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

作者： Wanshuang Lin Chunhe Xia Tianbo Wang Chen Chen Yuan Zhao Weidong Zhou School of Cyber Science and Technology Beihang University Beijing China 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 Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The TLS encryption protocol plays a vital role in securing data transmission, but it also presents challenges for payload-based network Intrusion Detection Systems (NIDS). Existing methods utilize statistical characteristics of side-channel features, such as the mean packet length, to identify encrypted traffic. However, packet lengths are constrained by the Maximum Segment Size (MSS) of the TCP protocol. This constraint causes the length-varied sequence to be encapsulated into segments of equal length, resulting in information loss. Moreover, flow-level statistical features are vulnerable to interference from noisy packets, making it challenging to detect malicious traffic injected with benign packets effectively. In this paper, we propose an encrypted traffic detection model based on Record length and frEquency Domain Analysis (REDA). First, we reconstruct the TLS Record Length Sequence (TRLS), which is a length-varied sequence, to capture differences in traffic content during transmission. Second, we employ the Discrete Fourier Transform (DFT) to extract frequency domain features of the TRLS, which are resistant to attacker interference. Finally, an improved One-Class Support Vector Machine (OCSVM) algorithm is devised for the unsupervised detection of malicious traffic, enabling the identification of unknown attacks. Experiments show that REDA is superior to other state-of-the-art methods in terms of accuracy by 2.44%.

关键词： Support vector machines Resistance Analytical models Privacy Protocols Frequency-domain analysis Computational modeling

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DRRNet: Macro–Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection

arXiv

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arXiv 2025年

作者： Sun, Jianlin Fang, Xiaolin Guan, Juwei Gui, Dongdong Wang, Teqi Zhu, Tongxin School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The core challenge in Camouflage Object Detection (COD) lies in the indistinguishable similarity between targets and backgrounds in terms of color, texture, and shape. This causes existing methods to either lose edge details (such as hair-like fine structures) due to over-reliance on global semantic information or be disturbed by similar backgrounds (such as vegetation patterns) when relying solely on local features. We propose DRRNet, a four-stage architecture characterized by a "context-detail-fusion-refinement" pipeline to address these issues. Specifically, we introduce an Omni-Context Feature Extraction Module to capture global camouflage patterns and a Local Detail Extraction Module to supplement microstructural information for the full-scene context module. We then design a module for forming dual representations of scene understanding and structural awareness, which fuses panoramic features and local features across various scales. In the decoder, we also introduce a reverse refinement module that leverages spatial edge priors and frequency-domain noise suppression to perform a two-stage inverse refinement of the output. By applying two successive rounds of inverse refinement, the model effectively suppresses background interference and enhances the continuity of object boundaries. Experimental results demonstrate that DRRNet significantly outperforms state-of-the-art methods on benchmark datasets. Our code is available at https://***/jerrySunning/DRRNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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EGRC-Net: Embedding-induced Graph Refinement Clustering network

arXiv

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arXiv 2022年

作者： Peng, Zhihao Liu, Hui Jia, Yuheng Hou, Junhui The Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong The School of Computing & Information Sciences Caritas In-stitute of Higher Education Hong Kong School of Computer Science and Engineering SoutheastUniversity Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology structure of the embedding space. In order to address this issue, we propose a novel clustering network called Embedding-Induced Graph Refinement Clustering network (EGRC-Net), which effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation. Subsequently, we utilize the local geometric structure within the feature embedding space to construct an adjacency matrix for the graph. This adjacency matrix is dynamically fused with the initial one using our proposed fusion architecture. To train the network in an unsupervised manner, we minimize the Jeffreys divergence between multiple derived distributions. Additionally, we introduce an improved approximate personalized propagation of neural predictions to replace the standard graph convolution network, enabling EGRC-Net to scale effectively. Through extensive experiments conducted on nine widely-used benchmark datasets, we demonstrate that our proposed methods consistently outperform several state-of-the-art approaches. Notably, EGRC-Net achieves an improvement of more than 11.99% in Adjusted Rand Index (ARI) over the best baseline on the DBLP dataset. Furthermore, our scalable approach exhibits a 10.73% gain in ARI while reducing memory usage by 33.73% and decreasing running time by 19.71%. The code for EGRC-Net will be made publicly available at https://***/ZhihaoPENG-CityU/EGRC-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation

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International Conference on computer Vision (ICCV)

作者： Lihe Yang Wei Zhuo Lei Qi Yinghuan Shi Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Tencent Key Lab of Computer Network and Information Integration (Ministry of Education) Southeast University

ISBN: (纸本)9781665428132

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and fore-ground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5 i and 7.0% mIOU on COCO-20 i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://***/LiheYoung/MiningFSS.

关键词： Training computer vision Costs Codes Training data Prototypes Benchmark testing

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MNet: Rethinking 2D/3D networks for Anisotropic Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Dong, Zhangfu He, Yuting Qi, Xiaoming Chen, Yang Shu, Huazhong Coatrieux, Jean-Louis Yang, Guanyu Li, Shuo LIST Key Laboratory of Computer Network and Information Integration [Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing China Dept. of Medical Biophysics University of Western Ontario LondonON Canada

The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and over-fitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. 2) Our MNet latently fuses multi-dimensional features inside each basic module, simultaneously taking the advantages of 2D (high segmentation accuracy of the easily recognized regions in 2D view) and 3D (high smoothness of 3D organ contour) representations, thus obtaining more accurate modeling for target regions. Comprehensive experiments are performed on four public datasets (CT&MR), the results consistently demonstrate the proposed MNet outperforms the other methods. The code and datasets are available at: https://***/zfdong-code/MNet. © 2022, CC BY-NC-ND.

关键词： Convolution

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FedDRC: A Robust Federated Learning-based Android Malware Classifier under Heterogeneous Distribution

FedDRC: A Robust Federated Learning-based Android Malware Cl...

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International Conference on computer Supported Cooperative Work in Design

作者： Changnan Jiang Chunhe Xia Mengyao Liu Chen Chen Huacheng Li Tianbo Wang Pengfei 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 SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

关键词： Degradation Privacy Accuracy Federated learning Transfer learning Semantics Malware

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Funny words detection via Contrastive Representations and Pre-trained Language Model

Funny words detection via Contrastive Representations and Pr...

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Artificial Intelligence, networking and information Technology (AINIT), International Seminar on

作者： Yiming Du Zelin Tian Key Lab of Computer Network and Information Integration School of Computer Science and Engineering- Southeast University (SEU) Nanjing China University of British Columbia Vancouver Canada

ISBN: (纸本)9781665412971

Funniness detection of news headlines is a challenging task in computational linguistics. However, most existing works on funniness detection mainly tackle the scenario by simply judging whether a sentence is humorous, whose result is unstable due to factors such as sentence length. To solve this issue, in this paper, our idea is to fine-grained mine the detailed information of the words and the contextual relationship between different words in the sentence, which help to evaluate the correlation between keywords and the funniness of news headlines quantitatively. Specifically, we propose a funny words detection algorithm based on the contrastive representations learning and BERT model. To quantify the impact of different words on the degree of humor, we first subtract the funniness grades of the original news headlines and the funniness grades of the original news headlines with a single word replaced. Both funniness grades are predicted with a pre-trained model, which is supervised by a a threshold to limit the amount of data and ensure the validity of data. To ensure the accuracy of our prediction, we further introduce the contrastive learning to constrain the differences of news headlines before and after word replacement. Finally, according to the Root Mean Square Error (RMSE) matrix in our experiment, we develop a BERT model with mixed sequence embedding to generate a table about words and their corresponding funniness improvement about the news headlines.

关键词： Seminars Representation learning Correlation Bit error rate Predictive models Data models Task analysis

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Topic-aware evidence reasoning and stance-aware aggregation for fact verification

arXiv

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arXiv 2021年

作者： Si, Jiasheng Zhou, Deyu Li, Tongzhe Shi, Xingyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Computer Science University of Warwick United Kingdom

Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification;(ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence;2) maintaining topical coherence among multiple pieces of evidence;3) ensuring semantic similarity between the global topic information and the semantic representation of evidence;4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://***/jasenchn/TARSA. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Topic-driven and knowledge-aware transformer for dialogue emotion detection

arXiv

引用

arXiv 2021年

作者： Zhu, Lixing Pergola, Gabriele Gui, Lin Zhou, Deyu He, Yulan Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jinsong Zhang Xiaoxuan Mao Lu Li Hanqian Wu Joint graduate school Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education School of Cyber Science and Engineering Southeast University Nanjing China

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability.

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