IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection

Fuente: arXiv

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Bibliographic Details
Main Authors:	Farabi, Ahsan, Shad, Muhaiminul Rashid, Khandaker, Israt
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
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author	Farabi, Ahsan Shad, Muhaiminul Rashid Khandaker, Israt
author_facet	Farabi, Ahsan Shad, Muhaiminul Rashid Khandaker, Israt
contents	Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), enabling effective hyperparameter tuning while maintaining computational efficiency. Our pipeline incorporates rigorous preprocessing, stratified data splitting, and dynamic class weighting to enhance the detection of rare classes. Experimental evaluation on NSL-KDD demonstrates that IntrusionX achieves 98% accuracy in binary classification and 87% in 5-class classification, with significant improvements in minority class recall (U2R: 71%, R2L: 93%). The novelty of IntrusionX lies in its reproducible, imbalance-aware design with metaheuristic optimization.
format	Preprint
id	arxiv_https___arxiv_org_abs_2510_00572
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection Farabi, Ahsan Shad, Muhaiminul Rashid Khandaker, Israt Cryptography and Security Machine Learning Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), enabling effective hyperparameter tuning while maintaining computational efficiency. Our pipeline incorporates rigorous preprocessing, stratified data splitting, and dynamic class weighting to enhance the detection of rare classes. Experimental evaluation on NSL-KDD demonstrates that IntrusionX achieves 98% accuracy in binary classification and 87% in 5-class classification, with significant improvements in minority class recall (U2R: 71%, R2L: 93%). The novelty of IntrusionX lies in its reproducible, imbalance-aware design with metaheuristic optimization.
title	IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
topic	Cryptography and Security Machine Learning
url	https://arxiv.org/abs/2510.00572