Design of Enterprise Data Security System Based on Lightweight LSTM-CRF

DOI: https://doi.org/10.62517/jbdc.202501314

Author(s)

Lan Li, Jinlong Tan, Fabin Yang, Jundi Wang*

Affiliation(s)

School of Computer and Artificial Intelligence, Lanzhou Institute of Technology, Lanzhou, Gansu, China *Corresponding Author

Abstract

In enterprise digitalization, enterprise data security must tackle both internal operational risks and emerging external attacks like Fuzzers Malformed Packet Injection. As a classic network intrusion detection benchmark dataset, NSL-KDD’s preprocessing and lightweight model training are critical for enterprise scenario adaptation. However, small and medium-sized enterprises (SMEs) face a prominent contradiction: limited resources versus urgent security needs. Traditional rule engines rely on manual updates, leading to low unknown threat recognition rates and high false positives; existing deep models such as LSTM and BERT have massive parameters and high resource consumption, hard to deploy on SMEs’ CPUs; standard LSTM-CRF is also plagued by sample imbalance, resulting in weak generalization. To solve these issues, this study designs a three-layer collaborative system centered on Lightweight LSTM-CRF, supported by multimodal log preprocessing, self-evolving defense and blockchain audit algorithms, with Dynamic Trust Evaluation embedded. Experiments on the NSL-KDD dataset in a common CPU environment show the system achieves over 90% anomaly recognition accuracy, less than 100ms threat response time, and 55% lower deployment costs, accurately meeting SMEs’ needs.

Keywords

Lightweight LSTM-CRF; Enterprise Data Security; Dynamic Trust Evaluation; System Design

References

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