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What the DOI identifies

Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks

10.46243/jst.2025.v10.i03.pp01-19 · JournalArticle — an article in a journal · Digital · Visual · Language

Published 2025-03-13

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 3 · pp. 1–19

Principal agents

• Purandhar. N (author → Author)
• L Nisar Ahmed (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 2 links, 48 references. Source: Crossref (member 25296), registered 2025-08-26, last deposited 2026-09-07.

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System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "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."
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            "doi": "10.62643/ijerst.2022.v18.i04.pp73-86",
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        },
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            "key": "ref3",
            "unstructured": "Poovendran, A., Sitaraman, S. R., Bhavana, V. S. H. G., Kalyan, G., & Harikumar, N. (2024). Adaptive CNN-LSTM and neuro-fuzzy integration for edge AI and IoMT-enabled chronic kidney disease prediction. International Journal of Applied Science Engineering and Management, 18(3), 553-582"
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            "unstructured": "Gollavilli, V. S. B. H., Gattupalli, K., Nagarajan, H., Alagarsundaram, P., & Sitaraman, S. R. (2023). Innovative Cloud Computing Strategies for Automotive Supply Chain Data Security and Business Intelligence. International Journal of Information Technology and Computer Engineering, 11(4), 259-282"
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            "unstructured": "Kadiyala, B. (2019). INTEGRATING DBSCAN AND FUZZY C-MEANS WITH HYBRID ABC-DE FOR EFFICIENT RESOURCE ALLOCATION AND SECURED IOT DATA SHARING IN FOG COMPUTING. International Journal of HRM and Organizational Behavior, 7(4), 1-13"
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            "unstructured": "Alagarsundaram, P. (2020). Analyzing the covariance matrix approach for DDoS HTTP attack detection in cloud environments. International Journal of Information Technology and Computer Engineering, 8(1), 29-47"
        },
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            "key": "ref7",
            "doi": "10.1155/2024/9853493",
            "unstructured": "Poovendran, A. (2024). Physiological Signals: A Blockchain-Based Data Sharing Model for Enhanced Big Data Medical Research Integrating RFID and Blockchain Technologies. Journal of Current Science, 9(2), 9726-001X"
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            "doi": "10.36548/jucct.2025.1.001",
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        },
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            "doi": "10.46243/jst.2023.v8.i08.pp18-34",
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            "unstructured": "Hussein, L., Kalshetty, J. N., Harish, V. S. B., Alagarsundaram, P., & Soni, M. (2024, August). Levy distribution-based Dung Beetle Optimization with Support Vector Machine Purandhar. N, L Nisar Ahmed: Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks for Sentiment Analysis of Social Media. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-5). IEEE"
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        },
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            "key": "ref27",
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            "unstructured": "Tamilarasan, B., Gollavilli, V. S. B. H., Alagarsundaram, P., & Muthu, B. (2024). Agile Practices for Software Development for Numerical Computing. In Coding Dimensions and the Power of Finite Element, Volume, and Difference Methods (pp. 1-31). IGI Global"
        },
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            "unstructured": "Gudivaka, B. R. (2024). Leveraging PCA, LASSO, and ESSANN for advanced robotic process automation and IoT systems. International Journal of Engineering & Science Research, 14(3), 718-731"
        },
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            "key": "ref30",
            "doi": "10.71443/9788197282164-16",
            "unstructured": "Alagarsundaram, P., Sitaraman, S. R., Gattupalli, K., & Khan, F. (2024). Implementing transfer learning and domain adaptation in IoT analytics. In RADemics (Chapter 16)"
        },
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            "doi": "10.30574/wjaets.2021.2.1.0085",
            "unstructured": "Basava, R. G. (2021). AI-powered smart comrade robot for elderly healthcare with integrated emergency rescue system. World Journal of Advanced Engineering Technology and Sciences, 2(1), 122-131"
        },
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            "doi": "10.1155/2018/8079697",
            "unstructured": "Gudivaka, B. R. (2019). BIG DATA-DRIVEN SILICON CONTENT PREDICTION IN HOT METAL USING HADOOP IN BLAST FURNACE SMELTING. International Journal of Information Technology and Computer Engineering, 7(2), 32-49"
        },
        {
            "key": "ref34",
            "doi": "10.1016/j.iot.2024.101361",
            "unstructured": "Basani, D. K. R., Gudivaka, B. R., Gudivaka, R. L., & Gudivaka, R. K. (2024). Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network. Internet of Things, 101361"
        },
        {
            "key": "ref35",
            "doi": "10.1142/s0129156425401494",
            "unstructured": "Grandhi, S. H., Gudivaka, B. R., Gudivaka, R. L., Gudivaka, R. K., Basani, D. K. R., & Kamruzzaman, M. M. (2025). Detection and Diagnosis of ECH Signal Wearable System for Sportsperson using Improved Monkey-based Search Support Vector Machine. International Journal of High Speed Electronics and Systems, 2540149"
        },
        {
            "key": "ref36",
            "doi": "10.1109/icdsis61070.2024.10594271",
            "unstructured": "Gudivaka, B. R., Almusawi, M., Priyanka, M. S., Dhanda, M. R., & Thanjaivadivel, M. (2024, May). An Improved Variational Autoencoder Generative Adversarial Network with Convolutional Neural Network for Fraud Financial Transaction Detection. In 2024 Second International Conference on Data Science and Information System (ICDSIS) (pp. 1-4). IEEE"
        },
        {
            "key": "ref37",
            "doi": "10.1109/icdsns62112.2024.10690873",
            "unstructured": "Kumaresan, V., Gudivaka, B. R., Gudivaka, R. L., Al-Farouni, M., & Palanivel, R. (2024, July). Machine Learning Based Chi-Square Improved Binary Cuckoo Search Algorithm for Condition Monitoring System in IIoT. In 2024 International Conference on Data Science and Network Security (ICDSNS) (pp. 1-5). IEEE"
        },
        {
            "key": "ref38",
            "doi": "10.1109/iacis61494.2024.10721631",
            "unstructured": "Palanivel, R., Basani, D. K. R., Gudivaka, B. R., Fallah, M. H., & Hindumathy, N. (2024, August). Support Vector Machine with Tunicate Swarm Optimization Algorithm for Emotion Recognition in Human-Robot Interaction. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-4). IEEE"
        },
        {
            "key": "ref39",
            "doi": "10.4018/979-8-3693-3964-0.ch012",
            "unstructured": "Mohammed, B. H., Abbas, Y. K., Gudivaka, B. R., & Grandhi, S. H. (2024). Validation and Verification of Numerical Models. In Coding Dimensions and the Power of Finite Element, Volume, and Difference Methods (pp. 248-273). IGI Global"
        },
        {
            "key": "ref40",
            "unstructured": "Kadiyala, B. (2020). Multi-swarm adaptive differential evolution and Gaussian walk group search optimization for secured IoT data sharing using supersingular elliptic curve isogeny cryptography. International Journal of Modern Electronics and Communication Engineering, 8(3), 109–115"
        },
        {
            "key": "ref41",
            "unstructured": "Nippatla, R. P., Alavilli, S. K., Kadiyala, B., Boyapati, S., & Vasamsetty, C. (2023). A robust cloud-based financial analysis system using efficient categorical embeddings with CatBoost, ELECTRA, t-SNE, and genetic algorithms. International Journal of Engineering & Science Research, 13(3), 166–184. Purandhar. N, L Nisar Ahmed: Robotic Cloud Automation-Enabled Attack Detection and Command Verification Using Attention-Based RNNs, ConvLSTM, and Bayesian Networks"
        },
        {
            "key": "ref42",
            "unstructured": "Kadiyala, B., & Kaur, H. (2021). Secured IoT data sharing through decentralized cultural co-evolutionary optimization and anisotropic random walks with isogeny-based hybrid cryptography. Journal of Science & Technology, 6(6), 231–245"
        },
        {
            "key": "ref43",
            "unstructured": "Alavilli, S. K., Kadiyala, B., Nippatla, R. P., Boyapati, S., & Vasamsetty, C. (2023). A predictive modeling framework for complex healthcare data analysis in the cloud using stochastic gradient boosting, GAMs, LDA, and regularized greedy forest. International Journal of Multidisciplinary Educational Research, 12(6), 22"
        },
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            "doi": "10.1109/icercs63125.2024.10895115",
            "unstructured": "Kadiyala, B., Alavilli, S. K., Nippatla, R. P., Boyapati, S., Vasamsetty, C., & Kaur, H. (2024, December). An IoMT-Based Surgical Monitoring System for Automated Image Synthesis and Segmentation Using Reinforcement Learning and DCGANs. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE"
        },
        {
            "key": "ref45",
            "doi": "10.1111/coin.12408",
            "unstructured": "Prabhakaran, V., & Kulandasamy, A. (2021). Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud. Computational Intelligence, 37(1), 344-370"
        },
        {
            "key": "ref46",
            "doi": "10.1515/itit-2020-0015",
            "unstructured": "Wressnegger, C. (2020). Efficient machine learning for attack detection. it-Information Technology, 62(5-6), 279-286"
        },
        {
            "key": "ref47",
            "doi": "10.1007/s41870-020-00583-w",
            "unstructured": "Singh, P., & Ranga, V. (2021). Attack and intrusion detection in cloud computing using an ensemble learning approach. International Journal of Information Technology, 13(2), 565-"
        },
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            "key": "ref48",
            "doi": "10.1088/1742-6596/1804/1/012007",
            "unstructured": "Ibrahim, O. J., & Bhaya, W. S. (2021, February). Intrusion detection system for cloud based software-defined networks. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012007). IOP Publishing"
        }
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