Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660407
Sheng Zhuang, Lin Cao, Zongmin Zhao, Dongfeng Wang
The method based on the fusion of radar and video in this paper is oriented to detecting surrounding objects while driving. This is usually a method of improving robustness and accuracy by using several senses, which makes sensor fusion a key part of the perception system. We propose a new fusion method called CT-EPNP, which uses radar and camera data for fast detection. Adding a central fusion algorithm on the basis of EPNP, and use the truncated cone method to compensate the radar information on the associated image when mapping. CT-EPNP returns to the object attributes depth, rotation, speed and other attributes. Based on this, simulation verification and related derivation of mathematical formulas are proved. We combined the improved algorithm with the RetinaNet model to ensure that the model is satisfied with the normal driving of the vehicle while gaining a certain increase in the detection rate. We have also made a certain improvement in ensuring repeated detection without using any additional time information.
{"title":"A Target Detection Method Based on the Fusion Algorithm of Radar and Camera","authors":"Sheng Zhuang, Lin Cao, Zongmin Zhao, Dongfeng Wang","doi":"10.1109/IC-NIDC54101.2021.9660407","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660407","url":null,"abstract":"The method based on the fusion of radar and video in this paper is oriented to detecting surrounding objects while driving. This is usually a method of improving robustness and accuracy by using several senses, which makes sensor fusion a key part of the perception system. We propose a new fusion method called CT-EPNP, which uses radar and camera data for fast detection. Adding a central fusion algorithm on the basis of EPNP, and use the truncated cone method to compensate the radar information on the associated image when mapping. CT-EPNP returns to the object attributes depth, rotation, speed and other attributes. Based on this, simulation verification and related derivation of mathematical formulas are proved. We combined the improved algorithm with the RetinaNet model to ensure that the model is satisfied with the normal driving of the vehicle while gaining a certain increase in the detection rate. We have also made a certain improvement in ensuring repeated detection without using any additional time information.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660529
Lingqiu Zeng, Yang Wang, Qingwen Han, Kun Zhou, L. Ye, Yang Long
Fatigue driving is one of the main reasons that cause sever accidents. It's necessary to detect fatigue state and warn drivers to avoid life-threatening accidents. There are many related technologies to detect fatigue, some of which based on physiological information or face features. However, biological indicators are difficult to analyze in real-time and the signal sensor is invasive while image-based approaches have relatively strong subjective. Hence, in this paper, a method combined physiological information and face features is employed. We use near-infrared spectroscopy (fNIRS) on behalf of physical states and eye and mouth condition representing face states. Firstly, Multi-Task Convolutional Neural Network (MTCNN) was used to extract image features and then a lightly classifier was designed to recognize the state of face states. Finally, we use Long Short-Term Memory (LSTM) model to fuse these characters and predict fatigue. Experiment results show that the method proposed have a high accuracy about 95.8% and fast speed about 6.12ms to detect fatigue.
{"title":"Driving Fatigue Detection Combining Face Features with Physiological Information","authors":"Lingqiu Zeng, Yang Wang, Qingwen Han, Kun Zhou, L. Ye, Yang Long","doi":"10.1109/IC-NIDC54101.2021.9660529","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660529","url":null,"abstract":"Fatigue driving is one of the main reasons that cause sever accidents. It's necessary to detect fatigue state and warn drivers to avoid life-threatening accidents. There are many related technologies to detect fatigue, some of which based on physiological information or face features. However, biological indicators are difficult to analyze in real-time and the signal sensor is invasive while image-based approaches have relatively strong subjective. Hence, in this paper, a method combined physiological information and face features is employed. We use near-infrared spectroscopy (fNIRS) on behalf of physical states and eye and mouth condition representing face states. Firstly, Multi-Task Convolutional Neural Network (MTCNN) was used to extract image features and then a lightly classifier was designed to recognize the state of face states. Finally, we use Long Short-Term Memory (LSTM) model to fuse these characters and predict fatigue. Experiment results show that the method proposed have a high accuracy about 95.8% and fast speed about 6.12ms to detect fatigue.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660403
Fan Zhang, Xueming Li, Qiang Fu
Recovery depth from a lenslet light field image can facilitate lots of applications including super-resolution and 3D reconstruction. However, current works mainly focus on the central sub-aperture image but pay little attention to full-viewpoint light field images. In this paper, we propose a deep learning-based method to recovery full-viewpoint depth by estimating the disparity map from the given light field image. We employ the ResNet to extract multi-dimensional features from the given light field image which is encoded as a 3D epipolar plane image, establish dense connections to enable the neural network to calculate the cost volume from extracted features, and use an AutoEncoder to convert the cost volume to a disparity map of the given light field image. We show several experimental results and two comparisons with the related works to demonstrate the effect and performance of our method.
{"title":"Full-Viewpoint Depth Recovery of Light Field Image via Deep Neural Network","authors":"Fan Zhang, Xueming Li, Qiang Fu","doi":"10.1109/IC-NIDC54101.2021.9660403","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660403","url":null,"abstract":"Recovery depth from a lenslet light field image can facilitate lots of applications including super-resolution and 3D reconstruction. However, current works mainly focus on the central sub-aperture image but pay little attention to full-viewpoint light field images. In this paper, we propose a deep learning-based method to recovery full-viewpoint depth by estimating the disparity map from the given light field image. We employ the ResNet to extract multi-dimensional features from the given light field image which is encoded as a 3D epipolar plane image, establish dense connections to enable the neural network to calculate the cost volume from extracted features, and use an AutoEncoder to convert the cost volume to a disparity map of the given light field image. We show several experimental results and two comparisons with the related works to demonstrate the effect and performance of our method.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128699995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660444
Juchuan Guo, Yichen Liu, Zhenyu Wu
In a wide range of classification tasks, training data will produce class-imbalance due to collection difficulties in some classes, which leads to prediction biases on minority classes. For the class-imbalanced problem, existing researches are usually based on the assumption that the training dataset and the test dataset are from similar distributions. In reality, both of the datasets often come from domains with different distributions, which challenges generalization performances of models. In this paper, a decoupling deep domain adaptation method is proposed to overcome these problems. Based on the adversarial domain adaptation model, the method uses a two-stage training strategy which decouples representation learning and classifier adjustment. The results of experiments under scenarios of bearing fault diagnosis and digit images classification with class-imbalance and domain discrepancy show that the effect of the combination of domain adaptation method and specific decoupling strategy is better than that of one-stage training only using resampling or cost-sensitive methods in the domain adaptation model.
{"title":"Decoupling Deep Domain Adaptation Method for Class-imbalanced Learning with Domain Discrepancy","authors":"Juchuan Guo, Yichen Liu, Zhenyu Wu","doi":"10.1109/IC-NIDC54101.2021.9660444","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660444","url":null,"abstract":"In a wide range of classification tasks, training data will produce class-imbalance due to collection difficulties in some classes, which leads to prediction biases on minority classes. For the class-imbalanced problem, existing researches are usually based on the assumption that the training dataset and the test dataset are from similar distributions. In reality, both of the datasets often come from domains with different distributions, which challenges generalization performances of models. In this paper, a decoupling deep domain adaptation method is proposed to overcome these problems. Based on the adversarial domain adaptation model, the method uses a two-stage training strategy which decouples representation learning and classifier adjustment. The results of experiments under scenarios of bearing fault diagnosis and digit images classification with class-imbalance and domain discrepancy show that the effect of the combination of domain adaptation method and specific decoupling strategy is better than that of one-stage training only using resampling or cost-sensitive methods in the domain adaptation model.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the academia has done a lot of research on DNS abnormal behavior, whether from the perspective of traffic or irregular domain name recognition, the mechanism behind DNS is ignored in the pre-processing of DNS logs and other data. In addition, most studies focus on traffic anomaly detection and unconventional domain name recognition, and lack of systematic research on the combination of the two, so the proposed algorithm has no practical application. This paper proposes a clustering method based on DNS client IP address traffic characteristics, which divides DNS logs into five access modes. Then, a DNS log preprocessing algorithm is designed to preprocess the logs that may exist in zombie hosts. Finally, a two-layer GRU network detection algorithm based on domain name text features is proposed. Experimental results show that this method can effectively identify zombie hosts in DNS logs.
{"title":"Zombie Hosts Identification Based on DNS Log","authors":"Renjie Wang, Yangsen Zhang, Ruixue Duan, Zhuofan Huang","doi":"10.1109/IC-NIDC54101.2021.9660578","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660578","url":null,"abstract":"Although the academia has done a lot of research on DNS abnormal behavior, whether from the perspective of traffic or irregular domain name recognition, the mechanism behind DNS is ignored in the pre-processing of DNS logs and other data. In addition, most studies focus on traffic anomaly detection and unconventional domain name recognition, and lack of systematic research on the combination of the two, so the proposed algorithm has no practical application. This paper proposes a clustering method based on DNS client IP address traffic characteristics, which divides DNS logs into five access modes. Then, a DNS log preprocessing algorithm is designed to preprocess the logs that may exist in zombie hosts. Finally, a two-layer GRU network detection algorithm based on domain name text features is proposed. Experimental results show that this method can effectively identify zombie hosts in DNS logs.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660590
M. Hamedani, Sang-Wook Kim
The link prediction task has attracted significant attention in the literature. Link-based similarity measures (in short, similarity measures) are the conventional methods for this task, while recently graph embedding methods (in short, embedding methods) are widely employed as well. In this paper, we extensively investigate the effectiveness of embedding methods and similarity measures (i.e., both non-recursive and recursive ones) in link prediction. Our experimental results with three real-world datasets demonstrate that 1) recursive similarity measures are not beneficial in this task than non-recursive one,2) increasing the number of dimensions in vectors may not help improve the accuracy of embedding methods, and 3) in comparison with embedding methods, Adamic/Adar, a non-recursive similarity measure, can be a useful method for link prediction since it shows promising results while being parameter-free.
{"title":"Embedding Methods or Link-based Similarity Measures, Which is Better for Link Prediction?","authors":"M. Hamedani, Sang-Wook Kim","doi":"10.1109/IC-NIDC54101.2021.9660590","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660590","url":null,"abstract":"The link prediction task has attracted significant attention in the literature. Link-based similarity measures (in short, similarity measures) are the conventional methods for this task, while recently graph embedding methods (in short, embedding methods) are widely employed as well. In this paper, we extensively investigate the effectiveness of embedding methods and similarity measures (i.e., both non-recursive and recursive ones) in link prediction. Our experimental results with three real-world datasets demonstrate that 1) recursive similarity measures are not beneficial in this task than non-recursive one,2) increasing the number of dimensions in vectors may not help improve the accuracy of embedding methods, and 3) in comparison with embedding methods, Adamic/Adar, a non-recursive similarity measure, can be a useful method for link prediction since it shows promising results while being parameter-free.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116917350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660465
Yaping Wang, Huiqin Shao, Zhen Li, Yan Zhu, Zhe Liu
Sequence to sequence (Seq2Seq) model together with pointer network (Ptr-Net) has recently show promising results in slot filling task, in the situation where only sentence-level annotations are available, while the model's prediction contains repetition of slot values. In this paper, we add a coverage mechanism to alleviate issues of repeating prediction in slot filling task. We use a coverage vector to record attention history, and then add to the computation of attention, which can force model to consider more about un-predicted slot values. Experiments show that the proposed model significantly improves slot value prediction F1 with 8.5% relative improvement compare to the baseline models on benchmark DSTC2 (Dialog State Tracking Challenge 2) datasets.
{"title":"Combined Coverage, Attention and Pointer Networks for Improving Slot Filling in Spoken Language Understanding","authors":"Yaping Wang, Huiqin Shao, Zhen Li, Yan Zhu, Zhe Liu","doi":"10.1109/IC-NIDC54101.2021.9660465","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660465","url":null,"abstract":"Sequence to sequence (Seq2Seq) model together with pointer network (Ptr-Net) has recently show promising results in slot filling task, in the situation where only sentence-level annotations are available, while the model's prediction contains repetition of slot values. In this paper, we add a coverage mechanism to alleviate issues of repeating prediction in slot filling task. We use a coverage vector to record attention history, and then add to the computation of attention, which can force model to consider more about un-predicted slot values. Experiments show that the proposed model significantly improves slot value prediction F1 with 8.5% relative improvement compare to the baseline models on benchmark DSTC2 (Dialog State Tracking Challenge 2) datasets.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660447
Wei Liu, Hanwen Xu, Cheng Fang, Lei Yang, Weidong Jiao
Aiming at the problem of adjusting the penalty term coefficient of feature enhancement in high-resolution synthetic aperture radar (SAR) imaging, a marginal estimation Bayes (MEB) algorithm is proposed, so that the prior features of the target can be fitted properly to improve the accuracy of image feature extraction. Firstly, the alternating direction method of multipliers (ADMM) convex optimization framework is modeled based on the echoed data, and least absolute shrinkage and selection operator (Lasso) model and sparse group Lasso (SG-Lasso) model are introduced, then the maximum marginal likelihood distribution of the regularization parameters is derived. Moreover, the Moreau Yoshida unadjusted Langevin algorithm (MYULA) is used to realize target posteriori sampling solution. Because the posterior distribution is difficult to solve, the gradient projection method is introduced to estimate the regularization parameters. Finally, auto-learning parameters are used to optimize the imaging. The proposed algorithm can not only estimate the parameters of a single regularization term, but also estimate the parameters of multiple regularization terms. Aiming at non-differentiable part in the prior, MYULA is adopted to calculate the subgradient of the non-differentiable posterior distribution. Therefore, the proposed algorithm is capable of auto-leaning parameters even regularization function is non-differentiable. In the experimental part, compared with the optimal value of manual debugging, the error between the proposed method and the optimal value is within 15%, and the effectiveness of the algorithm are verified by phase transition diagram (PTD).
针对高分辨率合成孔径雷达(SAR)成像中特征增强惩罚项系数的调整问题，提出了一种边缘估计贝叶斯(MEB)算法，使目标的先验特征得到适当拟合，提高图像特征提取的精度。首先，基于回波数据建立了交替方向乘子法(ADMM)凸优化框架模型，引入了最小绝对收缩和选择算子(Lasso)模型和稀疏群Lasso (SG-Lasso)模型，推导了正则化参数的最大边际似然分布;此外，采用Moreau - Yoshida unadjusted Langevin算法(MYULA)实现目标后验采样解。由于后验分布难以求解，引入梯度投影法估计正则化参数。最后，利用自学习参数对图像进行优化。该算法不仅可以估计单个正则化项的参数，还可以估计多个正则化项的参数。针对先验中不可微的部分，采用MYULA计算不可微后验分布的次梯度。因此，即使正则化函数不可微，该算法也能自动学习参数。在实验部分，与人工调试的最优值相比，所提方法与最优值的误差在15%以内，并通过相变图(PTD)验证了算法的有效性。
{"title":"Auto-Learning of Parameters for High Resolution Sparse Group Lasso SAR Imagery","authors":"Wei Liu, Hanwen Xu, Cheng Fang, Lei Yang, Weidong Jiao","doi":"10.1109/IC-NIDC54101.2021.9660447","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660447","url":null,"abstract":"Aiming at the problem of adjusting the penalty term coefficient of feature enhancement in high-resolution synthetic aperture radar (SAR) imaging, a marginal estimation Bayes (MEB) algorithm is proposed, so that the prior features of the target can be fitted properly to improve the accuracy of image feature extraction. Firstly, the alternating direction method of multipliers (ADMM) convex optimization framework is modeled based on the echoed data, and least absolute shrinkage and selection operator (Lasso) model and sparse group Lasso (SG-Lasso) model are introduced, then the maximum marginal likelihood distribution of the regularization parameters is derived. Moreover, the Moreau Yoshida unadjusted Langevin algorithm (MYULA) is used to realize target posteriori sampling solution. Because the posterior distribution is difficult to solve, the gradient projection method is introduced to estimate the regularization parameters. Finally, auto-learning parameters are used to optimize the imaging. The proposed algorithm can not only estimate the parameters of a single regularization term, but also estimate the parameters of multiple regularization terms. Aiming at non-differentiable part in the prior, MYULA is adopted to calculate the subgradient of the non-differentiable posterior distribution. Therefore, the proposed algorithm is capable of auto-leaning parameters even regularization function is non-differentiable. In the experimental part, compared with the optimal value of manual debugging, the error between the proposed method and the optimal value is within 15%, and the effectiveness of the algorithm are verified by phase transition diagram (PTD).","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660508
Conghui Ma, Bin Zhang, Lutao Yan, Haiyuan Li
This paper presents a data-driven optimal approach based on differential dynamic programming (DDP) for two-person differential game of nonlinear affine systems. Using test data, the Hamilton-Jacobi-Isaacs (HJI) equation is expanded into a set of high-order differential equations. Basis functions is adopted to approximate the unknown system function and value function. Based on the approximation, a data-driven optimal approach is proposed to obtain the unknown coefficients of the basis functions. A numerical example is proposed to demonstrate the effectiveness of this method.
{"title":"Data-driven Differential Games for Affine Nonlinear Systems","authors":"Conghui Ma, Bin Zhang, Lutao Yan, Haiyuan Li","doi":"10.1109/IC-NIDC54101.2021.9660508","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660508","url":null,"abstract":"This paper presents a data-driven optimal approach based on differential dynamic programming (DDP) for two-person differential game of nonlinear affine systems. Using test data, the Hamilton-Jacobi-Isaacs (HJI) equation is expanded into a set of high-order differential equations. Basis functions is adopted to approximate the unknown system function and value function. Based on the approximation, a data-driven optimal approach is proposed to obtain the unknown coefficients of the basis functions. A numerical example is proposed to demonstrate the effectiveness of this method.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660402
Guochen Shi, Gang He
Network intrusion detection system (NIDS) is the essential component of cyber security infrastructure to ensure the security of communication and information systems. In this paper, a collaborative multi-agent reinforcement learning, Major-Minor-RL, is proposed to make the detection more efficient. The model consists of one major agent and several minor agents. The role of major agent is to predict whether the traffic is normal or abnormal, while minor agents are auxiliary to the major agent and help it to correct errors. If the action of major agent is different fro m the behavior of most minor agents, the final action will be determined by minor agents, while in most cases, the final action is equal to the major one. In this paper, the model has been trained on NSL-KDD dataset and the results are boosted. After comparing with the existing models, we observed much better classification performance in Major-Minor-RL intrusion detection system.
{"title":"Collaborative Multi-agent Reinforcement Learning for Intrusion Detection","authors":"Guochen Shi, Gang He","doi":"10.1109/IC-NIDC54101.2021.9660402","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660402","url":null,"abstract":"Network intrusion detection system (NIDS) is the essential component of cyber security infrastructure to ensure the security of communication and information systems. In this paper, a collaborative multi-agent reinforcement learning, Major-Minor-RL, is proposed to make the detection more efficient. The model consists of one major agent and several minor agents. The role of major agent is to predict whether the traffic is normal or abnormal, while minor agents are auxiliary to the major agent and help it to correct errors. If the action of major agent is different fro m the behavior of most minor agents, the final action will be determined by minor agents, while in most cases, the final action is equal to the major one. In this paper, the model has been trained on NSL-KDD dataset and the results are boosted. After comparing with the existing models, we observed much better classification performance in Major-Minor-RL intrusion detection system.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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