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Smart World Congress (SWC), IEEE

作者： Huacheng Li Chunhe Xia Tianbo Wang Haopeng Zhao Key Laboratory of Beijing 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

Understanding the information diffusion trajectory in online social networks has become an ever-growing prominent research direction in the information era. Users interact with social information they are interested in, and cascades record diffusion trajectories of massive social information. It implies two characteristics of diffusion cascades: topic sharing among different cascades and infected user correlation within the same cascades. However, previous works ignore these two characteristics and primarily focus on whether each infected user is correctly predicted individually, which leads to learning biased diffusion models under the data-driven mode. To address this problem, we propose a novel architecture to exploit such latent user associations among topic-related cascades. First, we leverage the hypergraph and heterogeneous graph to exploit infected user correlations jointly and generate user feature vectors. Second, we devise an enhanced cascade similarity loss function to evaluate prediction results from delay (horizontal) and rank (vertical) scopes. Furthermore, we introduce the extra predictor to alleviate the interference with the primary model. Experiments on four public datasets validate the effectiveness of our proposed method and significantly outperform existing models on hits@κ and map@κ indicators.

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GUARD: Multigranularity-based Unsupervised Anomaly Detection Algorithm for Multivariate Time Series 8

GUARD: Multigranularity-based Unsupervised Anomaly Detection...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Meng, Fan Yang, Qunli He, Zhengda Yang, Shangdong Tang, Weidong Public Credit Information Center Jiangsu Strategy and Development Research Center Nanjing China Nanjing University State Key Lab for Novel Software Technology Nanjing China China Pharmaceutical University School of Science Nanjing China The School of Computer Science Nanjing University of Posts and Teleconnnunications Nanjing China China Telecom Corporation Limited Nanjing Branch Customer Operations Department Nanjing China

ISBN: (纸本)9781665477352

Unsupervised anomaly detection on multivariate time series (MTS) is a valuable problem in practice because it significantly reduces labeling costs. Even though most classical methods begin to consider inter-series dependencies, the hierarchical relationships between and within-series data are often ignored. To overcome these limitations, in this paper, we propose a multiGranUlArity based unsupeRvised anomaly Detection (GUARD) algorithm that captures the hierarchical relationships in three granularities and merges these dependencies in the following anomaly detection task. Specifically, the proposed algorithm first captures the internal dependencies within the subsequence and learns a hidden representation based on a trained autoencoder. Then, a hierarchical mechanism is designed to capture the local and global dependencies between the given subsequence and the whole set and learn their hidden representation. The experimental results show that the proposed algorithm can effectively and stably exploit the potential relationship of MTS and detect anomalies more accurately than the basic approaches. © 2022 IEEE.

关键词： Anomaly detection

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LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery

arXiv

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arXiv 2024年

作者： Ma, Xiaowen Lian, Rongrong Wu, Zhenkai Guo, Hongbo Yang, Fan Ma, Mengting Wu, Sensen Du, Zhenhong Song, Siyang Zhang, Wei The School of Software Technology Zhejiang University Hangzhou310027 China The school of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China The School of Computer Science and Technology Zhejiang University Hangzhou310027 China The School of Earth Sciences Zhejiang University Hangzhou310027 China The Innovation Center of Yangtze River Delta Zhejiang University Zhejiang Jiaxing314103 China The HUBG Lab Department of Computer Science University of Exeter United Kingdom

Remote sensing images usually characterized by complex backgrounds, scale and orientation variations, and large intra-class variance. General semantic segmentation methods usually fail to fully investigate the above issues, and thus their performances on remote sensing image segmentation are limited. In this paper, we propose our LOGCAN++, a semantic segmentation model customized for remote sensing images, which is made up of a Global Class Awareness (GCA) module and several Local Class Awareness (LCA) modules. The GCA module captures global representations for class-level context modeling to reduce the interference of background noise. The LCA module generates local class representations as intermediate perceptual elements to indirectly associate pixels with the global class representations, targeting at dealing with the large intra-class variance problem. In particular, we introduce affine transformations in the LCA module for adaptive extraction of local class representations to effectively tolerate scale and orientation variations in remote sensing images. Extensive experiments on three benchmark datasets show that our LOGCAN++ outperforms current mainstream general and remote sensing semantic segmentation methods and achieves a better trade-off between speed and accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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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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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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Resonant Beam Multi-Target DOA Estimation

arXiv

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arXiv 2024年

作者： Guo, Yixuan Jiang, Qingwei Xiong, Mingliang Fang, Wen Liu, Mingqing Zhang, Qingqing Liu, Qingwen Yan, Gang Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China Department of Computer Science and Technology Tongji University Shanghai201804 China College of Electronics and Information Engineering Tongji University Shanghai201804 China LiFi Research and Development Center Department of Engineering University of Cambridge Cambridge United Kingdom School of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou310014 China University of Electronic Science and Technology of China Zhejiang Huzhou313001 China School of Physics Science and Engineering Tongji University Shanghai200092 China

With the increasing demand for internet of things (IoT) applications, especially for location-based services, how to locate passive mobile targets (MTs) with minimal beam control has become a challenge. Resonant beam systems are considered promising IoT technologies with advantages such as beam self-alignment and energy concentration. To establish a resonant system in the radio frequency (RF) band and achieve multi-target localization, this paper designs a multi-target resonant system architecture, allowing a single base station (BS) to independently connect with multiple MTs. By employing a retro-directive array, a multi-channel cyclic model is established to realize one-to-many electromagnetic wave propagation and MT direction-of-arrival (DOA) estimation through echo resonance. Simulation results show that the proposed system supports resonant establishment between the BS and multiple MTs. This helps the BS to still have high DOA estimation accuracy in the face of multiple passive MTs, and can ensure that the DOA error is less than 1◦ within a range of 6 meters at a 50◦ field of view, with higher accuracy than active beamforming localization systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Direction of arrival

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REDA: Malicious Traffic Detection Based on Record Length and Frequency Domain Analysis

REDA: Malicious Traffic Detection Based on Record Length and...

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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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EFwork: An Efficient Framework for Constructing a Malware Knowledge Graph

EFwork: An Efficient Framework for Constructing a Malware Kn...

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

作者： Chen Chen Chunhe Xia Tianbo Wang Wanshuang Lin Yuan Zhao Yang 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

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Malware Knowledge Graph (MKG) serves as an essential auxiliary tool for malware detection and analysis. However, the construction of MKG faces several challenges, such as inadequate dataset quality, incomplete entity feature extraction, and the limitations imposed by deep learning techniques. To address these issues, we present an Efficient Framework for constructing a malware knowledge graph (EFwork). Firstly, we build a High-Quality Dataset (HQDataset) and introduce a metric for data quality assessment based on knowledge coverage, timeliness, and density. Subsequently, we develop a Named Entity Recognition (NER) model that extracts character features, part-of-speech features, and word features from the data, leveraging deep learning models to identify malware-related entities. Finally, we implement a rule-based filtering mechanism, utilizing a comprehensive Rule Database to eliminate entities that do not conform to predefined rules. Experimental result shows that our HQDataset demonstrates superior data quality when compared to other open-source datasets. Furthermore, our NER model combined with our Rule Database outperforms existing models, achieving improvements of 0.67%, 0.74%, and 0.69% in Precision, Recall, and F1-Score, respectively.

关键词： Measurement Deep learning Databases Filtering Data integrity Knowledge graphs Feature extraction

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Reinforcement Learning-Based Explainable Recommendation over Knowledge Graphs with Negative Sampling

Reinforcement Learning-Based Explainable Recommendation over...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Siyuan Zhang Yuanxin Ouyang Zhuang Liu Wenge Rong Zhang Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing China

Introducing knowledge graphs (KGs) into the recommender systems not only improves their performance but also enhances the interpretability. However, most KG-based recommendation methods have the problem of inefficiency and ex-post explanation, which reinforcement learning (RL) methods can solve properly. Most existing RL-based methods for explainable recommendations only consider positive rewards when designing the reward part of the RL environment, which is defective and misleads the policy of the RL agent. To address this problem, we propose Reinforced Knowledge Graph Reasoning with Reinforced Negative Sampling (RKGR-RNS) by introducing a negative sampling method into RL-based recommendation, which refines the reward mechanism to help optimize the agent’s policy. And a judge module is proposed to improve the performance of the recommender system further. Experiments on three real datasets demonstrate that our method is better than the state-of-the-art baseline.

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center-guided Classifier for Semantic Segmentation of Remote Sensing Images

arXiv

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arXiv 2025年

作者： Zhang, Wei Ma, Mengting Jiang, Yizhen Lian, Rongrong Wu, Zhenkai Cui, Kangning Ma, Xiaowen School of Software Technology Zhejiang University Hangzhou310027 China Innovation Center of Yangtze River Delta Zhejiang University Zhejiang Jiaxing314103 China School of Computer Science and Technology Zhejiang University Hangzhou310027 China Department of Mathematics City University of Hong Kong Hong Kong Noah’s Ark Lab Huawei Shanghai201206 China

Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing images more challenging. Moreover, existing semantic segmentation models for remote sensing images usually employ a vanilla softmax classifier, which has three drawbacks: (1) non-direct supervision for the pixel representations during training;(2) inadequate modeling ability of parametric softmax classifiers under large intraclass variance;and (3) opaque process of classification decision. In this paper, we propose a novel classifier (called centerSeg) customized for RSI semantic segmentation, which solves the abovementioned problems with multiple prototypes, direct supervision under Grassmann manifold, and interpretability strategy. Specifically, for each class, our centerSeg obtains local class centers by aggregating corresponding pixel features based on ground-truth masks, and generates multiple prototypes through hard attention assignment and momentum updating. In addition, we introduce the Grassmann manifold and constrain the joint embedding space of pixel features and prototypes based on two additional regularization terms. Especially, during the inference, centerSeg can further provide interpretability to the model by restricting the prototype as a sample of the training set. Experimental results on three remote sensing segmentation datasets validate the effectiveness of the model. Besides the superior performance, centerSeg has the advantages of simplicity, lightweight, compatibility, and interpretability. Code is available at https://***/xwmaxwma/rssegmentation. © 2025, CC BY.

关键词： Semantic Segmentation

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