Pedestrian-vehicle collisions are a critical subset of anomaly detection in overhead traffic surveillance, where automated identification aids timely safety interventions. An object detector locates ...
Abstract: The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security ...
Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Professional, modular analysis of historical stock prices using multiple anomaly detection methods (Z-Score, Isolation Forest, DBSCAN, Prophet, Rolling Quantile). Includes multi-ticker comparison, ...
Security Operation Centers (SOC) continuously monitor system logs to detect suspicious activities such as brute-force attacks, unauthorized access, or privilege abuse. However, the large volume and ...
ABSTRACT: The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based ...
ABSTRACT: The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based ...
5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
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