Cite this DOI

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*, *10*(3), 1–19. https://doi.org/10.46243/jst.2025.v10.i03.pp01-19

@article{purandharn2025robotic,
  author    = {Purandhar. N and L Nisar Ahmed},
  title     = {{Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {mar},
  volume    = {10},
  number    = {3},
  pages     = {1--19},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i03.pp01-19},
  url       = {https://doi.org/10.46243/jst.2025.v10.i03.pp01-19},
  abstract  = {Background Information: The emergence of robotic cloud automation has brought about freshcybersecurity hurdles, particularly in protecting communication and control systems fromcyber threats. It is crucial to guarantee strong intrusion detection and verify commandseffectively.Objectives: Create an AI framework by combining deep learning and probabilistic models toimprove intrusion detection and command verification in cloud-based robotic systems.Methods: The system combines Attention-Based RNN, ConvLSTM, and Bayesian Networksto identify abnormalities and authenticate instructions, utilizing temporal and spatial data forinstant threat identification.}
}

TY  - JOUR
TI  - Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks
AU  - Purandhar. N
AU  - L Nisar Ahmed
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/03/13/
VL  - 10
IS  - 3
SP  - 1
EP  - 19
PB  - Longman Publishers
SN  - 2456-5660
AB  - Background Information: The emergence of robotic cloud automation has brought about freshcybersecurity hurdles, particularly in protecting communication and control systems fromcyber threats. It is crucial to guarantee strong intrusion detection and verify commandseffectively.Objectives: Create an AI framework by combining deep learning and probabilistic models toimprove intrusion detection and command verification in cloud-based robotic systems.Methods: The system combines Attention-Based RNN, ConvLSTM, and Bayesian Networksto identify abnormalities and authenticate instructions, utilizing temporal and spatial data forinstant threat identification.
DO  - 10.46243/jst.2025.v10.i03.pp01-19
UR  - https://doi.org/10.46243/jst.2025.v10.i03.pp01-19
ER  -

{
    "type": "article-journal",
    "id": "10.46243/jst.2025.v10.i03.pp01-19",
    "DOI": "10.46243/jst.2025.v10.i03.pp01-19",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i03.pp01-19",
    "title": "Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Purandhar. N"
        },
        {
            "family": "L Nisar Ahmed"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2025,
                3,
                13
            ]
        ]
    },
    "volume": "10",
    "issue": "3",
    "page": "1-19",
    "publisher": "Longman Publishers",
    "abstract": "Background Information: The emergence of robotic cloud automation has brought about freshcybersecurity hurdles, particularly in protecting communication and control systems fromcyber threats. It is crucial to guarantee strong intrusion detection and verify commandseffectively.Objectives: Create an AI framework by combining deep learning and probabilistic models toimprove intrusion detection and command verification in cloud-based robotic systems.Methods: The system combines Attention-Based RNN, ConvLSTM, and Bayesian Networksto identify abnormalities and authenticate instructions, utilizing temporal and spatial data forinstant threat identification.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2025.v10.i03.pp01-19 gives all four in one JSON answer.