检索结果-内蒙古大学图书馆

Response time evaluation of mobile applications combining network protocol analysis and information fusion

INFORMATION AND software TECHNOLOGY 2022年 145卷

作者： Liu, Pan Li, Yihao Ludong Univ Sch Informat & Elect Engn Yantai Peoples R China Shanghai Business Sch Fac Business Informat Shanghai Peoples R China Shanghai Key Lab Comp Software Testing & Evaluati Shanghai Peoples R China

The response time of a mobile application (app), especially a mobile stock trading app, is an important factor that affects customer satisfaction. However, it is considerably difficult to accurately evaluate the performance of mobile apps owing to numerous real-world settings such as operating systems, hardware, and test environments. This paper presents a novel method to evaluate the response time of mobile apps on different mobile phones through combining network protocol analysis and information fusion. To make the data collected from the mobile app more reliable and credible, we recruited some volunteers to collect data on their mobile phones. Then we used the network protocol analysis method to obtain the response time of the mobile app on a mobile phone. Next, we adopted information fusion technology using the rank-score characteristic function to evaluate the response time of mobile apps on different mobile phones. Experiments were conducted to evaluate our approach on three types of mobile apps. The results showed that the proposed method can effectively evaluate the response time of mobile apps with low cost.

关键词： Response time evaluation Network protocol analysis Information fusion Mobile app

来源：评论

学校读者我要写书评

暂无评论

A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds

引用

FUTURE INTERNET 2018年第6期10卷

作者： Xie, Lei Chen, Shengbo Shen, Wenfeng Miao, Huaikou Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Key Lab Comp Software Testing & Evaluati Shanghai 201112 Peoples R China Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

关键词： self-adaptive VM consolidation dynamic multi-thresholds energy consumption QoS IaaS clouds

来源：评论

学校读者我要写书评

暂无评论

Test Sequence Reduction of Wireless Protocol Conformance testing to Internet of Things

引用

SECURITY AND COMMUNICATION NETWORKS 2018年第1期2018卷

作者： Lin, Weiwei Zeng, Hongwei Gao, Honghao Miao, Huaikou Wang, Xiaolin Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Key Lab Comp Software Testing & Evaluati Shanghai 201114 Peoples R China Taishan Univ Dept Informat Sci & Technol Tai An 271000 Shandong Peoples R China Shanghai Univ Comp Ctr Shanghai 200444 Peoples R China Shanghai Key Lab Intelligent Mfg & Robot Shanghai 200072 Peoples R China

Wireless communication protocols are indispensable in Internet of Things (loT), which refer to rules and conventions that must be followed by both entities to complete wireless communication or service. Wireless protocol conformance testing concerns an effective way to judge whether a wireless protocol is carried out as expected. Starting from existing test sequence generation methods in conformance testing, an unproved method based on overlapping by invertibility and multiple unique input/output (UIO) sequences is proposed in this paper. The method is accomplished in two steps: first, maximum-length invertibility-dependent overlapping sequences (IDOSs) are constructed, then a minimum-length rural postman tour covering the just constructed set of maximum-length I DOSs is generated and a test sequence is extracted from the tour. The soundness and effectiveness of the method are analyzed. Theory and experiment show that desirable test sequences can be yielded by the proposed method to reveal violations of wireless communication protocols in IoT.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Instance and Category Supervision are Alternate Learners for Continual Learning

Instance and Category Supervision are Alternate Learners for...

引用

International Conference on computer Vision (ICCV)

作者： Xudong Tian Zhizhong Zhang Xin Tan Jun Liu Chengjie Wang Yanyun Qu Guannan Jiang Yuan Xie East China Normal University Shanghai Key Laboratory of Computer Software Testing & Evaluating Chongqing Institute of East China Normal University Tencent YouTu Lab Xiamen University Contemporary Amperex Technology Co. Limited

Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills. Yet, current CL algorithms tend to incur class-level forgetting as the label information is often quickly overwritten by new knowledge. This motivates attempts to mine instance-level discrimination by resorting to recent self-supervised learning (SSL) techniques. However, previous works have pointed out that the self-supervised learning objective is essentially a trade-off between invariance to distortion and preserving sample information, which seriously hinders the unleashing of instance-level *** this work, we reformulate SSL from the information-theoretic perspective by disentangling the goal of instance-level discrimination, and tackle the trade-off to promote compact representations with maximally preserved invariance to distortion. On this basis, we develop a novel alternate learning paradigm to enjoy the complementary merits of instance-level and category-level supervision, which yields improved robustness against forgetting and better adaptation to each task. To verify the proposed method, we conduct extensive experiments on four different benchmarks using both class-incremental and task-incremental settings, where the leap in performance and thorough ablation studies demonstrate the efficacy and efficiency of our modeling strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Face photo-sketch synthesis via intra-domain enhancement

引用

KNOWLEDGE-BASED SYSTEMS 2023年 259卷

作者： Peng, Chunlei Zhang, Congyu Liu, Decheng Wang, Nannan Gao, Xinbo Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai 201112 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Face photo-sketch synthesis plays an increasingly important role in the field of law enforcement and entertainment. However, most of the existing methods only focus on how to cross the modality differences between the photo domain and the sketch domain, while neglecting the image quality enhancement of the generated faces. In this paper, we propose a novel intra-domain enhancement (IDE) method for the face photo-sketch synthesis task. Our method is composed of two steps to cope with the inter-domain generation gap and the intra-domain quality defect respectively, i.e. inter-domain face generation and intra-domain face enhancement. In the inter-domain face generation stage, we apply a probabilistic graphical model which is capable of synthesizing the coarse face sketch from photos with low quality. In the intra-domain face enhancement stage, a Multi-Layer feature Aggregation (MLA) based Generative Adversarial Networks (GAN) is designed to enhance facial details in the sketches and further refine the synthesized image quality. Our first phase helps bridge the modality gap between photos and sketches and ensures the shape consistency between them. The second phase aims to improve the detail-quality and resolution of the synthesized sketches. Extensive experimental results on public face sketch databases outperform the state-of-the-art face sketch synthesis methods. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Face sketch synthesis Probabilistic graphical model Generative Adversarial Networks Intra-domain enhancement

来源：评论

学校读者我要写书评

暂无评论

Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics

COMMUNICATIONS BIOLOGY

引用

COMMUNICATIONS BIOLOGY 2024年第1期7卷 1-17页

作者： Wang, Tianqi Zhu, Huitong Zhou, Yunlan Ding, Weihong Ding, Weichao Han, Liangxiu Zhang, Xueqin East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Sch Med Dept Clin Lab Shanghai Peoples R China Fudan Univ Huashan Hosp Shanghai Peoples R China Manchester Metropolitan Univ Sch Comp Math & Digital Technol Manchester England Shanghai Key Lab Comp Software Evaluating & Testin Shanghai Peoples R China

Spatial transcriptomics is an emerging technology that enables the profiling of gene expression in tissues while preserving spatial location information. This innovative approach is anticipated to provide a comprehensive understanding of the spatial distribution of different cells within tissues and facilitate in-depth analysis of tissue structure. To accurately recognize spatial domains from spatial transcriptomics, we have introduced a generalized deep learning method called GAAEST (Graph Attention-based Autoencoder for Spatial Transcriptomics). Our proposed approach effectively integrates both spatial location information and gene expression data from spatial transcriptomics. Specifically, it leverages spatial location details to construct a neighborhood graph and employs a graph attention network-based encoder to embed gene expression information into a spatially informed space. At the same time, to further optimize the learned potential embedding, self-supervised contrastive learning is introduced to capture spatial information at three levels: local, global and contextual feature of spots. Finally, the decoder reconstructs gene expressions, which are then clustered to identify spatial domains with similar expression patterns and spatial proximity. Based on our experiments conducted on multiple datasets, GAAEST consistently outperforms existing state-of-the-art methods. The proposed GAAEST demonstrates excellent capabilities in spatial domain recognition, positioning it as an ideal tool for advancing spatial transcriptomics research. The graph attention based automatic encoder utilizing contrastive learning effectively combines spatial location information and gene expression data from spatial transcriptomics for enhanced domain recognition capacity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EFwork: An Efficient Framework for Constructing a Malware Knowledge Graph

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：