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Fei Chen

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19 papers

2 author rows

EAAI Journal 2026 Journal Article

Component-wise independent adaptive learning and local optimization for long-term forecasting

Fei Chen
Ke Cheng
Shitong Wang
Yuanquan Wang

Long-term time series forecasting (LTSF) faces significant challenges on small datasets due to overfitting, inconsistent training progress across model layers, and lack of interpretability. To address these issues, we propose Component-wise Independent Adaptive Learning and Local Optimization (CIALLO), a novel parallel forecasting framework that decomposes time series into reversible components — trend, waveform, and amplitude — allowing for independent modeling and targeted training. Highlights benefits of modularization: flexible sub-model selection, independent pre-training, clearer convergence analysis, and higher training efficiency. Emphasizes structural interpretability via decomposition and component-wise optimization rather than post-hoc attention. Experiments on benchmark Electricity Transformer Temperature (ETT) datasets and Traffic truncated dataset demonstrate that CIALLO achieves comparable or competitive performance with state-of-the-art models, particularly on long-term horizons and under limited data conditions. Ablations on designed modules show that lightweight sub-models and independent component training improve optimization stability, while guided gradient has minimal impact on final performance. Decomposition ablations indicate that detrending dominates while amplitude is beneficial only when scaling is reliable. Ablation on the amplitude adjustment reveals a stable U-shaped behavior, with moderate values giving the most balanced correction. Component-wise contributions and early-stopping behavior are analyzed, revealing inconsistent training progress across components. Training-time analysis also shows faster overall convergence compared to baselines. The error contribution across samples, representative prediction cases and the parameters of the designed sub-models are visualized and analyzed. Finally, the overall results are summarized and their implications for future model design and interpretability are discussed.

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YNIMG Journal 2026 Journal Article

Cortical reorganization and listening effort in sound localization with left- vs. right-sided deafness

Yimeng Liu
Xuexin Tian
Aifang Fu
Zengzhi Guo
Lei Wang
Jie Tang
Fei Chen
Hongzheng Zhang

Sound source localization is a fundamental ability for living organisms and essential for human daily functioning. Sound localization heavily relies on binaural auditory input. Unilateral auditory impairment not only disrupts sound localization but also alters cortical activation patterns. However, current understanding of these adaptive cortical changes remains limited, with a lack of multidimensional and systematic research. Here, we employed multimodal neuroimaging-functional near-infrared spectroscopy (fNIRS) and pupillometry-to investigate the physiological mechanisms of sound localization from complementary perspectives. We recruited 33 normal-hearing participants, 12 with right single-sided deafness (RSSD), and 8 with left single-sided deafness (LSSD). All participants completed a sound localization task with seven azimuths (±90°, ±60°, ±30°, 0°) in the frontal hemifield. Our results revealed symmetric activation patterns in higher-order auditory cortices within the auditory dorsal stream and distinct cognitive processing for left vs. right hemispheres during sound localization. Following unilateral auditory deprivation, we observed distinct patterns of cortical reorganization that differed by deprivation side. Specifically, RSSD was associated with maintained spatial gradient encoding but required prefrontal compensation, whereas LSSD showed sacrificed spatial resolution for hemispheric processing efficiency. These findings offer novel insights into brain adaptation to unilateral auditory deprivation and provide a foundation for future research and clinical applications.

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JBHI Journal 2026 Journal Article

WGB-GLFI: A Novel Graph-Based Global-Local Feature Interaction Framework for Automated Seizure Detection

Xiang Li
Mingxing Zhu
Chuqi Yang
Ke Zhang
Xin Wang
Sunday Timothy Aboyeji
Fei Chen
Chen Yao

Epilepsy detection faces significant challenges due to unpredictable seizures, ranging from brief awareness lapses to severe convulsions, posing risks to patients' safety and quality of life. In recent years, deep learning has become a mainstream approach in this field, leveraging advanced computational resources and EEG datasets. However, a key challenge remains: existing methods often lack unified spatial modeling and struggle to effectively handle local detailed features, thereby limiting their accuracy and robustness. To address these issues, we propose the Weighted Graph Building Global-Local Feature Interaction (WGB-GLFI) framework, which integrates spatial connectivity and dynamic patterns through a Weighted Graph Building (WGB) module and a Global-Local Feature Interaction (GLFI) module. This approach excels by comprehensively capturing the dynamic spatial relationships during epileptic seizures and achieving seamless global-local feature integration, significantly enhancing seizure detection performance. Its effectiveness has been validated across multiple datasets, including CHB-MIT, Siena Scalp, and private datasets, demonstrating robust and reliable results. Evaluated on these datasets, our model achieves accuracy rates of 99. 28%, 99. 21%, and 99. 30%, respectively. The reliability and robustness of our framework provide epilepsy patients with faster and more reliable seizure detection, which helps to intervene in a timely manner and improve the quality of life of patients.

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JBHI Journal 2025 Journal Article

Ensembled-SAMs for Enhanced Small Coronary Artery Segmentation in CCTA Images

Fei Chen
Junyao Ge
Yang Zheng
Kaitai Guo
Feng Cao
Jimin Liang

Accurate coronary artery segmentation is crucial for quantitative analysis of coronary arteries in noninvasive coronary computed tomography angiography (CCTA) images. However, current segmentation algorithms often have unsatisfactory recall due to the small size and complex morphology of coronary arteries, particularly in the distal segments. To address this issue, we introduce a new fully automated method named Ensembled-SAMs, which harnesses the strengths of the Segment Anything Model (SAM) and the no-new-U-Net (nnU-Net). First, noisy bounding box prompts are automatically generated by a vesselness algorithm that highlights the tubular structures in the CCTA images. These noisy prompts are then used to fine-tune the SAM and its two variants separately. The SAM variants introduce a classification head in their mask decoder to alleviate the false positives. In addition, an nnU-Net segmentation network is trained from scratch. Finally, the outputs of the SAMs and the nnU-Net are strategically aggregated to obtain the final segmentation result. Experiments on both a self-built dataset and the public Automated Segmentation of Coronary Arteries (ASOCA) challenge dataset demonstrate that the proposed Ensembled-SAMs outperforms the state-of-the-arts, achieving precise segmentation of coronary arteries, with particular enhancement in delineating small coronary artery segments.

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IROS Conference 2025 Conference Paper

Open-World Task Planning for Humanoid Bimanual Dexterous Manipulation via Vision-Language Models

Zixin Tang
Zhihao Li
Junjia Liu
Zhuo Li
Fei Chen

Open-world task planning, characterized by handling unstructured and dynamic environments, has been increasingly explored to integrate with long-horizon robotic manipulation tasks. However, existing evaluations of the capabilities of these planners primarily focus on single-arm systems in structured scenarios with limited skill primitives, which is insufficient for numerous bimanual dexterous manipulation scenarios prevalent in the real world. To this end, we introduce OBiMan-Bench, a large-scale benchmark designed to rigorously evaluate open-world planning capabilities in bimanual dexterous manipulation, including task-scenario grounding, workspace constraint handling, and long-horizon cooperative reasoning. In addition, we propose OBiMan-Planner, a vision-language model-based zero-shot planning framework tailored for bimanual dexterous manipulation. OBiMan-Planner comprises two key components, the scenario grounding module for grounding open-world task instructions with specific scenarios and the task planning module for generating sequential stages. Extensive experiments on OBiMan-Bench demonstrate the effectiveness of our method in addressing complex bimanual dexterous manipulation tasks in open-world scenarios. The code, benchmark, and supplementary material are released at https://github.com/Zixin-Tang/OBiMan.

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JBHI Journal 2024 Journal Article

Age-Related Changes in Blood Volume Pulse Wave at Fingers and Ears

Wan-Hua Lin
Dingchang Zheng
Guanglin Li
Fei Chen

Objective: The decline in vascular elasticity with aging can be manifested in the shape of pulse wave. The study investigated the pulse wave features that are sensitive to age and the pattern of these features change with increasing age were examined. Methods: Five features were proposed and extracted from the photoplethysmography (PPG)-based pulse wave or its first derivative wave. The correlation between these PPG features and ages was studied in 100 healthy subjects with a wide range of ages (20-71 years). Piecewise regression coefficients were calculated to examine the rates of change of the PPG features with age at different age stages. Results: The proposed PPG features obtained from the finger showed a strong and significant correlation with age (with r = 0. 76 – 0. 77, p r = 0. 66 – 0. 75). The correlation remained significant even after correcting for other clinical variables. The rate of change of the PPG feature values was found to be significantly faster in subjects aged ≥40 years compared to those aged Conclusions: The proposed PPG features showed a high correlation with chronological age in healthy subjects and exhibited a similar age-related change trend as PWV. Significance: With the convenience of PPG measures, the proposed age-related features have the potential to be used as biomarkers for vascular aging and estimating the risk of cardiovascular disease.

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EAAI Journal 2024 Journal Article

Revisiting the loss functions in sequential recommendation

Fangyu Li
Hong Peng
Shenbao Yu
Feng Zeng
Fei Chen
Fan Yang

There has been a growing interest in benchmarking sequential recommendation models and reproducing/improving existing models. However, the exploration of loss functions in this context has been relatively limited. To address this gap, we investigate the application of diverse loss functions in sequential recommendation, focusing on Cross-Entropy (CE), Binary Cross-Entropy (BCE), and Bayesian Personalized Ranking (BPR) losses. Our objective is to enhance model performance through refining these loss functions. Existing loss functions in sequential recommendation are discussed and analyzed, summarizing their pros and cons. Following this, we identify the two crucial characteristics that an efficient loss function in sequential recommendation should embody, namely Time-Awareness and Efficiency. Subsequently, we introduce a straightforward yet impactful approach that seamlessly integrates time-awareness and efficiency into the computation of the loss function. Further, by utilizing meta-loss learning, we train time-aware parameters and integrate them into the loss function, enabling the model to adaptively capture significant user interactions with items. Our methodology is extensively validated through comprehensive experiments involving various classical and advanced sequential recommendation models on multiple publicly available datasets. Impressively, our approach not only leads to substantial performance improvements but also enables baseline models to surpass state-of-the-art models.

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EAAI Journal 2023 Journal Article

A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles

Peng Mei
Hamid Reza Karimi
Hehui Xie
Fei Chen
Cong Huang
Shichun Yang

Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the ”dimension disaster” once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.

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NeurIPS Conference 2023 Conference Paper

Complexity Matters: Rethinking the Latent Space for Generative Modeling

Tianyang Hu
Fei Chen
Haonan Wang
Jiawei Li
Wenjia Wang
Jiacheng Sun
Zhenguo Li

In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e. g. , Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the process of identifying it remain unclear. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity. Our investigation starts with the classic generative adversarial networks (GANs). Inspired by the GAN training objective, we propose a novel "distance" between the latent and data distributions, whose minimization coincides with that of the generator complexity. The minimizer of this distance is characterized as the optimal data-dependent latent that most effectively capitalizes on the generator's capacity. Then, we consider parameterizing such a latent distribution by an encoder network and propose a two-stage training strategy called Decoupled Autoencoder (DAE), where the encoder is only updated in the first stage with an auxiliary decoder and then frozen in the second stage while the actual decoder is being trained. DAE can improve the latent distribution and as a result, improve the generative performance. Our theoretical analyses are corroborated by comprehensive experiments on various models such as VQGAN and Diffusion Transformer, where our modifications yield significant improvements in sample quality with decreased model complexity.

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EAAI Journal 2023 Journal Article

Hierarchical federated learning with local model embedding

Yunlong He
Dandan Yan
Fei Chen

Federated learning can synergize the local model training with private data samples from geo-distributed users. Nevertheless, the unification process of a comprehensive global model through periodical parameter sharing can be time-consuming at a high cost. On the one hand, the data samples are collected from users with diverse preferences, and the data distribution can be non-independent and identically distributed (non-IID). On the other hand, the consequent model trained locally needs to communicate with a remote parameter server periodically for parameter synchronization, which leads to overwhelming communication and synchronization overhead given heterogeneous device capacities and network conditions of end-users. Generally, a hierarchical system design with a clustered group is ideal for accommodating diversity. Actually, it is still challenging to maintain the relationship of the local model training without knowing the data samples in advance for privacy concerns. Therefore, we present hierarchical federated model embedding to formulate the relationship between local data distributions. Initially, the local models are embedded through the global shared dataset to obtain feature latent representation vectors. The cloud server groups the clients according to the vectors, making clients with similar data distribution train collaboratively in a same group. Then, these vectors are used to train the predictor on the cloud server, which is utilized for efficient group assignment when new clients join the system. Compared with the baseline, the accuracy of the group model can be improved by 1. 22 % ∼ 5. 63 % and that of the global model can be improved by 3. 97 % ∼ 14. 25 % on different datasets.

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JBHI Journal 2022 Journal Article

Single-Channel Selection for EEG-Based Emotion Recognition Using Brain Rhythm Sequencing

Jia Wen Li
Shovan Barma
Peng Un Mak
Fei Chen
Cheng Li
Ming Tao Li
Mang I Vai
Sio Hang Pun

Recently, electroencephalography (EEG) signals have shown great potential for emotion recognition. Nevertheless, multichannel EEG recordings lead to redundant data, computational burden, and hardware complexity. Hence, efficient channel selection, especially single-channel selection, is vital. For this purpose, a technique termed brain rhythm sequencing (BRS) that interprets EEG based on a dominant brain rhythm having the maximum instantaneous power at each 0. 2 s timestamp has been proposed. Then, dynamic time warping (DTW) is used for rhythm sequence classification through the similarity measure. After evaluating the rhythm sequences for the emotion recognition task, the representative channel that produces impressive accuracy can be found, which realizes single-channel selection accordingly. In addition, the appropriate time segment for emotion recognition is estimated during the assessments. The results from the music emotion recognition (MER) experiment and three emotional datasets (SEED, DEAP, and MAHNOB) indicate that the classification accuracies achieve 70–82% by single-channel data with a 10 s time length. Such performances are remarkable when considering minimum data sources as the primary concerns. Furthermore, the individual characteristics in emotion recognition are investigated based on the channels and times found. Therefore, this study provides a novel method to solve single-channel selection for emotion recognition.

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AAAI Conference 2021 Conference Paper

MetaAugment: Sample-Aware Data Augmentation Policy Learning

Fengwei Zhou
Jiawei Li
Chuanlong Xie
Fei Chen
Lanqing Hong
Rui Sun
Zhenguo Li

Automated data augmentation has shown superior performance in image recognition. Existing works search for datasetlevel augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples naively could greatly increase the computing cost. In this paper, we learn a sample-aware data augmentation policy efficiently by formulating it as a sample reweighting problem. Specifically, an augmentation policy network takes a transformation and the corresponding augmented image as inputs, and outputs a weight to adjust the augmented image loss computed by a task network. At training stage, the task network minimizes the weighted losses of augmented training images, while the policy network minimizes the loss of the task network on a validation set via meta-learning. We theoretically prove the convergence of the training procedure and further derive the exact convergence rate. Superior performance is achieved on widely-used benchmarks including CIFAR-10/100, Omniglot, and ImageNet.

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NeurIPS Conference 2021 Conference Paper

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

Mi Luo
Fei Chen
Dapeng Hu
Yifan Zhang
Jian Liang
Jiashi Feng

A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server. Other works also share public datasets or synthesized samples to supplement the training of under-represented classes or introduce a certain level of personalization. Though effective, they lack a deep understanding of how the data heterogeneity affects each layer of a deep classification model. In this paper, we bridge this gap by performing an experimental analysis of the representations learned by different layers. Our observations are surprising: (1) there exists a greater bias in the classifier than other layers, and (2) the classification performance can be significantly improved by post-calibrating the classifier after federated training. Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model. Experimental results demonstrate that CCVR achieves state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10. We hope that our simple yet effective method can shed some light on the future research of federated learning with non-IID data.

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IROS Conference 2020 Conference Paper

LAVAPilot: Lightweight UAV Trajectory Planner with Situational Awareness for Embedded Autonomy to Track and Locate Radio-tags

Hoa Van Nguyen
Fei Chen
Joshua Chesser
Hamid Rezatofighi
Damith Ranasinghe

Tracking and locating radio-tagged wildlife is a labor-intensive and time-consuming task necessary in wildlife conservation. In this article, we focus on the problem of achieving embedded autonomy for a resource-limited aerial robot for the task capable of avoiding undesirable disturbances to wildlife. We employ a lightweight sensor system capable of simultaneous (noisy) measurements of radio signal strength information from multiple tags for estimating object locations. We formulate a new lightweight task-based trajectory planning method-LAVAPilot-with a greedy evaluation strategy and a void functional formulation to achieve situational awareness to maintain a safe distance from objects of interest. Conceptually, we embed our intuition of moving closer to reduce the uncertainty of measurements into LAVAPilot instead of employing a computationally intensive information gain based planning strategy. We employ LAVAPilot and the sensor to build a lightweight aerial robot platform with fully embedded autonomy for jointly tracking and planning to track and locate multiple VHF radio collar tags used by conservation biologists. Using extensive Monte Carlo simulation-based experiments, implementations on a single board compute module, and field experiments using an aerial robot platform with multiple VHF radio collar tags, we evaluate our joint planning and tracking algorithms. Further, we compare our method with other information-based planning methods with and without situational awareness to demonstrate the effectiveness of our robot executing LAVAPilot. Our experiments demonstrate that LAVAPilot significantly reduces (by 98. 5%) the computational cost of planning to enable real-time planning decisions whilst achieving similar localization accuracy of objects compared to information gain based planning methods, albeit taking a slightly longer time to complete a mission. To support research in the field, and conservation biology, we also open source the complete project. In particular, to the best of our knowledge, this is the first demonstration of a fully autonomous aerial robot system where trajectory planning and tracking to survey and locate multiple radio-tagged objects are achieved onboard.

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IJCAI Conference 2020 Conference Paper

SEBF: A Single-Chain based Extension Model of Blockchain for Fintech

Yimu Ji
Weiheng Gu
Fei Chen
Xiaoying Xiao
Jing Sun
Shangdong Liu
Jing He
Yunyao Li

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted or-acle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verifi-cation.

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YNICL Journal 2018 Journal Article

Short- and long-range synergism disorders in lifelong premature ejaculation evaluated using the functional connectivity density and network property

Jiaming Lu
Xin Zhang
Huiting Wang
Zhao Qing
Peng Han
Ming Li
Jiadong Xia
Fei Chen

This study was aimed to investigate brain function connectivity in premature ejaculation (PE) patients using the functional connectivity density (FCD) and network property of resting-state functional magnetic resonance imaging. Twenty PE patients (mean age: 27. 95 ± 4. 52 years) and 15 normal controls (mean age: 27. 87 ± 3. 78 years) with no self-reported history of neurologic or psychiatric disease were enrolled in this study. International Index of Erectile Function-5 and Chinese Index of Sexual Function for Premature Ejaculation-5 questionnaires and self-reported intravaginal ejaculatory latency time (IELT) were obtained from each participant for symptom assessment. Two-sample t-tests (intergroup comparison) were applied in the short-range FCD (SFCD) analysis, long-range FCD (LFCD) analysis, region of interest–based analysis, and network topological organization analysis. Pearson correlation analysis was performed to correlate IELT with FCD or the network property. The patients with PE showed significantly decreased SFCD in the bilateral middle temporal gyrus, left orbitofrontal cortex, nucleus accumbens, fusiform, caudate, and thalamus (p < 0. 05, AlphaSim-corrected). Notably, all these aforementioned brain areas are located in the dopamine pathway. In contrast, increased LFCD was observed in the left insula, Heschl's gyrus, putamen, bilateral precuneus, supplementary motor area, middle cingulate cortex, and anterior cingulate cortex in PE patients (p < 0. 05, AlphaSim-corrected). In addition, the network topological analysis found reinforced network connectivity between several nodes. The degree of hub nodes increased in the patients with PE. IELT was positively correlated with SFCD and negatively correlated with LFCD or the degree of hub nodes (p < 0. 05, Pearson correlation). In summary, our results are important for understanding the brain network in PE patients. The present findings indicate that PE patients have a significant synergism disorder across the region of dopamine pathway, which implied neuronal pathological changes might be related with the change of dopamine. The FCD and network property can serve as new disease severity biomarkers and therapeutic targets in PE.

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NeurIPS Conference 2016 Conference Paper

Automated scalable segmentation of neurons from multispectral images

Uygar Sümbül
Douglas Roossien
Dawen Cai
Fei Chen
Nicholas Barry
John Cunningham
Edward Boyden
Liam Paninski

Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.

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TCS Journal 2014 Journal Article

Period distribution of generalized discrete Arnold cat map

Fei Chen
Kwok-wo Wong
Xiaofeng Liao
Tao Xiang

The generalized discrete Arnold cat map is adopted in various cryptographic and steganographic applications where chaos is employed. In this paper, we analyze the period distribution of this map. A systematic approach for addressing the general period distribution problem for any integer value of the modulus N is outlined, followed by a complete analysis for the case of prime N. The analysis is based on similar techniques studying linear feedback shift register (LFSR) sequences. Together with our previous results when N is a power of a prime [1, 2], the period distribution of the cat map is characterized nearly completely for any integer N. Our results are also useful for evaluating the security of the cryptographic and steganographic algorithms based on the cat map as well as computing all unstable periodic orbits of the chaotic Arnold cat map.

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TAAS Journal 2012 Journal Article

First step towards automatic correction of firewall policy faults

Fei Chen
Alex X. Liu
Jeehyun Hwang
Tao Xie

Firewalls are critical components of network security and have been widely deployed for protecting private networks. A firewall determines whether to accept or discard a packet that passes through it based on its policy. However, most real-life firewalls have been plagued with policy faults, which either allow malicious traffic or block legitimate traffic. Due to the complexity of firewall policies, manually locating the faults of a firewall policy and further correcting them are difficult. Automatically correcting the faults of a firewall policy is an important and challenging problem. In this article, we first propose a fault model for firewall policies including five types of faults. For each type of fault, we present an automatic correction technique. Second, we propose the first systematic approach that employs these five techniques to automatically correct all or part of the misclassified packets of a faulty firewall policy. Third, we conducted extensive experiments to evaluate the effectiveness of our approach. Experimental results show that our approach is effective to correct a faulty firewall policy with three of these types of faults.

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