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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection shapley additive explanation hybrid explainable neural network (HEN)

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Improving Ultrasonic Testing by Using Machine Learning Framework Based on Model Interpretation Strategy

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Chinese Journal of Mechanical Engineering 2023年第5期36卷 174-186页

作者： Siqi Shi Shijie Jin Donghui Zhang Jingyu Liao Dongxin Fu Li Lin NDT&E Laboratory Dalian University of TechnologyDalian 116024China China Nuclear Industry 23 Construction Co. Ltd.Beijing 101300China

Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying *** signifcant features from UT data is essential for efcient defect ***,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in *** this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of ***,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature ***,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating shapley additive explanation(SHAP),flter method,embedded method and wrapper *** most efective ML model and the optimal feature subset with better correlation to the target defects are determined *** proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different *** ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by *** optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent *** experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT *** this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC *** imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,***

关键词： Ultrasonic testing Machine learning Feature extraction Feature selection shapley additive explanation

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基于分类集成的Lendingclub数据中客户违约预测

基于分类集成的Lendingclub数据中客户违约预测

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作者：刘鑫兰州大学

学位级别：硕士

近年来随着网络时代的迅速发展,互联网金融产品迅猛发展起来,大型的借贷平台也逐步兴起,Lending Club是其中一家发展迅速、运作较好的大型P2P（Peer to Peer）交易平台,由于P2P平台交易门槛低、流程简单、投资回报率高等优势,迅速吸引... 详细信息

近年来随着网络时代的迅速发展,互联网金融产品迅猛发展起来,大型的借贷平台也逐步兴起,Lending Club是其中一家发展迅速、运作较好的大型P2P（Peer to Peer）交易平台,由于P2P平台交易门槛低、流程简单、投资回报率高等优势,迅速吸引了大批量客户进入市场,从中也衍生出了一些违规贷款和欺诈事件,所以本文以LendingClub公司2018年10月1日到2018年12月31日的批贷数据进行建模分析,通过集成分类预测的方法进行风险评估,提高P2P平台关于违约率较高客户的识别能力,从而为该平台及公司提供科学决策依据。本文主要运用机器学习算法进行评估预测,通过Synthetic Minority Oversam-pling Technique（SMOTE）方法对不平衡数据进行过采样,对特征进行衍生及筛选,并在此基础上使用网格搜索算法对Logistic Regression（LR）,Random Forest（RF）,eXtreme Gradient Boosting（XGBoost）,Light Gradient Boosting Machine（LightGBM）等模型进行参数优化,并进行模型比较。最后对分类效果较好的模型进行融合,融合结果通过Logistic Regression,多数投票和概率平均三种方式进行输出,比较发现融合后的模型可以对客户进行更准确地分类。同时采用shapley additive exPlana-tion（SHAP）可解释模型从局部和全局的角度对最优模型进行可解释分析,并找出了数据中使得客户违约的重要特征。因此集成模型有更高的准确度对客户的违约行为进行识别。

关键词： Lending Club Random Forest XGBoost LightGBM Logistic Regression shapley additive explanation 网格搜索不平衡数据处理模型融合

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