信息安全研究2026，Vol.12Issue(5)：402-409,8.DOI:10.12379/j.issn.2096-1057.2026.05.02
基于特征选择和时间残差注意力的在线社交网络入侵检测方法
OSN Intrusion Detection Method Based on Residual Time-attention with Feature Selection
摘要
Abstract
Online social network(OSN),as core platform for information exchange,currently face serious intrusion threats.However,existing OSN intrusion detection techniques exhibit poor detection performance when dealing with issues such as high dimensionality,diverse datasets with different types of structures,significant semantic differences,and mismatched dynamic features.Therefore,an intrusion detection method based on residual time-attention with feature selection(RTA-S)is proposed.The method utilizes the pretrained language model BERT for data preprocessing and designs a classifier based on residual time-attention.The model effectively captures contextual features in a wide range of text information through a bidirectional LSTM and attention mechanism.Meanwhile,an adaptive feature selection method based on deep reinforcement learning is proposed,which utilizes adaptive learning to obtain the optimal feature set.The experiment shows that the proposed method achieves accuracies of 98.53％,98.68％,and 98.33％in detecting multiple threat patterns on datasets from Facebook,Google+,and Twitter,respectively.The average accuracy on the three datasets exceeds other mainstream methods.关键词
深度强化学习/残差注意力/特征选择/在线社交网络/入侵检测Key words
deep reinforcement learning/residual attention/feature selection/online social network/intrusion detection分类
信息技术与安全科学引用本文复制引用
张一鸣,汤艳君,明泰龙..基于特征选择和时间残差注意力的在线社交网络入侵检测方法[J].信息安全研究,2026,12(5):402-409,8.基金项目
公安部科技计划项目(2024LL09-05) （2024LL09-05）
辽宁省教育厅高校基本科研项目(LJ212410175002) （LJ212410175002）
