A Synergistic Hybrid Model for Proactive Intrusion Detection in Cyber-Physical Networks

DOI

10.2991/978-94-6463-858-5_271How to use a DOI?

Keywords

IDS; M.L; SVM+LSTM

Abstract

The purpose of an intrusion detection system is to prevent damaging attacks. Additionally, the tactics and technologies used by attackers are always changing. In the last work, we proposed employing random forest and support vector machines (SVM) in military defense environments (KDD dataset).Numerous experiments were conducted on the KDD dataset in order to lower FN rates and increase efficiency, regardless of whether SVM and RF have demonstrated good accuracy and precision failed to decrease FN rate and many deep learning modals has been studied. The hybrid SVM+LSTM modal demonstrated successful decreasing FN rate, increased accuracy, recall, and precedence.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Raja Nandini
AU  - P. Bulah Pushpa  Rani
AU  - Syed Zahada
AU  - A. Murali
AU  - Syed Shahada
PY  - 2025
DA  - 2025/11/04
TI  - A Synergistic Hybrid Model for Proactive Intrusion Detection in Cyber-Physical Networks
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3252
EP  - 3262
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_271
DO  - 10.2991/978-94-6463-858-5_271
ID  - Nandini2025
ER  -