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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-Frequency Transform for Domain Generalization

ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jintao Guo Na Wang Lei Qi Yinghuan Shi State Key Laboratory for Novel Software Technology Nanjing University National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structureirrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://***/lingeringlight/ALOFT/.

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DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization

DomainDrop: Suppressing Domain-Sensitive Channels for Domain...

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International Conference on computer Vision (ICCV)

作者： Jintao Guo Lei Qi Yinghuan Shi State Key Laboratory for Novel Software Technology Nanjing University National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance degradation due to domain shifts. In this paper, we introduce a novel approach for domain generalization from a novel perspective of enhancing the robustness of channels in feature maps to domain shifts. We observe that models trained on source domains contain a substantial number of channels that exhibit unstable activations across different domains, which are inclined to capture domain-specific features and behave abnormally when exposed to unseen target domains. To address the issue, we propose a DomainDrop framework to continuously enhance the channel robustness to domain shifts, where a domain discriminator is used to identify and drop unstable channels in feature maps of each network layer during forward propagation. We theoretically prove that our framework could effectively lower the generalization bound. Extensive experiments on several benchmarks indicate that our framework achieves state-of-the-art performance compared to other competing methods. Our code is available at https://***/lingeringlight/DomainDrop.

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Supervised Contrastive Learning with Prototype Distillation for Data Incremental Learning

SSRN

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SSRN 2024年

作者： Yang, Suorong Zhang, Tianyue Xu, Baile Shen, Furao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

The goal of Data Incremental Learning (DIL) is to enable learning from small-scale data batches from non-stationary data streams without clear task divisions. This approach often encounters the issue of catastrophic forgetting in deep neural networks. Meanwhile, practical applications may involve unbalanced data distributions across classes. To address the challenges of DIL, using prototypes as classifiers offers a balance between stability and flexibility. This facilitates the preservation of information from previous classes while evolving to accommodate new ones. Prototypes are particularly effective for classifying separable embeddings in the feature space, as they draw embeddings from the same class closer together while pushing those from different classes further apart. This aligns with the objective of contrastive learning. In this paper, we propose Supervised Contrastive learning with the Prototype Distillation (SCPD) method for the DIL problem. First, we employ supervised contrastive loss (SCL) for model training. To further mitigate the forgetting problem, we introduce the prototype distillation loss, which ensures that features remain close to their corresponding prototypes. Exemplar memory is utilized to store representative samples, enabling the model to maintain performance on previously learned tasks. Moreover, data augmentation techniques are applied to improve the models' robustness and continual learning capability. Experimental results demonstrate that the SCPD model outperforms prior state-of-the-art approaches across several benchmarks, including those with various imbalanced setups. Code will be made publicly available soon. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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DomainAdaptor: A Novel Approach to Test-time Adaptation

DomainAdaptor: A Novel Approach to Test-time Adaptation

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International Conference on computer Vision (ICCV)

作者： Jian Zhang Lei Qi Yinghuan Shi Yang Gao State Key Laboratory for Novel Software Technology Nanjing University National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture co-efficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://***/koncle/DomainAdaptor.

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Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models

Aster: Encoding Data Augmentation Relations into Seed Test S...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Haipeng Wang Zhengyuan Wei Qilin Zhou Bo Jiang W. K. Chan City University of Hong Kong Hong Kong China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Data-augmented deep learning models are widely used in real-world applications. However, many state-of the-art loss-based or coverage-based fuzzing techniques fail to produce fuzzing samples for them from many seeds. This paper proposes Aster, a novel technique to address this problem to enhance their fuzzing effectiveness for deep learning models trained with multi-sample data augmentation methods. Aster formulates a novel reachability-based strategy to encode the insights of every seed’s direct and indirect data augmentation relation instances into the replacement seed of that seed systematically. Our experiment shows that Aster is highly effective. On average, loss-based and coverage-based fuzzing techniques can generate 166% and 110% more fuzzing samples and reduce 31% and 22% unsuccessful seeds, respectively, after adopting the replacement seeds generated by Aster to replace their original seeds. Their improved models also become up to 55% and 40% on average more robust against FGSM and PGD attacks in the experiment.

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GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Gene Mutation

GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Ge...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lu, Runfeng Sun, Yuzhu Sun, Haofeng Fu, Xiao Luo, Bin Du, Xiaojiang Shi, Jin Aitsaadi, Nadjib Guizani, Mohsen Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Nanjing University School of Information Management Nanjing China Université Paris-Saclay David Uvsq Versailles France Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security. © 2024 IEEE.

关键词： Android Fuzzing Gene Mutation Intent

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Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization

arXiv

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arXiv 2023年

作者： Wang, Xiran Zhang, Jian Qi, Lei Shi, Yinghuan The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University China The School of Computer Science and Engineering Southeast University China

Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains. However, most current methods do not account for a common realistic scenario where the source and target domains have different classes. To overcome this deficiency, open set domain generalization (OSDG) then emerges as a more practical setting to recognize unseen classes in unseen domains. An intuitive approach is to use multiple one-vs-all classifiers to define decision boundaries for each class and reject the outliers as unknown. However, the significant class imbalance between positive and negative samples often causes the boundaries biased towards positive ones, resulting in misclassification for known samples in the unseen target domain. In this paper, we propose a novel meta-learning-based framework called dualistic MEta-learning with joint DomaIn-Class matching (MEDIC), which considers gradient matching towards inter-domain and inter-class splits simultaneously to find a generalizable boundary balanced for all tasks. Experimental results demonstrate that MEDIC not only outperforms previous methods in open set scenarios, but also maintains competitive close set generalization ability at the same time. Our code is available at https://***/zzwdx/MEDIC. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Semi-Supervised Image Captioning Considering Wasserstein Graph Matching

arXiv

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arXiv 2024年

作者： Yang, Yang The Nanjing University of Science and Technology Nanjing210094 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Ministry of Education State Key Lab. for Novel Software Technology Nanjing University China

Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we propose a novel Semi-Supervised Image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function. Experiments show that our method can outperform state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split, under different complex semi-supervised scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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