train and assess the SVM model after a thorough normalization of the dataset using Stan-dard Scaler. With an F1 score, a precision, and a recall. The model’s accuracy was further confirmed by a low Mean Squared Error (MSE), Prediction scatter plots and other visualizations. Results: The findings highlight the considerable potential of SVM-based anomaly detection systems to improve user experiences and strengthen security protocols in online retail settings. higher scores for the classification metrics. The model performed well, obtaining an F1 score of 0.93, a precision of 99per, and a recall of 1.00, d

Background: The main purpose of this thesis is in electronic commerce, reliable anomaly detection systems are essential for maintaining security and improving user experiences, especially in the electronics industry. With the goal of filling in the gaps in the current anomaly detection methods, this study examines the efficacy of SVM as a fundamental algorithmic framework for anomaly detection in retail electronics online. The goal of the research is to better understand user interfaces and security protocols one-commerce platforms by spotting anomalies within user behavioral events. Objectives: To evaluate the effectiveness of SVM in identifying anomalies in user activity patterns, a rigorous experimental design comprising feature extraction, preprocessing, and model evaluation is used in the technique. Methods: The study establishes the foundation for analysis and model creation by utilizing data from the REES46 platform, which records a broad range of user interactions over an extended period of time. Utilizing this extensive dataset, the study explores the subtle aspects of user behavior and offers insights into SVM algorithm-based anomaly detection methods. The methodology’s rigorous data preprocessing and feature extraction ensured the dataset’s integrity, contributing to the model’s ef-fectiveness. Metrics including precision, recall, and F1-score were used to train and assess the SVM model after a thorough normalization of the dataset using Stan-dard Scaler. With an F1 score, a precision, and a recall. The model’s accuracy was further confirmed by a low Mean Squared Error (MSE), Prediction scatter plots and other visualizations. Results: The findings highlight the considerable potential of SVM-based anomaly detection systems to improve user experiences and strengthen security protocols in online retail settings. higher scores for the classification metrics. The model performed well, obtaining an F1 score of 0.93, a precision of 99per, and a recall of 1.00, d