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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in April 2026 (Volume 30, Issue 1) Submit manuscript

The detailed investigation paper on network invasion awareness

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  • The detailed investigation paper on network invasion awareness

P Kamakshi Thai 1, Akshitha Allam 2, *, Pranay Sai Gabbula 2 and Yagnesh Kannam 2

1 Assistant Professor, Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Hyderabad, Telangana, India.
2 IV B. Tech students Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Hyderabad, Telangana, India.
 
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 2448-2453
Article DOI: 10.30574/wjarr.2024.21.3.0782
DOI url: https://doi.org/10.30574/wjarr.2024.21.3.0782
 
Received on 29 January 2024; revised on 27 March 2024; accepted on 29 March 2024
 
Intrusion detection is critical for network security, with deep learning-based algorithms gaining traction. This project introduces NIDS - CNNLSTM, a model designed for the Industrial Internet of Things wireless sensing environment. It effectively identifies network traffic data, ensuring Industrial Internet of Things equipment and operations remain secure. Trained on the NSL_KDD data set, it exhibits strong convergence and performance across three data set, accurately classifying traffic types. Comparative analysis underscores NIDS - CNNLSTM efficacy enhancements. Experimental results validate increased detection rates, classification accuracy, and reduced false alarms, making it suitable for Industrial Internet of Things varied network data scenarios.
 
Network Intrusion Detection; Deep Learning; CNN; LSTM
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-0782.pdf

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P Kamakshi Thai, Akshitha Allam, Pranay Sai Gabbula and Yagnesh Kannam. The detailed investigation paper on network invasion awareness. World Journal of Advanced Research and Reviews, 2024, 21(3), 2448-2453. Article DOI: https://doi.org/10.30574/wjarr.2024.21.3.0782

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