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Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission

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Frontiers of information Technology & Electronic Engineering 2024年第6期25卷 791-808页

作者： Ziyang XING Xiaoqiang DI Hui QI Jing CHEN Jinhui CAO Jinyao LIU Xusheng LI Zichu ZHANG Yuchen ZHU Lei CHEN Kai HUANG Xinghan HUO Jilin Key Laboratory of Network and Information Security Changchun 130022China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Information Center Changchun University of Science and TechnologyChangchun 130022China

information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing ***,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth *** content delivery networks are ill-equipped to handle such bursts due to their pre-deployed *** this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing *** strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic *** formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic *** address these challenges,we leverage federated reinforcement learning ***,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing *** software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery *** paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

关键词： information-centric satellite network Burst traffic Content delivery Federated reinforcement learning Mixed-integer linear programming model Bloom filter Dynamic network

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DDoS Attack Detection and Mitigation Based on Asynchronous Personalized Federated Learning

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 808-827页

作者： Zhu, Hai-Ting Wei, Ming-Gang Liu, Feng-Ning He, Gao-Feng Zhang, Lu School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China School of Computer Science Nanjing Audit University Nanjing211815 China

network traffic classification serves as a fundamental component of both network management and cybersecurity, playing a pivotal role in ensuring the stability and security of modern communication infrastructures. Among various network security threats, the accurate identification of Distributed Denial of Service (DDoS) attacks remains particularly crucial, as such attacks have the potential to severely compromise system availability and overall network reliability. DDoS attacks can cause significant disruptions to network services by overwhelming targeted systems with an excessive volume of malicious traffic, leading to service outages, rapid depletion of computational and network resources, and substantial financial losses. These consequences critically impair the Quality of Service (QoS), affecting both end users and service providers by degrading network performance and reducing operational efficiency. While centralized machine learning models have demonstrated notable success in detecting DDoS attacks by leveraging vast amounts of labeled data for training, their deployment in real-world network environments is hindered by several intrinsic challenges. One of the most critical issues is the highly imbalanced distribution of network traffic data, where attack patterns and normal traffic exhibit significant variations across different clients. Furthermore, the centralized collection and transmission of large-scale network data not only introduce severe privacy and security concerns but also pose substantial bandwidth and storage constraints, making traditional data aggregation approaches impractical. Additionally, heterogeneous network devices—ranging from high-performance servers to resource-constrained edge devices—operate under diverse computational capabilities and network conditions, while the constantly evolving nature of network traffic dynamics further complicates the feasibility of real-time DDoS detection and mitigation strategies. To effectively addres

关键词： Quality of service

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Evolutionary Classifier Chain for Multi-Dimensional Classification 39

Evolutionary Classifier Chain for Multi-Dimensional Classifi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yu-Yang Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing 210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou 730050 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)157735897X

In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order optimization and feature selection (ECCO) is proposed. Specifically, this approach designs a meta-heuristic algorithm to optimize the chain order of multiple classifiers. Simultaneously, the approach selects dimension-specific feature combinations that are more conducive to class prediction of each dimension. These strategies enhance the class prediction capability of the constructed MDC model. Comparative experiments on 14 real datasets validate that ECCO outperforms 7 state-of-the-art MDC approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Changhu, Cheng Peng, Yang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9780738110837

Few-shot learning aims to build a classification model by training a small amount of labeled sample data, which can be well adapted to new domains. The key point of few-shot learning is that a small amount of sample data cannot reflect the true data distribution. Training on a small amount of sample data will lead to over-fitting of the deep neural network model. The differences between different categories are ignored when using similarity measures for classification. This paper proposes a novel few-shot learning method based on similarity-difference relation network, which uses shallow wide residual network to extract the features of the training dataset and fuses them into a category prototype. Meanwhile, SDRN pays attention to the characterization of similarities and differences between positive and negative samples. This paper verifies the effectiveness of the similarity-difference relational network on the Mini-ImageNet and Tiered-ImageNet datasets. The experimental results show that the similarity-difference two-way relational network further improves image classification accuracy in the few-shot learning task. © 2021 IEEE.

关键词： Deep neural networks

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SCREADER: Prompting Large Language Models to Interpret scRNA-seq Data 24

SCREADER: Prompting Large Language Models to Interpret scRNA...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Li, Cong Long, Qingqing Zhou, Yuanchun Xiao, Meng Institute of Zoology Chinese Academy of Sciences State Key Laboratory of Stem Cell and Reproductive Biology China University of Chinese Academy of Sciences China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798331530631

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in the field of life sciences, where vast collections of single-cell omics data from multiple species provide a foundation for training foundational models. However, the challenge lies in the disparity of data scales across different species, hindering the development of a comprehensive model for interpreting genetic data across diverse organisms. In this study, we propose an innovative hybrid approach that integrates the general knowledge capabilities of LLMs with domain-specific representation models for single-cell omics data interpretation. We begin by focusing on genes as the fundamental unit of representation. Gene representations are initialized using functional descriptions, leveraging the strengths of mature language models such as LLaMA-2. By inputting single-cell gene-level expression data with prompts, we effectively model cellular representations based on the differential expression levels of genes across various species and cell types. In the experiments, we constructed developmental cells from humans and mice, specifically targeting cells that are challenging to annotate. We evaluated our methodology through basic tasks such as cell annotation and visualization analysis. The results demonstrate the efficacy of our approach compared to other methods using LLMs, highlighting significant improvements in accuracy and interoperability. Our hybrid approach enhances the representation of single-cell data and offers a robust framework for future research in cross-species genetic analysis. 1 © 2024 IEEE.

关键词： Data Mining Large Language Model Prompt Learning Single Cell RNA Data

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Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization 39

Partial Label Causal Representation Learning for Instance-De...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yizhi Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing 210096 China School of Information and Physical Sciences The University of Newcastle Callaghan 2308 NSW Australia Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)157735897X

Partial label learning (PLL) addresses situations where each training example is associated with a set of candidate labels, among which only one corresponds to the true class label. As the candidate labels often come from crowdsourced workers, their generation is inherently dependent on the features of the instance. Existing PLL methods primarily aim to resolve these ambiguous labels to enhance classification accuracy, overlooking the opportunity to use this feature dependency for causal representation learning. This focus on accuracy can make PLL systems vulnerable to stylistic variations and shifts in domain. In this paper, we explore the learning of causal representations within an instance-dependent PLL framework, introducing a new approach that uncovers identifiable latent representations. By separating content from style in the identified causal representation, we introduce CausalPLL+, an algorithm for instance-dependent PLL based on causal representation. Our algorithm performs exceptionally well in terms of both classification accuracy and generalization robustness. Qualitative and quantitative experiments on instance-dependent PLL benchmarks and domain generalization tasks verify the effectiveness of our approach. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Transformer Helps Gnns Express Better Via Distillation of Long-Range Dependencies

SSRN

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SSRN 2024年

作者： Li, Jiaxing Zhang, Xiaotong Zhang, Ke Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Jiangsu Province Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Graph neural networks (GNNs) are powerful architectures for graph representation learning. However, GNNs based on message-passing cannot make fully use of the valuable long-range dependencies in graph-structured data. Recently, researchers have designed Transformer-based graph learning models to expand the receptive field to capture long-range dependencies between pairwise nodes. Although the preliminary performance of the graph Transformer is encouraging and even surpasses GNNs, the quadratic complexity of the self-attention leads to much longer inference time than GNNs, which limits the deployment in practical applications. To address this issue, we propose a knowledge distillation framework to transfer long-range dependencies captured by graph Transformer to GNNs. Our framework extracts knowledge from a pre-trained graph Transformer teacher and transfer it to a GNN-based student. To better transfer the long-range dependencies, we further design a long-range dependencies transfer module, in which the intermediate graph representations of the graph Transformer and GNNs are extracted as distributions. Then we minimize the distance between the two distributions, thereby achieving the long-range dependencies transfer more accurately. The proposed method can be extended to any graph Transformer and GNNs. We evaluate our proposed method on a large number of graph classification benchmark datasets with different GNN-based baselines, and the experimental results demonstrate that our method can obtain the performance close to graph Transformer with faster inference speed and smaller memory usage. © 2024, The Authors. All rights reserved.

关键词： Distillation

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Test-Time Training with Invariant Graph Learning for Out-of-Distribution Generalization

SSRN

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SSRN 2024年

作者： Pi, Li Li, Jiaxing Song, Lei Yang, Guanyu Chen, Yang Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Jiangsu Province Nanjing China

Graph Neural networks (GNNs) frequently do not perform optimally on out-of-distribution (OOD) data. Previous research on graph OOD generalization suggests extracting invariant subgraphs, which provide crucial categorical information even under test distributions. However, this strategy is not optimal due to two issues. Firstly, there is a significant distribution shift between the test and training sets, which may result in incorrect invariant subgraphs being extracted during the test phase. Secondly, the extraction of subgraphs can lead to the loss of important structural information. To address these issues, we propose a novel two-stage training paradigm named \textbf{I}nvariant \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{T}raining (\textbf{IGT3}), which uses the training paradigm called Test-Time Training (TTT). We also propose invariant graph recognition to learn key parts of the graph without destroying the original graph structure. In addition, we introduce a multi-level graph contrastive learning method as an auxiliary self-supervised task. To prevent TTT failure, we use the feature constraint to enhance the robustness of the training process. We conduct experiments on seven common graph classification OOD benchmark datasets. The results demonstrate that IGT3 outperforms state-of-the-art (SOTA) methods by an average of 4.2\%, confirming the effectiveness of each module. The paper code is available at https://***/r/IGT3-3281/ © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Encrypted Malicious Traffic Detection Based on Albert and Deep Learning

SSRN

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SSRN 2024年

作者： Zang, Xiaodong Wang, Tongliang Zhang, Xinchang Gong, Jian Gao, Peng Zhang, Guowei School of Cyber Science and Engineering Qufu Normal University Qufu China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

The focus on privacy protection has brought much-encrypted network traffic. However, attackers always abuse traffic encryption to conceal malicious behaviors. Although researchers have proposed several enlightening detection methods, they must enhance the generalization ability or improve detection performance. Our inspiration is that the packet header fields, as do the underlying grammatical rules for constructing sentences, have a strict order. We consider the original packet as text and devise a robust approach with natural language processing and a deep learning model to improve the generalization ability and detection performance. We capture the critical keywords as characteristic representations of the traffic and design an adaptive domain generalization algorithm with a new loss function. It is robust against various datasets and can augment the minority malicious samples to balance the data. Simultaneously, we design an efficient feature selection algorithm, which obtains an optimal feature subset and reduces feature dimensions by 75.3%. We conducted extensive experiments with two open-source datasets and the actual Internet backbone traffic(CERNET). Experimental results demonstrate our proposal's efficiency and robustness, outperforming state-of-the-art detection schemes. Specifically, the accuracy rate of our work can improve by at most 22.8% and achieve the average detection latency of 0.73s in the backbone. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Multi-Instance Partial-Label Learning with Margin Adjustment

arXiv

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arXiv 2025年

作者： Tang, Wei Yang, Yin-Fang Wang, Zhaofei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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