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adversarial training Against adversarial Attacks for Machine Learning-Based Intrusion Detection Systems

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Computers, Materials & Continua 2022年第11期73卷 3513-3527页

作者： Muhammad Shahzad Haroon Husnain Mansoor Ali Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyKarachi75600Pakistan

Intrusion detection system plays an important role in defending networks from security ***-to-end machine learning-based intrusion detection systems are being used to achieve high detection ***,in case of adversarial attacks,that cause misclassification by introducing imperceptible perturbation on input samples,performance of machine learning-based intrusion detection systems is greatly *** such problems have widely been discussed in image processing domain,very few studies have investigated network intrusion detection systems and proposed corresponding *** this paper,we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets and then using adversarial samples to train various machine learning algorithms(adversarial training)to test their defence *** is achieved by first creating adversarial sample based on Jacobian-based Saliency Map Attack(JSMA)and Fast Gradient Sign Attack(FGSM)using NSLKDD,UNSW-NB15 and CICIDS17 *** study then trains and tests JSMA and FGSM based adversarial examples in seen(where model has been trained on adversarial samples)and unseen(where model is unaware of adversarial packets)*** experiments includes multiple machine learning classifiers to evaluate their performance against adversarial *** performance parameters include Accuracy,F1-Score and Area under the receiver operating characteristic curve(AUC)Score.

关键词： Intrusion detection system adversarial attacks adversarial training adversarial machine learning

Identity-Preserving adversarial training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving adversarial training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

Boosting adversarial training with Learnable Distribution

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Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： adversarial training feature space learnable distribution distribution centroid

Multi-feedback Pairwise Ranking via adversarial training for Recommender

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Chinese Journal of Electronics 2020年第4期29卷 615-622页

作者： WANG Jianfang FU Zhiyuan NIU Mingxin ZHANG Pengbo ZHANG Qiuling College of Computer Science and Technology Henan Polytechnic University

Personalized recommendation systems predict potential demand by analyzing user ***, user feedback information is inferred from implicit feedback or explicit ***,feedback can be contaminated by user's mis-operations or malicious operations, and may thus lead to incorrect *** propose a novel Multi-feedback pairwise ranking method via adversarial training(AT-MPR) for recommender to enhance the robustness and overall performance in the event of rating *** MPR method extends Bayesian personalized ranking(BPR) to cover three types of feedback: positive, negative, and *** obtains user preferences in a probabilistic way through multiple feedbacks at different *** reduce the impact of feedback noise, we train an MPR objective function using minimax adversarial *** on two datasets show that the AT-MPR model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models in two evaluation metrics.

关键词： adversarial training Pairwise ranking Collaborative filtering Recommender system

GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-training Model and adversarial training

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Acta Geologica Sinica(English Edition) 2024年第5期98卷 1404-1417页

作者： MA Kai HU Xinxin TIAN Miao TAN Yongjian ZHENG Shuai TAO Liufeng QIU Qinjun Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichangHubei 443002China College of Computer and Information Technology China Three Gorges UniversityYichangHubei 443002China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Quantitative Resource Evaluation and Information Engineering Ministry of Natural ResourcesChina University of GeosciencesWuhan 430074China

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain *** generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition *** study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological *** model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer *** experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

关键词： geological named entity recognition geological report adversarial training confrontation training global pointer pre-training model

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A Study of Ensemble Feature Selection and adversarial training for Malicious User Detection

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China Communications 2023年第10期20卷 212-229页

作者： Linjie Zhang Xiaoyan Zhu Jianfeng Ma State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China

The continuously booming of information technology has shed light on developing a variety of communication networks,multimedia,social networks and Internet of Things ***,users inevitably suffer from the intrusion of malicious *** studies focus on static characteristics of malicious users,which is easy to be bypassed by camouflaged malicious *** this paper,we present a malicious user detection method based on ensemble feature selection and adversarial ***,the feature selection alleviates the dimension disaster problem and achieves more accurate classification ***,we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction ***,we use an effective ensemble learning which could avoid over-fitting and has good noise ***,we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the *** simplifies the parameters,obtaining more robust interaction features and pattern *** demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection,where the robustness issues are notable concerns.

关键词： malicious user detection feature selection ensemble learning adversarial training

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Improving the performance of SSVEP-BCI contaminated by physiological noise via adversarial training

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Medicine in Novel Technology and Devices 2023年第2期18卷 102-113页

作者： Dai Wang Aiping Liu Bo Xue Le Wu Xun Chen School of Information Science and Technology University of Science and Technology of ChinaHefei230027China Institute of Dataspace Hefei Comprehensive National Science CenterHefei230088China

Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer *** practical application scenarios,SSVEP-BCI systems are easily interfered by physiological noises such as electromyography(EMG)and electrooculography(EOG).The performance of traditional SSVEP recognition methods will degrade in such a noisy environment,which limits their real-world *** alleviate the interference of noise,existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological *** this study,we utilize adversarial training(AT)and neural networks(NNs)to construct a robust recognition method for SSVEP contaminated by physiological *** model training,we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome *** this way,we strengthen the robustness of the model to potential noises,such as physiological *** this study,we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and *** experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and *** also verified that introducing AT can slightly improve the performance of models under a cross-subject *** method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.

关键词： Steady-state visual evoked potentials Neural networks adversarial training Electroencephalography Physiological artifacts

Towards sustainable adversarial training with successive perturbation generation

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Frontiers of Information Technology & Electronic Engineering 2024年第4期25卷 527-539页

作者： Wei LIN Lichuan LIAO College of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China College of Economics and Management Xi’an University of TechnologyXi’an 710048China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou 350118China

adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network ***,most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution,which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean *** this paper,we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness,and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational *** this end,we propose a successive perturbation generation scheme for adversarial training(SPGAT),which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial *** proposed SPGAT is both efficient and effective;e.g.,the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training,and the performance boost is more than 7%and 3%in terms of adversarial accuracy and clean accuracy,*** extensively evaluate the SPGAT on various datasets,including small-scale MNIST,middle-scale CIFAR-10,and large-scale *** experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.

关键词： adversarial training adversarial attack Stochastic weight average Machine learning Model generalization

Mathematical Named Entity Recognition Based on adversarial training and Self-Attention

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Intelligent Automation & Soft Computing 2024年第4期39卷 649-664页

作者： Qiuyu Lai Wang Kang Lei Yang Chun Yang Delin Zhang Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Sichuan Normal University) Ministry of EducationChengdu610166China School of Mathematics and Science Sichuan Normal UniversityChengdu610166China Chengdu State-Owned Jinjiang Machine Factory Chengdu610100China

Mathematical named entity recognition(MNER)is one of the fundamental tasks in the analysis of mathematical *** solve the existing problems of the current neural network that has local instability,fuzzy entity boundary,and long-distance dependence between entities in Chinese mathematical entity recognition task,we propose a series of optimization processing methods and constructed an adversarial training and Bidirectional long shortterm memory-Selfattention Conditional random field(AT-BSAC)*** our model,the mathematical text was vectorized by the word embedding technique,and small perturbations were added to the word vector to generate adversarial samples,while local features were extracted by Bi-directional Long Short-Term Memory(BiLSTM).The self-attentive mechanism was incorporated to extract more dependent features between *** experimental results demonstrated that the AT-BSAC model achieved a precision(P)of 93.88%,a recall(R)of 93.84%,and an F1-score of 93.74%,respectively,which is 8.73%higher than the F1-score of the previous Bi-directional Long Short-Term Memory Conditional Random Field(BiLSTM-CRF)*** effectiveness of the proposed model in mathematical named entity recognition.

关键词： Named entity recognition BiLSTM-CRF adversarial training selfattentive mechanism mathematical texts