计算机工程与应用2025，Vol.61Issue(10)：299-307,9.DOI:10.3778/j.issn.1002-8331.2405-0065
结合Transformer的双向GRU入侵检测研究
Intrusion Detection Research Combining Transformer and Bidirectional GRU
摘要
Abstract
In network intrusion detection systems,previous systems often suffered from interference by noisy features during feature extraction,exhibited poor distinction at the boundaries of minority class samples when dealing with imbal-anced data,and the detection models tended to miss important temporal information.Such issues compromised the training effectiveness and diminished the detection performance of the models.To overcome these challenges,this paper introduces a hybrid model that amalgamates the pigeon-inspired optimization(PIO)algorithm and the borderline synthetic minority over-sampling technique(SMOTE)with a Transformer-bidirectional gated recurrent unit(BiGRU).The pigeon-inspired optimization algorithm is utilized for automatic feature selection,enhancing the capability of the model to process com-plex datasets,and mitigating the impact of noise features.The borderline SMOTE is applied to balance the data,with a specific emphasis on minority class samples,thereby enhancing their representation and quality within the balanced dataset.A deep learning model that integrates Transformer and BiGRU is developed for intrusion detection.This integra-tion exploits the Transformer's ability to capture global dependencies and the BiGRU's competence in temporal sequence modeling,enabling a more nuanced understanding of the bidirectional contextual relationships in sequence data.Experi-mental results derived from the NSL-KDD dataset indicate that the model demonstrates commendable detection perfor-mance,attaining an accuracy rate of 83.64%and an F1 score of 78.41%,which surpasses the benchmarks set by traditional machine learning models and other deep learning models used for comparison.关键词
鸽群优化算法/边界过采样/多头注意力/双向循环门控单元/入侵检测Key words
pigeon-inspired optimization algorithm/Borderline SMOTE/multi-head attention/bidirectional gated recur-rent unit/intrusion detection分类
信息技术与安全科学引用本文复制引用
李道全,刘旭寅,刘嘉宇,陈思慧..结合Transformer的双向GRU入侵检测研究[J].计算机工程与应用,2025,61(10):299-307,9.基金项目
山东省自然科学基金(ZR2023MF052). （ZR2023MF052）
