摘要
由于无线网络的开放性,车联网容易受到网络攻击,如拒绝服务、模糊和欺骗攻击。为此,提出融合随机森林(random forest,RF)和梯度提升决策树(gradient boosting decision tree,GBDT)的堆叠(stacking)的入侵检测(RG-IDS)模型。首先,RG-IDS模型利用自适应合成采样(adaptive synthetic sampling,ADASYN)算法对不平衡类别的数据样本进行近邻采样,进而生成更多同类别的近似样本,形成相对平衡的样本数据。其次,RG-IDS模型利用GBDT评估特征的重要性,并选择具有重要特征的样本数据,建立轻量级分类器。最后,RG-IDS采用基于k折交叉验证的堆叠方法,降低过拟合的概率。将RF、GBDT和LightGBM分类器作为基学习器。采用数据集CICIDS 2017和NSL-KDD对RG-IDS模型进行实验测试。实验结果表明,RG-IDS模型可实现较高的F1值。
Due to openness of wireless communication,Internet of vehicles(IoV)is vulnerable to many cyber-attacks such as denial of service,spoofing and fuzzy attacks.Therefore,random forest(RF)and gradient boosting decision tree-based stacking intrusion detection(RF-IDS)model was proposed.Firstly,the adaptive synthetic sampling(ADASYN)algorithm was adopted to generate more similar samples through the nearest neighbor sampling strategy in order to balance the training samples of different categories,and form a relatively symmetric dataset.Secondly,GBDT was used to evaluate the importance of features and select sample data with important features to build a lightweight classifier.Finally,the k-fold cross-validation stacking method was used to reduce the probability of overfitting.RF,GBDT and LightGBM classifiers serve were used as base-learner.The RG-IDS model was tested by CICIDS 2017 and NSL-KDD datasets.The experimental results demonstrate that RG-IDS model can achieve a higher F1-score.
作者
徐会彬
方龙
张莎
XU Huibin;FANG Long;ZHANG Sha(School of Information Engineering,Huzhou University,Huzhou 313000,China)
出处
《电信科学》
北大核心
2024年第12期38-50,共13页
Telecommunications Science
基金
湖州市自然科学基金资助项目(No.2024YZ08)。
