Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks
DOI:
https://doi.org/10.46243/jst.2025.v10.i03.pp01-19Keywords:
Automated robotics, security in the cloud,, artificial intelligence,, detecting intrusions, verifying commands,, recurrent neural networks, ConvLSTM,, networks based on Bayesian statistics, identifying anomalies,, information security.Abstract
Background Information: The emergence of robotic cloud automation has brought about fresh
cybersecurity hurdles, particularly in protecting communication and control systems from
cyber threats. It is crucial to guarantee strong intrusion detection and verify commands
effectively.
Objectives: Create an AI framework by combining deep learning and probabilistic models to
improve intrusion detection and command verification in cloud-based robotic systems.
Methods: The system combines Attention-Based RNN, ConvLSTM, and Bayesian Networks
to identify abnormalities and authenticate instructions, utilizing temporal and spatial data for
instant threat identification.
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Published
2025-03-13
How to Cite
Purandhar. N, & L Nisar Ahmed. (2025). Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks. Journal of Science & Technology (JST), 10(3), 1–19. https://doi.org/10.46243/jst.2025.v10.i03.pp01-19
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