The detailed investigation paper on network invasion awareness
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.
World Journal of Advanced Research and Reviews, 2024, 21(03), 2448–2453
Publication history:
Received on 29 January 2024; revised on 27 March 2024; accepted on 29 March 2024
Abstract:
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.
Keywords:
Network Intrusion Detection; Deep Learning; CNN; LSTM
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0