用混合模型+优化算法提升网络入侵检测准确率
IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
- CNN+LSTM捕捉流量局部与时间特征，结合松鼠搜索算法调参
- 在NSL-KDD数据集上二分类准确率达98%，少数类召回最高达93%
- 适合关注安全检测、不平衡数据处理的研究者
网络入侵检测系统面临攻击手段演进、高维流量数据和基准数据集（如NSL-KDD）中严重类别不平衡的挑战。为此，我们提出IntrusionX，一种融合卷积神经网络（CNN）进行局部特征提取和长短期记忆网络（LSTM）进行时序建模的混合深度学习框架，并采用松鼠搜索算法（SSA）进行超参数优化，兼顾性能与计算效率。管道包含严格预处理、分层数据划分和动态类别加权，以增强对稀有类别的检测能力。在NSL-KDD上的实验表明，IntrusionX在二分类任务中达到98%准确率，在五分类任务中达87%；少数类召回率显著提升：U2R为71%，R2L达93%。其创新性在于可复现、注重类别不平衡问题的架构设计与元启发式优化。
原文摘要 · Abstract (English)
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.
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