Class‐Unbalanced Sample Oriented Network Intrusion Detection Model Based on SSA and CNN ‐ LSTM in Big Data Network
Xiaogang Yuan, Jianxin Wan, Dezhi An · Concurrency and Computation Practice and Experience · 2025
ABSTRACT As network throughput increases and security threats escalate in big‐data environments, shallow machine‐learning models are inadequate for handling large‐scale network traffic. Aiming at the common class imbalance and high dimension problem of intrusion detection data sets, this paper proposes SSA‐CL (Sparrow Search Algorithm‐CNN‐LSTM) model based on SSA (Sparrow Search Algorithm) and CNN‐LSTM (Convolutional Neural Network, Long Short‐Term Memory) model. SSA‐CL model uses CNN and LSTM to build a CNN‐LSTM hybrid model that can automatically extract features and effectively process sequence data. We apply SMOTE to mitigate class imbalance and employ SSA to optimize hyperparameters, significantly improving detection accuracy and efficiency. The experimental results show that the proposed method has significant advantages in key indicators such as accuracy and recall, with excellent multi‐classification effect. SSA‐CL combines a compact architecture, fast training, and strong detection performance, indicating practical value for network‐security applications.