2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)最新文献_第3页

Causal Unstructured Pruning in Linear Networks Using Effective Information 基于有效信息的线性网络因果非结构化剪枝

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00056

Changyu Zeng, Li Liu, Haocheng Zhao, Yu Zhang, Wei Wang, Ning Cai, Yutao Yue

{"title":"Causal Unstructured Pruning in Linear Networks Using Effective Information","authors":"Changyu Zeng, Li Liu, Haocheng Zhao, Yu Zhang, Wei Wang, Ning Cai, Yutao Yue","doi":"10.1109/CyberC55534.2022.00056","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00056","url":null,"abstract":"Excessive number of parameters in today’s (deep) neural networks demands tremendous computational resources and slows down training speed. The problem also makes it difficult to deploy these neural network models on capability constrained devices such as mobile devices. To address this challenge, we propose an unstructured pruning method that measures the causal structure of neural networks based on effective information (EI). It introduces an intervention to the input and computes the mutual information between the interference and its corresponding output, within a single linear layer measuring the importance of each weight. In the experiments, we found that the sparsity of EI pruning can reach more than 90%. Only 10% of non-zero parameters in the linear layers were needed compared to the benchmark methods without pruning, while ensuring similar level of accuracy and stable training performance in iterative pruning. In addition, as the invariance of the causal structure of the network is exploited, the network after pruning using EI is highly generalizable and interpretable than other methods.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123184963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DPP: Data Privacy-Preserving for Cloud Computing based on Homomorphic Encryption 基于同态加密的云计算数据隐私保护

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00016

Jing Wang, Fengheng Wu, Tingbo Zhang, Xiaohua Wu

引用次数: 1

Machine Learning Model Design for IoT-Based Flooding Forecast 基于物联网的洪水预测机器学习模型设计

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00025

Qinghua Wang

引用次数: 1

A Lightweight Deep Learning Model for Real-time Detection and Recognition of Traffic Signs Images Based on YOLOv5 基于YOLOv5的交通标志图像实时检测与识别的轻量级深度学习模型

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00042

Hui He, Qihong Chen, Guoping Xie, Boxiong Yang, Shelei Li, Bo Zhou, Yuye Gu

{"title":"A Lightweight Deep Learning Model for Real-time Detection and Recognition of Traffic Signs Images Based on YOLOv5","authors":"Hui He, Qihong Chen, Guoping Xie, Boxiong Yang, Shelei Li, Bo Zhou, Yuye Gu","doi":"10.1109/CyberC55534.2022.00042","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00042","url":null,"abstract":"The rapid and accurate identification of various road traffic signs is an important research topic in automotive vision systems. Specifically, the correct identification of road signs is an urgent problem requiring effective solutions to facilitate automatic driving. This paper proposes a new approach, PP-LCNet-P2-CT, for the detection and recognition of urban road signs in an automotive vision system using an improved YOLOv5 deep learning model. The main improvement of the PP-LCNet-P2-CT model includes the following: (1) Replacing the YOLOv5 backbone network with the lightweight network PP-LCNet to improve the real-time performance of the detection network; (2) Adding a small target detection head to the detection head to meet the needs of target detection with different scales and mitigate the adverse effects caused by drastic target scale changes; and (3) Integrating the CBAM convolutional block attention model that focuses on target features and the transformer coding block that can capture different local information to ensure the accuracy of lightweight model target detection. The model was tested with the Tsinghua traffic sign dataset, TT100k. The results show that the mAP index of the PP-LCNet-P2-CT model is increased by 29.84% and the FPS is increased by 24.05%, while the number of model parameters is decreased by 32.78% and the GFLOPs decreased by 34.41% compared with the classic YOLOv5 algorithm. The PP-LCNet-P2-CT model allows complex deep learning to be used successfully for unmanned ground vehicles (UGVs) with ordinary computing speeds and high real-time requirements.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Attack and Defense for Webshell Detection on Machine Learning Models 基于机器学习模型的Webshell检测的对抗性攻击与防御

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00017

Q. Zhang, Lishen Chen, Qiao Yan

{"title":"Adversarial Attack and Defense for Webshell Detection on Machine Learning Models","authors":"Q. Zhang, Lishen Chen, Qiao Yan","doi":"10.1109/CyberC55534.2022.00017","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00017","url":null,"abstract":"Machine learning (ML) models can be used for the automated processing and analysis of source codes, thus improving the detection of webshell malware source codes, which can enhance the security of the whole network. However, despite the successes of ML-based models in webshell detection, these models lack large amounts of data for training and are vulnerable to adversarial examples. We have built a larger and more precise dataset containing 2015 manually labeled webshell malware. A detection model trained with this dataset can achieve higher detection accuracy. We have also proposed a method to generate adversarial examples for the programming language without changing its logic. The main idea of our method is to insert perturbation codes that do not modify the webshell program’s semantics, thereby creating an adversarial example that can bypass the model’s detection. This method can effectively attack the existing webshell malware ML detection models without changing the original malicious functions. Experiments have shown that our method can generate webshell malware adversarial examples that evade model detection while obtaining the model’s confidence output. To defend against such attacks, we have applied retraining and adversarial fine-tuning.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131094272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Relation Enhanced Model For Abstractive Dialogue Summarization 抽象对话摘要的关系增强模型

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00047

Pengyao Yi, Ruifang Liu

引用次数: 0

UAV Trajectory Optimization for PHY Secure Communication Against Cooperative Eavesdroppers 针对协同窃听者的物理层安全通信无人机轨迹优化

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00055

Huanran Zhang, Lingfeng Shen, Ning Wang, X. Mu

引用次数: 0

Study on fast convergence method of IIR low pass filter IIR低通滤波器快速收敛方法研究

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00035

Zuping Zhang

引用次数: 0

Ghost Expectation Point with Deep Reinforcement Learning in Financial Portfolio Management 基于深度强化学习的幽灵期望点金融投资组合管理

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00030

Xuting Yang, Ruoyu Sun, Xiaotian Ren, Angelos Stefanidis, Fengchen Gu, Jionglong Su

{"title":"Ghost Expectation Point with Deep Reinforcement Learning in Financial Portfolio Management","authors":"Xuting Yang, Ruoyu Sun, Xiaotian Ren, Angelos Stefanidis, Fengchen Gu, Jionglong Su","doi":"10.1109/CyberC55534.2022.00030","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00030","url":null,"abstract":"Reinforcement learning algorithms have a wide range of applications in diverse areas, such as portfolio management, automatic driving, and visual object detection. This paper introduces a novel network architecture Ghost expectation point (GXPT) embedded in a deep reinforcement learning framework based on GhostNet, which is constructed using convolutional neural networks and ghost bottleneck modules. The Ghost bottleneck module can generate many Ghost feature maps, improving the ability of the network to extract information from the real-world market. Furthermore, the number of parameters and floating point operations (FLOPs) is reduced. We use the GXPT to realize Jiang et al.’s Ensemble of Identical Independent Evaluators (EIIE) framework. In the EIIE framework, GhostNet is adapted to implement Identical Independent Evaluators to evaluate the growth potential of each asset. In our experiments, we chose the Accumulated Portfolio Value (APV) and the Sharpe Ratio (SR) to assess the efficiency of our strategy in the back-test. It is found that our strategy is at least 5.11% and 29.9% higher than the comparison strategies in APV and SR, respectively.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dangerous Action Recognition for Ship Sailing to Limited Resource Environment 有限资源环境下船舶航行危险行为识别

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00050

Shengchao Yuan, Yimin Zhou, Lei Shi, Yongxin Huang

引用次数: 0