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HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

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Computers, Materials & Continua 2024年第11期81卷 2643-2664页

作者： Rongwei Yu Xiya Guo Peihao Zhang Kaijuan Zhang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Encrypted traffic plays a crucial role in safeguarding network security and user ***,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic *** methods for detecting encrypted traffic face two significant ***,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network ***,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential *** address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed *** approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context ***-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate *** select the ISCXVPN and the USTC-TK2016 dataset for our *** results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

关键词： Encrypted network traffic graph neural network traffic classification deep learning

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SPIDER:Speeding up Side-Channel Vulnerability Detection via Test Suite Reduction

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Tsinghua Science and Technology 2023年第1期28卷 47-58页

作者： Fei Yan Rushan Wu Liqiang Zhang Yue Cao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Side-channel attacks allow adversaries to infer sensitive information,such as cryptographic keys or private user data,by monitoring unintentional information leaks of running *** side-channel detection methods can identify numerous potential vulnerabilities in cryptographic implementations with a small amount of execution traces due to the high diffusion of secret inputs in crypto ***,because non-cryptographic programs cover different paths under various sensitive inputs,extending existing tools for identifying information leaks to non-cryptographic applications suffers from either insufficient path coverage or redundant *** address these limitations,we propose a new dynamic analysis framework named SPIDER that uses fuzzing,execution profiling,and clustering for a high path coverage and test suite reduction,and then speeds up the dynamic analysis of side-channel vulnerability detection in non-cryptographic *** analyze eight non-cryptographic programs and ten cryptographic algorithms under SPIDER in a fully automated way,and our results confirm the effectiveness of test suite reduction and the vulnerability detection accuracy of the whole framework.

关键词： side-channel detection test suite reduction dynamic analysis

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Omni-Detection of Adversarial Examples with Diverse Magnitudes

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China Communications 2024年第12期21卷 139-151页

作者： Ke Jianpeng Wang Wenqi Yang Kang Wang Lina Ye Aoshuang Wang Run Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University) Ministry of EducationWuhan 430072China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of *** of methods have been pro-posed to defend against adversarial ***-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown *** ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,***,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer *** results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,***,our approach also shows its potential in detecting unknown attacks.

关键词： adversarial example detection ensemble learning feature maps fragile and high-intensity ad-versarial examples

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A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification

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Journal of Ambient Intelligence and Humanized computing 2023年第6期14卷 8003-8018页

作者： Ding, Hongwei Cui, Xiaohui School of Cyber Science and Engineering Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan China

Imbalanced data often exists in related fields such as banking, insurance, security and medical care. The imbalanced distribution of data will lead to the deviation of decision-making, making it easy for a small number of samples to be divided incorrectly. Therefore, as a challenging task, imbalanced data classification has attracted extensive research in many disciplines. In this study, we propose a method based on the combination of clustering and generative adversarial network (GAN) to deal with the problem of imbalanced data classification. Firstly, we divide the majority class samples into three types according to the K-nearest neighbor algorithm. Secondly, undersampling is performed on the three types of data in the majority class through the clustering method. Then, a GAN model for tabular data generation is designed for oversampling of minority class samples. Finally, the preprocessed majority and minority data are used to train the machine learning model. We used real-world data sets to conduct relevant test experiments. The experimental results show that the imbalanced data processed by the method in this paper have achieved excellent results in the two evaluation indicators of the three common classification methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Generative adversarial networks

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Generalize Audio Deepfake Algorithm Recognition via Attribution Enhancement

Generalize Audio Deepfake Algorithm Recognition via Attribut...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zhigang Ye, Dengpan Li, Jingyang Deng, Jiacheng Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798350368741

The development of voice cloning techniques has made forgery audios indistinguishable, posing an urgency to trace their sources. Many existing works focus on improving identification accuracy for audio deepfake algorithm recognition. However, most methods ignore the impact of complex information in audio signals on attribution. In this paper, we propose an audio deepfake attribution enhancement (ADAE) strategy, which aims to magnify the fingerprints of generation styles by removing the speaker information. This is achieved through an information disentangle block with an extra speaker encoder. In addition, we propose the FakeSource dataset, a novel audio deepfake algorithm recognition dataset that contains 25 different voice cloning algorithms, to address the constraint of data scarcity. Experiments on the FakeSource dataset demonstrate that ADAE improves the performance of unseen algorithm detection. We also assess ADAE on a more challenging training-free task which shows competitive performance. © 2025 IEEE.

关键词： Audio deepfake algorithm recognition feature enhancement representation disentangle

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Harnessing Dimensional Contrast and information Compensation for Sentence Embedding Enhancement

Harnessing Dimensional Contrast and Information Compensation...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Kang Ding, Yuzhe Li, Bobo Wang, Haining Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798350368741

Unsupervised sentence embedding learning excels through positive sample construction and instance-level contrastive learning (ICL). However, this approach can lead to over-compression and dimensional contamination from noisy data augmentation and unconstrained ICL processes. To mitigate these issues, we propose a novel enhancement method, MSSE, which incorporates an information Compensation Mechanism (ICM) and a Dimensional-Level Contrastive Learning Mechanism (DCM). ICM, inspired by the information bottleneck principle, prevents excessive compression in representation learning. DCM constrains ICL and minimizes information leakage across dimensions. Experimental results show that MSSE surpasses existing competitive baselines across seven STS tasks, including unsupervised and few-shot scenarios. The source code is available at https://***/Hekang001/MSSE. © 2025 IEEE.

关键词： contrastive learning natural language processing Sentence embedding unsupervised learning

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NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction 31

NCRE: A Benchmark for Document-level Nominal Compound Relati...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cao, Jincheng Li, Bobo Liu, Jiang Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798891761964

Entity and relation extraction is a conventional task in the field of information extraction. Existing work primarily focuses on detecting specific relations between entities, often constrained to particular fields and lacking general applicability. In response, we propose a novel task: nominal compound relation extraction (NCRE), which concentrates on abstract and broadly applicable relation extraction between noun phrases. This task diverges significantly from traditional entity and relation extraction in two key respects. Firstly, our task involves general nominal compounds rather than named entities, which are longer and encompass a broader scope, presenting significant challenges for extraction. Secondly, relation extraction in NCRE demands an in-depth understanding of context to detect abstract relations. We manually annotate a high-quality Chinese dataset for the NCRE task and develop a model incorporating the rotary position-enhanced word pair (RoWP) detection schema. Experimental results demonstrate the efficiency of our RoWP model over previous baselines, while the suboptimal F1 scores indicate that NCRE remains a challenging task. Our code and data are available at https://***/yeecjc/NCRE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic information 30

Enhancing Cross-Document Event Coreference Resolution by Dis...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Gao, Qiang Li, Bobo Meng, Zixiang Li, Yunlong Zhou, Jun Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9782493814104

Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lacking the ability to utilize document-level information. As a result, they struggle to capture long-distance dependencies. This shortcoming leads to their underwhelming performance in determining coreference for the events where their argument information relies on long-distance dependencies. In light of these limitations, we propose the construction of document-level Rhetorical Structure Theory (RST) trees and cross-document Lexical Chains to model the structural and semantic information of documents. Subsequently, cross-document heterogeneous graphs are constructed and GAT is utilized to learn the representations of events. Finally, a pair scorer calculates the similarity between each pair of events and co-referred events can be recognized using standard clustering algorithm. Additionally, as the existing cross-document event coreference datasets are limited to English, we have developed a large-scale Chinese cross-document event coreference dataset to fill this gap, which comprises 53,066 event mentions and 4,476 clusters. After applying our model on the English and Chinese datasets respectively, it outperforms all baselines by large margins. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Clustering algorithms

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Transferable and Robust Dynamic Adversarial Attack against Object Detection Models

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IEEE Internet of Things Journal 2025年第11期12卷 16171-16180页

作者： Wang, Xinxin Chen, Jing Zhang, Zijun He, Kun Wu, Zongru Du, Ruiying Li, Qiao Liu, Gongshen Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Shanghai Jiao Tong University Department of Electronic Information and Electrical Engineering Shanghai201100 China

Object detection models have been widely deployed in physical world applications, and they are vulnerable to adversarial attacks. However, most adversarial attacks are implemented in a white-box setting, and under ideal shooting conditions such as fixed distances and angles, and thus have limited attack success rate (ASR) in practice. In this paper, we present a transferable and robust dynamic adversarial attack where the adversarial patches can be printed on or attached to non-rigid objects such as clothes. We develop a cascade module with a momentum-based technique to optimize adversarial patches against various object detection models, achieving better transferability of the patches in a black-box setting. We also develop a strategy of distance-Adaptive patch generation and employ perspective transformation to enhance the robustness of patches. To evaluate the attack performance, we conduct extensive experiments on seven mainstream object detection models at different distances and angles. The results show that our method can achieve an average ASR of 69.85%, which is 3.27 times that of the baseline method at 3 meters. © 2014 IEEE.

关键词： Object detection Computational modeling Closed box Robustness Adaptation models trusted computing information security aerospace engineering Proposals Aerodynamics

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PowerDetector:Malicious PowerShell Script Family Classification Based on Multi-Modal Semantic Fusion and Deep Learning

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China Communications 2023年第11期20卷 202-224页

作者： Xiuzhang Yang Guojun Peng Dongni Zhang Yuhang Gao Chenguang Li School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationWuhan 430072China School of Information Guizhou University of Finance and EconomicsGuiyang 550025China

Power Shell has been widely deployed in fileless malware and advanced persistent threat(APT)attacks due to its high stealthiness and live-off-theland ***,existing works mainly focus on deobfuscation and malicious detection,lacking the malicious Power Shell families classification and behavior ***,the state-of-the-art methods fail to capture fine-grained features and semantic relationships,resulting in low robustness and *** this end,we propose Power Detector,a novel malicious Power Shell script detector based on multimodal semantic fusion and deep ***,we design four feature extraction methods to extract key features from character,token,abstract syntax tree(AST),and semantic knowledge ***,we intelligently design four embeddings(i.e.,Char2Vec,Token2Vec,AST2Vec,and Rela2Vec) and construct a multi-modal fusion algorithm to concatenate feature vectors from different ***,we propose a combined model based on transformer and CNN-Bi LSTM to implement Power Shell family *** experiments with five types of Power Shell attacks show that PowerDetector can accurately detect various obfuscated and stealth PowerShell scripts,with a 0.9402 precision,a 0.9358 recall,and a 0.9374 ***,through singlemodal and multi-modal comparison experiments,we demonstrate that PowerDetector’s multi-modal embedding and deep learning model can achieve better accuracy and even identify more unknown attacks.

关键词： deep learning malicious family detection multi-modal semantic fusion PowerShell

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