elektronika: Anomaly detection

Evidence: 100 • authority: 72

In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data. Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear reject…

Evidence: 46 • authority: 92

Dual contrastive learning-based reconstruction for anomaly detection in attributed networks.. Anomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains. Existing graph-based methods face two limitations: (i) embedding-based approaches obscure fine-grained structural and attribute patterns, and (ii) reconstruction-based methods neglect cross-view discrepancies during training, leaving cross-view discrepancies underutilized. To address these gaps, we propose Dual Contrastive Learning-based Reconstruction (DCOR), a dual autoencoder with a shared Graph neural network (GNN) encoder tha…

Evidence: 40 • authority: 80

Anomaly Detection of Financial Data using Machine Learning

Evidence: 40 • authority: 80

Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic

Evidence: 40 • authority: 92

Machine learning-based criminal behavior analysis for enhanced digital forensics.. In an increasingly digital world, uncovering criminal activity often relies on analyzing vast amounts of online behavior. Traditional methods in digital forensics struggle to keep up with the complexity and volume of data, particularly when trying to detect subtle deviations in online activity that could signal illegal intent. This research introduces an innovative approach that leverages machine learning to analyze internet activity-specifically browser artifacts-shedding new light on criminal behaviors that would otherwise remain hidden.Using advanced machine learning techniques such as Long Short-Term Memo…

Evidence: 35 • authority: 80

Machine Learning for Anomaly Detection

Evidence: 35 • authority: 80

Machine Learning for Anomaly Detection

Evidence: 35 • authority: 80

A Comparative Study of Anomaly Detection Algorithms for Cybersecurity Applications

Evidence: — • authority: 80

FraudGNN: Self-Supervised Graph Neural Anomaly Detection for Real-Time Financial Fraud with Adversarial Robustness and Explainable Reasoning

Evidence: — • authority: 80

Fusing copula-based anomaly detection with ensemble machine learning models for real-time and interpretable financial fraud detection

Evidence: — • authority: 88

Deep Learning for Anomaly Detection in E-commerce and Financial Transactions: Enhancing Fraud Prevention and Cybersecurity. E-commerce and financial transaction platforms are increasingly vulnerable to cyber threats and fraudulent activities due to the rapid digitization of global markets. Anomaly detection plays a vital role in identifying unusual behavior indicative of fraud, security breaches, or financial manipulation. Traditional methods such as rule-based systems and statistical models often fall short in adapting to evolving patterns of fraud. Deep learning, with its ability to extract complex features and learn non-linear relationships from massive datasets, offers a transformative…

Evidence: — • authority: 72

The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression was coined by Richard E. Bellman when considering problems in dynamic programming. The curse generally refers to issues that arise when the number of datapoints is small (in a suitably defined sense) relative to the intrinsic dimension of the data. Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that wh…