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Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

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IEEE Network 2025年

作者： Li, Xuzeng Zhang, Tao Wang, Jian Han, Zhen Liu, Jiqiang Kang, Jiawen Niyato, Dusit Jamalipour, Abbas Beijing Jiaotong University School of Cyberspace Science and Technology China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Guangdong University of Technology Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore University of Sydney SydneyNSW2006 Australia

Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. In order to improve the perception ability of DRL on the network, some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. However, as networks continue to evolve and become increasingly complex, existing GNN-DRL methods still face challenges in terms of scalability and robustness. Moreover, these methods are inadequate for addressing network security issues. From the perspective of security and robustness, this paper explores the solution of combining GNNs with DRL to build a resilient network. This article starts with a brief tutorial of GNNs and DRL, and introduces their existing applications in networks. Furthermore, we introduce the network security methods that can be strengthened by GNN-DRL approaches. Then, we designed a framework based on GNN-DRL to defend against attacks and enhance network resilience. Additionally, we conduct a case study using an encrypted traffic dataset collected from real IoT environments, and the results demonstrated the effectiveness and superiority of our framework. Finally, we highlight key open challenges and opportunities for enhancing network resilience with GNN-DRL. © 2025 IEEE. All rights reserved,

关键词： Deep reinforcement learning

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud 27

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TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Jiang, Yu Xu, Xiaolong Lin, Kunda Duan, Weihua Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665408783

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms. © 2021 IEEE.

关键词： Quality of service

Model-based Offline Policy Optimization with Adversarial Network

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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

HeRB: Heterophily-Resolved Structure Balancer for Graph Neural Networks

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arXiv 2025年

作者： Chen, Ke-Jia Mu, Wenhui ZhengLiu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China State Key Laboratory of Novel Software Technology Nanjing University Jiangsu Nanjing210093 China

—Recent research has witnessed the remarkable progress of Graph Neural Networks (GNNs) in the realm of graph data representation. However, GNNs still encounter the challenge of structural imbalance. Prior solutions to this problem did not take graph heterophily into account, namely that connected nodes process distinct labels or features, thus resulting in a deficiency in effectiveness. Upon verifying the impact of heterophily on solving the structural imbalance problem, we propose to rectify the heterophily first and then transfer homophilic knowledge. To the end, we devise a method named HeRB (Heterophily-Resolved Structure Balancer) for GNNs. HeRB consists of two innovative components: 1) A heterophily-lessening augmentation module which serves to reduce inter-class edges and increase intra-class edges;2) A homophilic knowledge transfer mechanism to convey homophilic information from head nodes to tail nodes. Experimental results demonstrate that HeRB achieves superior performance on two homophilic and six heterophilic benchmark datasets, and the ablation studies further validate the efficacy of two proposed components. Copyright © 2025, The Authors. All rights reserved.

关键词： Network theory (graphs)

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension 39

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Hierarchical Alignment-enhanced Adaptive Grounding Network f...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

ISBN: (纸本)157735897X

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing no-target and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge Computing 8th

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SCESP: An Edge Server Placement Method Based on Spectral Cl...

8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Wang, Lijuan Guo, Yingya Yao, Jiangyuan Zhou, Siyu College of Computer and Data Science Fuzhou University Fuzhou350000 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350000 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China Tandon School of Engineering New York University New York10012 United States

ISBN: (纸本)9783031067600

With the rapid development of Internet of Things (IoT) and 5G, mobile edge computing is gaining popularity for its low computation latency, bandwidth costs and energy consumption. In mobile edge computing, the placement of edge servers is one of the most significant problems and attracts worldwide attention. However, two major problems of edge server placement: high access delay and unbalanced workload of edge servers, have not been completely solved yet. To better solve these two problems, this paper proposes a new Spectral-Clustering-based Edge Server Placement (SCESP) algorithm, which can effectively reduce the access delay and make the workload of each edge server more balanced. In the evaluation, we use the Shanghai Telecom’s base station dataset to test the performance of SCESP and extensive experiments demonstrate the superior performance of SCESP in reducing the access delay and balancing the edge server workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

Research and application of artificial intelligence based webshell detection model: A literature review

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

Research and application of Transformer based anomaly detection model: A literature review

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection