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

作者： Huacheng Li Chunhe Xia Tianbo Wang Haopeng Zhao Key Laboratory of Beijing Network Technology Beihang University Beijing China Key Laboratory of Beijing Network Technology Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Information diffusion prediction is the basis of many fundamental tasks, such as social recommendation and community detection. Currently, most researchers infer user correlation based on cascade records to predict future infected users. However, they only consider whether the infected users at each location are correctly predicted, ignoring the semantic differences between users who participate in the retweets of similar content and other users. This naturally leads to learning biased user features. In this paper, we compute semantic relevance between two different cascades (e.g., c m and c n ) based on user coincidence and develop the cascade correlation graph (CCG) to learn user features. Afterward, we devise a position-independent sequence similarity loss (SSL) function combined with an auxiliary predictor to optimize the user encoder. The above two methods make users participating in the relevant cascades more relevant than others. Experiments show that the two methods are effective, and the combination of them can improve performance more significantly.

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CHESS: Learning User Correlation from Semantic-related Cascades for Diffusion Prediction

CHESS: Learning User Correlation from Semantic-related Casca...

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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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Load Balancing Optimization for Transformer in Distributed Environment

Load Balancing Optimization for Transformer in Distributed E...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Delu Ma Zhou Lei Shengbo Chen Peng Wang School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9781665408790

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. Therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. Distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. The results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy.

关键词： Training Computational modeling Machine learning Load management Transformers Servers Distributed computing

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CFC: a Cascade Forest approach to discover Cancer driver genes using multi-omics data

CFC: a Cascade Forest approach to discover Cancer driver gen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Zhang Yijing Yang Zhe Wang Dongdong Li Jingping Liu Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science University of Illinois Urbana-Champaign Champaign Illinois United States of America

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

With the development of next-generation sequencing technology, massive genomic data has been generated, primarily encouraging research on cancer driver genes. Many bioinformatics methods were proposed to identify driver genes. However, the results of driver gene identification a mong these methods show considerable differences. It is still challenging to obtain a comprehensive catalog of cancer drivers. Although current methods have greatly promoted the development of driver genes, few methods can integrate the identification results of existing methods. To solve such problems in cancer driver genes research, we proposed a cascade forest model to discover cancer driver genes(CFC) that can integrate multi-omics data and annotation scores from different cancer driver gene identification algorithms. The proposed method got precise results for 33 cancer types and Pan-cancer. The CFC framework identified 275 driver genes in Pan-cancer, of which 179 were included in the Gold standard. The identified genes were enriched i n t he principal cancer signaling pathways.

关键词： Sequential analysis Annotations Genomics Forestry Data models Bioinformatics Next generation networking

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Test Sequence Generation Based on Chain Unique Input-Output Sequence in FSM Conformance testing

Test Sequence Generation Based on Chain Unique Input-Output ...

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作者： Weiwei Lin Hongwei Zeng School of Computer Engineering and Science Shanghai UniversityShanghai Key Laboratory of Computer Software Evaluating &Testing School of Computer Engineering and Science Shanghai University

This paper introduces a test method based on chain unique input/output(CUIO) sequence in finite state machine(FSM) conformance testing. For FSMs with UIO for each state, the test sequence based on CUIO can identify all the states and verify all the transitions. For FSMs without UIO for some states, the test sequence based on CUIO can identify as many states and verify as many transitions as possible. Meanwhile, W method is introduced to deal with the unidentified states and the unverified transitions. For FSMs without UIO for each state, the method completely degenerates to W method. It is proved by experiment that the average and maximum reduction rate are 45.5% and 63.2% comparing with UIO ***, the method is always feasible even when UIO method fails.

关键词： Test Sequence Generation Based on Chain Unique Input-Output Sequence in FSM Conformance testing

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computer-Aided Formalization of Product Family Models

SSRN

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SSRN 2022年

作者： Liu, Hongbo Wang, Xi Wang, Weiwei School of Computer Engineering and Scinece Shanghai University No.333 Nan Chen Road Baoshan District Shanghai Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating No.333 Nan Chen Road Baoshan District Shanghai200444 China

The software product line enables the rapid construction of software products by reusing core assets. Product family modeling is one of the important activities in software product line engineering, which is a model for managing the variability of the core assets of a product family, and the dominant technique is feature modeling. Feature modeling reflects the software requirements of the whole domain by recording a set of relatively stable features and the relationships between the features, and lacks feature individual requirement analysis. Some approaches provide internal analysis of features, but separate it from the feature model. To deal with the above problems, this paper proposes a systematic approach to formalizing product family models and provides automatic support for the formalization process. The approach takes the initial feature model as the starting point, and examines the consistency of the results with the feature model in the formalization of each feature, and uses this as a basis to guide the evolution of the feature model. A template library is given to provide a set of templates to guide the construction and evolution of the feature model. A library of rules is given to check the consistency of the formal specification of the feature with the feature model. Our approach combines variability modeling with the formal specification of features to obtain an internally consistent product family feature model. © 2022, The Authors. All rights reserved.

关键词： Formal specification

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Message from the InSTA 2022 chairs

Proceedings - 2022 IEEE 14th International Conference on Sof...

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Proceedings - 2022 IEEE 14th International Conference on software testing, Verification and Validation Workshops, ICSTW 2022 2022年 xvii页

作者： Masuda, Satoshi Nishi, Yasuharu Hagar, Jon Katayama, Tetsuro Liu, Qin Liu, Zhenyu Tsuda, Kazuhiko Yumoto, Tsuyoshi Takeda, T. Zhang, W. Hiruta, Y. Suhara, H. Masuda, S. Suzuki, K. Hagar, J. Tokyo City University Japan The University of Electro-Communications Japan Grand Software Testing United States University of Miyazaki Japan Tongji University China Shanghai Key Laboratory of Computer Software Testing and Evaluation China China University of Tsukuba Japan Japan Npo ASTER Japan Japan Software Bug Prediction Model Based on Mathematical Graph Features Metrics Japan Obtaining Fuzzing Results with Different Timeouts

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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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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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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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