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International Joint Conference on Neural networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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HF-Mid: A Hybrid Framework of network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning information leakage Malware Security Risk management

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MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

arXiv

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arXiv 2022年

作者： Duan, Yue Zhao, Zhen Qi, Lei Wang, Lei Zhou, Luping Shi, Yinghuan Gao, Yang The National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia The School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while low-confidence samples are employed to achieve a simpler goal — to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Our code and model weights have been released at https://***/NJUyued/MutexMatch4SSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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A Text Multi-label Classification Scheme Based on Resampling and Ensemble Learning 8th

A Text Multi-label Classification Scheme Based on Resampling...

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

作者： Wang, Tianhao Weng, Tianrang Ji, Jiacheng Zhong, Mingjun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China Department of Computing Science University of Aberdeen AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9783031067600

The medical dispute cases are professional and closely related to medicine. Therefore, the mediation of cases in practice depends heavily on similar historical cases. Multi-label classification of legal documents can efficiently filter irrelevant historical cases, which helps to recommend the similar historical cases faster and better. However, the imbalance and label symbiosis of the data set directly affect multi-label classification of legal documents. Therefore, a multi-label classification scheme based on resampling and ensemble learning is presented in this paper The scheme includes two parts: in the first part, in order to reduce the impact of label symbiosis on resampling, a resampling algorithm based on the average sparsity of the label set is proposed improve the imbalance of the data set;in the second one, a multi-label classification algorithm based on ensemble learning is proposed to train multiple base classifiers and combine each base classifier with a voting strategy of one vote. It can effectively improve the effect of multi-label classification. The experimental results show that the scheme proposed in this paper can improve the effect of multi-label classification and is not only suitable for legal documents but also applicable for other text data sets with imbalanced classes and label symbiosis problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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A study on improved mrmf algorithm on uncertain graph 2nd

A study on improved mrmf algorithm on uncertain graph

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2nd International Cognitive Cities Conference, IC3 2019

作者： Wen, Kejie Yang, Juan Liu, Wei Yang, Yanchun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

With the increased size and density of graph, the performance of the space decomposition-based algorithm (SDBA) decreases rapidly in terms of the most reliable maximum flow problem (MRMF). To solve this problem, the double filter space decomposition-based algorithm (DF-SDBA) is proposed by applying the probability filter and the cut-set filter. First, among the intervals to be processed in the process of space decomposition, DF-SDBA algorithm uses the probability constraint to filter out the intervals which own smaller probability of the lower boundary distribution than the current maximum flow, reducing the number of iterations effectively. Then the cut-set constraint is applied to unspecified intervals. According to the rule that all the edges in the cut-sets must exist in the upper bound of each interval, the unspecified intervals are filtered out, cutting down the number of partition intervals once again. The experimental results reveal that compared with the SDBA algorithm, the DF-SDBA algorithm can reduce the number of the intervals needed to be divided can obtain better performance. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

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Fine-Grained Access Control with Privacy-Preserving Data Retrieval for Cloud-Assisted IoV

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Wenchao Xia, Chunhe Yang, Simiao Wang, Kangming Huang, Guotao Huang, Luqi Guo, Fuchun Susilo, Willy Wang, Tianbo Beihang University School of Cyber Science and Technology Beijing100191 China University of Wollongong School of Computing and Information Technology Institute of Cybersecurity and Cryptology WollongongNSW2522 Australia Beihang University Beijing Key Laboratory of Network Technology Beijing100191 China Guangxi Normal University Guangxi Collab. Innovation Center of Multi-source Information Integration and Intelligent Processing Guilin541004 China Beihang University Sino-French Engineer School Beijing100191 China Beihang University Computer Science and Engineering School Beijing100191 China

In addition to the autonomous driving technology of single vehicles, the inter-group control algorithm serving the data sharing of multi-vehicle cooperative driving has also attracted widespread attention. To ensure secure communication, many encryption schemes have been proposed to protect the interaction data between vehicles. Nevertheless, traditional public key encryption schemes hinder the sharing of encrypted data. Based on the premise of ensuring the confidentiality of encrypted information, in order to facilitate efficient data sharing, conduct data searches across extensive cloud-based datasets, and authorize access under specified conditions, we introduce Fine-Grained Access Control with Privacy-Preserving Data Retrieval (FGAC-PPDR) for the Internet of Vehicles. This scheme offers a secure, flexible, and privacy-centric approach to data sharing for groups of vehicles in the IoV. Our proposed scheme enables encrypted data to be retrieved at the group level by the cloud server, and prevents vehicles outside the group from performing equality tests on the ciphertext. Furthermore, the data owner can create an authorization token with defined conditions to specify how the data is shared. During the process of data search and sharing, intensive computing tasks are undertaken by cloud servers with abundant computational resources. We also demonstrate that our scheme is secure against chosen ciphertext attacks (CCA). Finally, we provide security and performance analyses that verify the feasibility and effectiveness of our proposal. © 2025 IEEE.

关键词： Differential privacy

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Transfer synthetic over-sampling for class-imbalance learning with limited minority class data

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Frontiers of computer Science 2019年第5期13卷 996-1009页

作者： Xu-Ying LIU Sheng-Tao WANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Collaborative Innovation Center for Wireless Communications Technology Nanjing 210096 China

The problem of limited minority class data is encountered in manyclass imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Mos t sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.

关键词： machine learning data mining class imbalance over sampling boosting transfer learning

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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Multi-Functional Beamforming Design for Integrated Sensing, Communication, and Computation

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IEEE Transactions on Communications 2024年

作者： Zhao, Yapeng Wu, Qingqing Chen, Wen Zeng, Yong Liu, Ruiqi Mei, Weidong Hou, Fen Ma, Shaodan Shanghai Jiao Tong University Department of Electronic Engineering 200240 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518057 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Macau Department of Electrical and Computer Engineering 999078 China

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints. © 1972-2012 IEEE.

关键词： Convex optimization

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