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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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An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model 1

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

作者： Han, Weijie Xue, Jingfeng Zhang, Fuquan Zhang, Yingfeng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Space Information Space Engineering University Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Equipment Development Department Beijing100071 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic. © 2020, Springer Nature Switzerland AG.

关键词： learning systems

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machine learning Agricultural Application Based on the Secure Edge Computing Platform 1

引用

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

作者： Fan, Wu Xu, Zhuoqun Liu, Huanghe Zongwei, Zhu Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou215000 China Graduate School at Shenzhen Tsinghua University Shenzhen518055 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

machine learning (ml) is fast becoming a powerful tool for increasing agricultural production, for instance, ml predicts weather and yield through satellite images. Such approaches, however, most applications are based on expensive cloud servers to handle massive calculations, which will easily lead to important data leakage (e.g. crop cultivation methods, annual output, etc.), and increase the economic burden of farmers. Motivated by the status of agriculture, especially small farms that are underfunded and require data confidentiality. We propose an application for agricultural object detection based on a secure edge computing platform that can be used for agricultural output statistics and automated spraying agriculture. Compared with cloud-based ml agricultural applications, edge-based computing platforms do not require data uploading and downloading, which protects data security and reduces capital costs. We use some techniques in the training model and compression model to solve the problems of model accuracy and real-time, as follows: (1) Category-based Assisted Excitation model is proposed to improve the YOLOv3 accuracy. (2) Use layer pruning and channel pruning together to achieve a small-scale structure search and maximize real-time performance. We have evaluated our optimized model based on NVIDIA Jetson TX2 and Fruit Dataset. Its frames per second (FPS) is 11.63, mean average precision (MAP) is 89.6%. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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Querying Little Is Enough: Model Inversion Attack via Latent Information 3rd

Querying Little Is Enough: Model Inversion Attack via Latent...

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

作者： Mo, Kanghua Huang, Teng Xiang, Xiaoyu Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China College of Cyber Science Nankai University Tianjin China

ISBN: (纸本)9783030624590

With the development of machine learning (ml) technology, various online intelligent services use ml models to provide predictions. However, attacker may obtain privacy information of the model through interaction with online services. Model inversion attacks (MIA) is a privacy stealing method that utilizes ml models output values to reconstruct input values. In particular, an indispensable step of implementing proposed MIA approaches is that the attacker query the auxiliary datasets entirely. However, in reality, it will be inefficient to transfer huge datasets to online services to get prediction values of inference models. More seriously, the huge transmission may cause the administrator’s active defense. In this paper, we propose a novel MIA scheme which reduce queries on auxiliary datasets, by utilizing latent information of primitive models as high dimension features. We systematically evaluate our inversion approach in convolutional neural networks (CNN) classifier on LFW, pubFig, MNIST datasets. The experimental results show that even with a few queries of the inference model, our inversion approach still work accurately and outperforms than previous approaches. As conclusion, our method proves that implementing MIA does not require querying all auxiliary data on the classifier model, making it more difficult for the administrator to defend against the attack and elicit more investigations for privacy-preserving. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Event-Triggered Control for Distributed Optimal in Multi-agent Systems with External Disturbance 3rd

Event-Triggered Control for Distributed Optimal in Multi-age...

引用

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

作者： Liu, Yuanshan Yang, Hongyong Yang, Yize Li, Yuling School of Information and Electrical Engineering Ludong University Yantai264025 China School of Electrical and Electronic Engineering University of Adelaide Adelaide Australia Key Laboratory of Cyber-Physical System and Intelligent Control in Universities of Shandong Ludong University Yantai264025 China

ISBN: (纸本)9783030622220

In this paper, based on linear quadratic theory, the optimal control problem for multi-agent systems with external disturbances is studied. Firstly, the optimal distributed controller is designed by the performance index function without the disturbances. Then, by using observer to estimate the external disturbances. Next, based on an event-triggered mechanism, the composite control protocol is designed with least sampling interval. The consistency control algorithm is analyzed by means of modern control theory and matrix theory, and distributed event-triggered conditions are obtained. Finally, the proposed algorithm is verified by simulation. © 2020, Springer Nature Switzerland AG.

关键词： Multi agent systems

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Building Undetectable Covert Channels Over Mobile Networks with machine learning 3rd

Building Undetectable Covert Channels Over Mobile Networks w...

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

作者： Zhang, Xiaosong Pang, Ling Guo, Linhong Li, Yuanzhang Department of Computer Science and Technology Tangshan University Tangshan063000 China National Key Laboratory of Science and Technology on Information System Security Beijing100101 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030622220

Covert channel is an important way to transmit covert message and implement covert communication through the network. However, the existing research on covert channel cannot meet the security requirements of covert communication in the complex mobile networks. There are problems such as low transmission capacity, insufficient adaptability to network complexity, and difficulty in countering the detection of covert channels by adversaries. In this paper, we preprocess video traffics over mobile network, and extract traffic features to build a target model. We analysis traffic data by machine learning method to improve the undetectability of the covert channel. Based on the characteristics of real-time interactive communication, gray code and interval block are employed to improve the robustness of covert communication in the complex network environment. A cover channel over VoLTE video traffic, which is based on video packet reordering supported by machine learning algorithms, is proposed to realize the awareness and confrontation of detection attacks on the network side. The covert channel is built over mobile network to ensure end-to-end reliable covert communication under complex network conditions. © 2020, Springer Nature Switzerland AG.

关键词： Wireless networks

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A Novel Method of Network security Situation Assessment Based on Evidential Network 3rd

A Novel Method of Network Security Situation Assessment Base...

引用

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

作者： Li, Xiang Deng, Xinyang Jiang, Wen School of Electronics and Information Northwestern Polytechnical University Xi’an China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030622220

Network security situation awareness is a new type of network security technology. It evaluates the network security situation in real time from a macro perspective. Also it can predict the trend of the development of the network security situation, providing a basis for the decision analysis of administrators. It is difficult to obtain complete and accurate information in network security situation assessment by using evidential network. So we introduce an evidential network based on Bayesian network to solve that problem. Firstly, transform the parent node information and inference rules into plausibility function so as to be compatible with imperfect and inaccurate information. Secondly, we use the full probability formula of Bayesian network as reference to make similar reasoning under the framework of evidence theory. Then transform the inference result to BPA form by using the minimum specificity algorithm, and obtain the final result by projection. Finally, an example of network security situation assessment is given to illustrate the rationality and effectiveness of the method. © 2020, Springer Nature Switzerland AG.

关键词： Bayesian networks

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Cross-Project Software Defect Prediction Based on Feature Selection and Transfer learning 3rd

Cross-Project Software Defect Prediction Based on Feature Se...

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

作者： Lei, Tianwei Xue, Jingfeng Han, Weijie School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Space Information Space Engineering University Beijing101416 China

ISBN: (纸本)9783030624620

Cross-project software defect prediction solves the problem that traditional defect prediction can’t get enough data, but how to apply the model learned from the data of different mechanisms to the target data set is a new problem. At the same time, there is the problem that information redundancy in the training process leads to low accuracy. Based on the difference of projects, this paper uses MIC to filter features to solve the problem of information redundancy. At the same time, combined with the TrAdaboost algorithm, which is based on the idea of aggravating multiple classification error samples, this paper proposes a cross-project software prediction method based on feature selection and migration learning. Experimental results show that the algorithm proposed in this paper has better experimental results on AUC and F1. © 2020, Springer Nature Switzerland AG.

关键词： Defects

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Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting 1

引用

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

作者： Xie, Qian Huang, Zheng Guo, Jie Qiu, Weidong Shanghai Jiao Tong University Shanghai China Westone Cryptologic Research Center Beijing China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Distributed Denial-of-Service (DDoS) attacks disrupts the availability of essential services, which are one of the most harmful threats in today’s Internet. Many DDoS detection algorithms based on machine learning technology have emerged in recent years, for example, the LUCID algorithm, GNN model. But considering that DDoS attacks are based on both time and space, these algorithms only considered time and ignored space. Besides, by piece network traffic data is often difficult to obtain. The model we used in this paper is based on the Spatio-Temporal Graph Convolutional Network (STGCN) proposed by Yu B. et al. And on this basis, the model is improved for the unique characteristics of DDoS attacks. By considering the time-dependence of the network topology and network traffic, this model has a good recognition rate on the online DDoS data set, and can achieve detection of DDoS attacks under the premise of using only two-way traffic information. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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A File-Level Continuous Data Protection Scheme for Enforcing security Baseline 3rd

A File-Level Continuous Data Protection Scheme for Enforcing...

引用

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

作者： Jiang, Xiangxiang Ma, Yuxi Zhao, Gang Kuang, Xiaohui Li, Yuanzhang Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing China Artificial Intelligence and Blockchain Guangzhou University Guangzhou China National Key Laboratory of Science and Technology on Information System Security Beijing China Zhejiang Lab HangzhouZhejiang China

ISBN: (纸本)9783030622220

Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance. © 2020, Springer Nature Switzerland AG.

关键词： Digital storage

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An Adaptive Data Protection Scheme for Optimizing Storage Space 3rd

An Adaptive Data Protection Scheme for Optimizing Storage Sp...

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

作者： Ming, Meng Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China National Key Laboratory of Science and Technology on Information System Security Beijing China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9783030624590

Data is the main driving factor of artificial intelligence represented by machine learning, and how to ensure data security is one of the severe challenges. In many traditional methods, a single snapshot strategy is used to protect data. In order to meet the flexibility of data protection and optimize storage space, this paper presents a new architecture and an implementation in the Linux kernel. The idea is to hook system calls and analyze the relationship between applications and files. By tracking system calls, the system can perceive the file modification and automatically adjust the time interval for generating snapshots. Time granularity changes with the application load to achieve on-demand protection. Extensive experiments have been carried out to show that the scheme can monitor the process of operating files, reduce storage costs and hardly affect the performance of system. © 2020, Springer Nature Switzerland AG.

关键词： Digital storage

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