Abstract: Anomaly detection in gas sensor data is crucial for food quality control, environmental monitoring, and industrial safety, yet traditional supervised approaches require labeled anomalous ...

ABSTRACT: The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality ...

This project implements an unsupervised anomaly detection system for time series data. The model is trained on normal sequences and learns to reconstruct them ...

This project implements a GAN-based approach for detecting anomalies in smart meter readings using the Large-scale Energy Anomaly Detection (LEAD) dataset. The model uses LSTM-based Generator and ...

A key highlight of IRP v4.3 is the introduction of Automatic Anomaly Detection (AAD) - a new capability designed to identify abnormal traffic behavior and mitigate threats directly at the network edge ...

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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?

Abstract: In recent years, hard disk drives (HDDs) and solid-state drives (SSDs) are both widely deployed in data centers. As a proactive warning technology, drive anomaly detection can detect ...