Anomaly Detection；Dynamic Network；Web Services; IoT; Machine Learning; Network Security; Dataset;
This dataset contains synthetic smart meter data with simulated cyber attacks, designed to support research in anomaly detection, cybersecurity, and energy consumption analysis. The dataset is based on 159 users from the Smart Meters in London dataset, selected for their regular consumption patterns. This larger dataset can be found in
https://www.kaggle.com/datasets/jeanmidev/smart-meters-in-london,
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Containerization has emerged as a revolutionary technology in the software development and deployment industry. Containers offer a portable and lightweight solution that allows for packaging applications and their dependencies systematically and efficiently. In addition, containers offer faster deployment and near-native performance with isolation and security drawbacks compared to Virtual Machines. To address the security issues, scanning tools that scan containers for preexisting vulnerabilities have been developed, but they suffer from false positives.
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The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.
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