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检索条件"任意字段=3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020"

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3rd international conference on machine learning for cyber security, ml4cs 2020

3rd International Conference on Machine Learning for Cyber S...

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

ISBN: (纸本)9783030624590

The proceedings contain 158 papers. The special focus in this conference is on machine learning for cyber security. The topics include: Classification of Malware Variant Based on Ensemble learning;Detection of Malicious Domains in APT via Mining Massive DNS Logs;Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting;cerebral Microbleeds Detection Based on 3D Convolutional Neural Network;Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network;machine learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things;A New Lightweight CRNN Model for Keyword Spotting with Edge Computing Devices;machine learning Agricultural Application Based on the Secure Edge Computing Platform;qoS Investigation for Power Network with Distributed Control Services;AndrOpGAN: An Opcode GAN for Android Malware Obfuscations;fog Server Placement for Multimodality Data Fusion in Neuroimaging;event-Triggered Control for Distributed Optimal in Multi-agent Systems with External Disturbance;Towards Privacy-Preserving Aggregated Prediction from SPDZ;a Secure Neural Network Prediction Model with Multiple Data Providers;GAN-Based Image Privacy Preservation: Balancing Privacy and Utility;A Novel Color Image Encryption Scheme Based on Controlled Alternate Quantum Walks and DNA Sequence Operations;cloud-Assisted Privacy Protection for Data Retrieval Against Keyword Guessing Attacks;deep learning Algorithms Design and Implementation Based on Differential Privacy;building Undetectable Covert Channels Over Mobile Networks with machine learning;an Improved Privacy-Preserving Stochastic Gradient Descent Algorithm;an Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model;recommender Systems with Condensed Local Differential Privacy;exploiting Bluetooth to Enquire Close Contacts Without Privacy Leakage.

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3rd international conference on machine learning for cyber security, ml4cs 2020

3rd International Conference on Machine Learning for Cyber S...

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3rd international conference on machine learning for cyber security, ml4cs 2020

ISBN: (纸本)9783030622220

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3rd international conference on machine learning for cyber security, ml4cs 2020

3rd International Conference on Machine Learning for Cyber S...

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3rd international conference on machine learning for cyber security, ml4cs 2020

ISBN: (纸本)9783030624620

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Survey on Privacy-Preserving machine learning Protocols 3rd

Survey on Privacy-Preserving Machine Learning Protocols

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Yang, Ruidi School of Management and Engineering Capital University of Economics and Business Beijing100070 China

ISBN: (纸本)9783030622220

machine learning, especially deep learning, is a hot research field in academia, and it is revolutionizing industry. However, the privacy-preserving problems are not solved. In this paper, we investigate the privacy-preserving technology in machine learning application. We first introduce the models in privacy-preserving machine learning protocols, and then we give an overview of the main privacy-preserving machine learning technologies. At last, we analyze the existing problems. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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security Problems and Countermeasures of Network Accounting Information System 3rd

Security Problems and Countermeasures of Network Accounting ...

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Hu, Yanling Jilin Engineering Normal University Changchun Jilin China

ISBN: (纸本)9783030622220

With the rapid development of network technology, the network environment has brought unprecedented opportunities to the development of network accounting information system, as well as the Problems of accounting information system security. Because of the openness of the network, the network accounting information system may be attacked from any corner of the world. This paper introduces the existing problems of network accounting information system security. Then, the countermeasures to solve the problems are put forward from two aspects of technical methods and design methods. © 2020, Springer Nature Switzerland AG.

关键词： Network security

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A DNS Server Classification Method Based on Long-Term Behavior Features 3rd

A DNS Server Classification Method Based on Long-Term Behavi...

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Wang, Rui Luo, Hao Wu, Zhigang Zhang, Shuzhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030624620

Obtaining the overall status of domain name system (DNS) by DNS measurement is of great significance to DNS protection and Internet traffic optimization. The accurate identification of the DNS server type is one of the challenging problems in the field of DNS measurement, given that the identification simply by protocol fields will cause inaccurate results owing to factors such as server misconfiguration and network attacks. This paper proposes a DNS server classification method based on long-term behavioral features, in which mass data of DNS records will be analyzed to extract long-term behavioral features, and machine learning (ml) will be applied to classify its types. Experimental results show that the method can effectively identify recursive and authoritative services, 25% higher in accuracy than that relying only on protocol fields. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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A Feature-Based Detection System of Adversarial Sample Attack 3rd

A Feature-Based Detection System of Adversarial Sample Attac...

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Wang, Fangwei Lu, Yuanyuan Li, Qingru Wang, Changguang Lab of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9783030624590

With the increase of malware, traditional malicious detection methods are not suitable to high-intensity detection work. In response to this question, many malware detection methods based on machine learning (ml-based) are proposed to address this problem. However, the ml-based detection method is vulnerable to the attack from adversarial samples. To overcome the limitation, we present our model. We protect the classification model from adversarial example attack by quantifying the similarity between the extracted image features and the expected features of the prediction class. Experimental results demonstrate that our model can detect the misclassification caused by adversarial samples with a higher accuracy than that of the Resnet-50. © 2020, Springer Nature Switzerland AG.

关键词： Data visualization

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Research on Classification Model of Government Big Data Based on Deep learning 3rd

Research on Classification Model of Government Big Data Base...

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Yuan, Qiong Mai, Duqiang Sun, Ying Liang, Peiyu Institute of Information Security Beijing Electronic Science and Technology Institute Beijing China Department of Electronics and Communication Engineering Beijing Electronic Science and Technology Institute Beijing China Department of Cryptography and Technology Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9783030624620

Based on analyzing the textual features of Government Big Data, this paper proposes the Attention-BiLSTM-CNN hybrid classification model. Firstly, the model uses the BiLSTM model to extract the sentence vector of the text, and then introduces an attention mechanism to automatically learn the weight distribution of keywords, and finally uses the CNN model to calculate the probability of the category. And design experiments to verify the effect of the new model. The experimental results show that the classification model proposed in this paper has a good classification effect in the government big data corpus, which can improve the efficiency of government big data classification. © 2020, Springer Nature Switzerland AG.

关键词： Big data

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machine learning for cyber security 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Xiaofeng Chen Hongyang Yan Qiben Yan Xiangliang Zhang

This three volume book set constitutes the proceedings of the Third international conference on machine learning for cyber security, ml4cs 2020, held in Xi’an, China in October 2020.

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

This three volume book set constitutes the proceedings of the Third international conference on machine learning for cyber security, ml4cs 2020, held in Xi’an, China in October 2020.

关键词： Systems and Data security Computer Appl. in Social and Behavioral Sciences Computers and Education machine learning Computer Communication Networks Computer System Implementation

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machine learning for cyber security 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Xiaofeng Chen Hongyang Yan Qiben Yan Xiangliang Zhang

This three volume book set constitutes the proceedings of the Third international conference on machine learning for cyber security, ml4cs 2020, held in Xi’an, China in October 2020.

ISBN: (数字)9783030624606

ISBN: (纸本)9783030624590

This three volume book set constitutes the proceedings of the Third international conference on machine learning for cyber security, ml4cs 2020, held in Xi’an, China in October 2020.

关键词： Systems and Data security Computer Appl. in Social and Behavioral Sciences Computers and Education Legal Aspects of Computing Cryptology Mobile and Network security

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