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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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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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Overlapping Community Extraction: A Link Hypergraph Partitioning Based Method

Overlapping Community Extraction: A Link Hypergraph Partitio...

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IEEE International Conference on Services Computing (SCC)

作者： Haicheng Tao Zhiang Wu Jin Shi Jie Cao Xiaofeng Yu College of Computer Sci. and Eng. Nanjing University of Science and Technology China Jiangsu Provincial Key Lab. of E-Business Nanjing University of Finance and Economics China School of Information Management Nanjing University China School of Business Nanjing University China

Real-world networks often contain communities with pervasive overlaps such that nodes simultaneously belong to several groups. Community extraction, emerging in recent years, is considered to be a promising solution for finding meaningful communities from social networks. In this paper, we explore overlapping community extraction from a link partitioning perspective. First, we define the local link structure composed of a set of closely interrelated links, by extending the similarity of link-pairs to that of a group of links. Second, based upon our prior work, we transform the problem of mining local link structures into a pattern mining problem, and thus present an efficient mining algorithm. Third, we propose to use the hypergraph to assemble all local link structures, and employ hMETIS for hypergraph partitioning. Finally, based on extracted link communities, we restore the membership of nodes in the original graph owing to its links. Experimental results on various real-life social networks validate the effectiveness of the proposed method.

关键词： Communities Educational institutions Google Proteins Itemsets Partitioning algorithms Social network services

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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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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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A Quality-Centric Framework for Generic Deepfake Detection

arXiv

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arXiv 2024年

作者： Song, Wentang Yan, Zhiyuan Lin, Yuzhen Yao, Taiping Chen, Changsheng Chen, Shen Zhao, Yandan Ding, Shouhong Li, Bin Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security The SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen518060 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Youtu Lab Tencent Shanghai200233 China

This paper addresses the generalization issue in deepfake detection by harnessing forgery quality in training data. Generally, the forgery quality of different deepfakes varies: some have easily recognizable forgery clues, while others are highly realistic. Existing works often train detectors on a mix of deepfakes with varying forgery qualities, potentially leading detectors to short-cut the easy-to-spot artifacts from low-quality forgery samples, thereby hurting generalization performance. To tackle this issue, we propose a novel quality-centric framework for generic deepfake detection, which is composed of a Quality Evaluator, a low-quality data enhancement module, and a learning pacing strategy that explicitly incorporates forgery quality into the training process. The framework is inspired by curriculum learning, which is designed to gradually enable the detector to learn more challenging deepfake samples, starting with easier samples and progressing to more realistic ones. We employ both static and dynamic assessments to assess the forgery quality, combining their scores to produce a final rating for each training sample. The rating score guides the selection of deepfake samples for training, with higher-rated samples having a higher probability of being chosen. Furthermore, we propose a novel frequency data augmentation method specifically designed for low-quality forgery samples, which helps to reduce obvious forgery traces and improve their overall realism. Extensive experiments show that our method can be applied in a plug- and-play manner and significantly enhance the generalization performance. © 2024, CC0.

关键词： Digital forensics

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Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

arXiv

引用

arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high lab.r cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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Matching Biomedical Ontologies with Long Short-Term Memory Networks

Matching Biomedical Ontologies with Long Short-Term Memory N...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Jiang Xingsi Xue Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian China School of Computer Science and Mathematics Fujian University of Technology Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin Guangxi China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical concepts in different biomedical ontologies could be defined in various contexts or with different terms, which yields the biomedical ontology heterogeneity problem. It is crucial to find mapping among heterogeneity concepts of different biomedical ontologies for bridging the semantic gaps, which is the so-called biomedical ontology matching. Biomedical ontology matching problem is an open challenge due to the rich semantic meaning and the flexible representation on a biomedical concept. To address this challenging problem, in this work, it is regarded as a binary classification problem, and a Long Short-Term Memory Networks (LSTM)-based ontology matching technique is proposed to solve it. Our proposal improves the quality of the alignment by introducing the char-embedding technique, which takes into account the semantic and context information of concepts. The comparing results with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Semantics Logic gates Training Human anatomy Proposals Biomedical measurement

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