2022 RIVF International Conference on Computing and Communication Technologies (RIVF)最新文献_第10页

A Method for Automated Test Data Generation for Units using Classes of Qt Framework in C++ Projects c++项目中使用Qt框架类自动生成单元测试数据的方法

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013869

Thu Anh Bui, Lam Nguyen Tung, Hoang-Viet Tran, Pham Ngoc Hung

引用次数: 1

Low-Resource Speech Recognition Based on Transfer Learning 基于迁移学习的低资源语音识别

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013881

Wei-Hong Tsai, P. Thi, Tzu-Chiang Tai, Chien-Lin Huang, Jia-Ching Wang

引用次数: 0

Adversarial AutoEncoder and Generative Adversarial Networks for Semi-Supervised Learning Intrusion Detection System 半监督学习入侵检测系统的对抗自编码器和生成对抗网络

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013926

Ho Huy Thai, Nguyen Duc Hieu, N. V. Tho, Hien Do Hoang, Phan The Duy, V. Pham

{"title":"Adversarial AutoEncoder and Generative Adversarial Networks for Semi-Supervised Learning Intrusion Detection System","authors":"Ho Huy Thai, Nguyen Duc Hieu, N. V. Tho, Hien Do Hoang, Phan The Duy, V. Pham","doi":"10.1109/RIVF55975.2022.10013926","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013926","url":null,"abstract":"As one of the defensive solutions against cyberattacks, an Intrusion Detection System (IDS) plays an important role in observing the network state and alerting suspicious actions that can break down the system. There are many attempts of adopting Machine Learning (ML) in IDS to achieve high performance in intrusion detection. However, all of them necessitate a large amount of labeled data. In addition, labeling attack data is a time-consuming and expensive human-labor operation, it makes existing ML methods difficult to deploy in a new system or yields lower results due to a lack of labels on pre-trained data. To address these issues, we propose a semi-supervised IDS model that leverages Generative Adversarial Networks (GANs) and Adversarial AutoEncoder (AAE), called a semi-supervised adversarial autoencoder (SAAE). Our SAAE experimental results on two public datasets for benchmarking ML-based IDS, including NF-CSE-CIC-IDS2018 and NF-UNSW-NB15, demonstrate the effectiveness of AAE and GAN in case of using only a small number of labeled data. In particular, our approach outperforms other ML methods with the highest detection rates in spite of the scarcity of labeled data for model training, even with only 1% labeled data.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126692075","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

Empirical Study of Cryptocurrency Prices Using Linear Regression Methods 基于线性回归方法的加密货币价格实证研究

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013790

L. Tran, Son Thanh Le, Ha Manh Tran

引用次数: 1

Improving Intent Detection and Slot Filling for Vietnamese 改进的意图检测和越南语槽填充

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013846

Thien Nguyen, Xuan-Do Dao, Quoc-Quan Chu, Kiem-Hieu Nguyen

引用次数: 0

Leveraging Deep Reinforcement Learning for Automating Penetration Testing in Reconnaissance and Exploitation Phase 利用深度强化学习实现侦察和开发阶段的自动化渗透测试

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013801

Le Van Hoang, Nguyen Xuan Nhu, To Trong Nghia, N. H. Quyen, V. Pham, Phan The Duy

{"title":"Leveraging Deep Reinforcement Learning for Automating Penetration Testing in Reconnaissance and Exploitation Phase","authors":"Le Van Hoang, Nguyen Xuan Nhu, To Trong Nghia, N. H. Quyen, V. Pham, Phan The Duy","doi":"10.1109/RIVF55975.2022.10013801","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013801","url":null,"abstract":"Penetration testing is one of the most common methods for assessing the security of a system, application, or network. Although there are different support tools with great efficiency in this field, penetration testing is done mostly manually and relies heavily on the experience of the ethical hackers who are doing it, known as pentesters. This paper presents an automated penetration testing approach that leverages deep reinforcement learning (RL) to automate the penetration testing process, including the reconnaissance and exploitation phases. More specifically, the RL agent is trained with the A3C model to gain experience choosing an exact payload to exploit available vulnerabilities. Ad-ditionally, our RL-based pentesting tool has three main functions: information gathering, vulnerability exploitation, and reporting. The performance of this approach is benchmarked against real-world vulnerabilities in our experimental environments. After training with environmental settings, the RL agent can assist pentesters in quickly identifying vulnerabilities in their own servers. The RL-based approach can mitigate the problems of labor costs and hunger data for automating penetration testing in the system by learning how to execute exploits on its own. The more pentesters who use this tool, the more accurate the pentesting results will be. With outstanding results, this method proves that it can accumulate learning results from previous environments to successfully exploit vulnerabilities for the next exploit in another environment on the first try.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122676455","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 Method for Flexible Definition and Automatic Implementation of Laboratory Environment in Online Training Platforms 在线培训平台实验室环境的柔性定义与自动化实现方法

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013872

Do Thi Thu Hien, Hien Do Hoang, Phan The Duy, Do Thi Huong Lan, V. Pham

{"title":"A Method for Flexible Definition and Automatic Implementation of Laboratory Environment in Online Training Platforms","authors":"Do Thi Thu Hien, Hien Do Hoang, Phan The Duy, Do Thi Huong Lan, V. Pham","doi":"10.1109/RIVF55975.2022.10013872","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013872","url":null,"abstract":"Cloud infrastructure enables individuals, organizations, and enterprises to offer scalable and elastic resources to support business operations remotely. The demand for digital transformation encourages communities and technical professionals to adopt cloud computing and automation platforms for facilitating their resource capacity, including operating systems, networks, and applications. Of cloud-based applications for social good, virtual education platforms play an important role to re-duce the cost and effort for trainees and trainers during practical courses, especially in the context of pandemics such as Covid-19. Nonetheless, the task of setting up practical environments with virtual machines, network elements, and software programs is the burden of the system that hosts many training courses with numerous trainees or resources. Hence, this research provides the mechanism for defining and automatically implementing the hands-on laboratory environments for information technology (IT) training. Specifically, we design and implement a concurrent scheme and local repository for deploying multiple environments with high performance in large virtual classrooms. The total time to finish environmental settings for learners is kept stable to meet the satisfaction of users in case of the remarkable growth in the number of environments and trainees.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123942636","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

Proposing XLSR Multilingual Model for Vietnamese Language Recognition 越南语识别的XLSR多语言模型

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013877

P. N. Huu, Sy-Tuyen Ho, Chau Nguyen Le Bao, M. Anh, Luong Nguyen Thien, Hieu Nguyen Duc, Minh-Trien Pham, Dat Vu Tien, Q. Minh

引用次数: 0

Protection of People on Ground Vehicles Moving in an Inclined Plane Using the Rotary Stewart Platform 利用旋转Stewart平台保护斜面移动的地面车辆上的人员

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013865

Phung Cong Phi Khanh, K. Dang, P. Yen, Bui Thanh Tung, Nguyen Thi Thu, Duc-Tan Tran

引用次数: 1

Lightweight Model Using Graph Neural Networks for Air Quality Impact Assessment on Human Health 基于图神经网络的空气质量对人体健康影响评价轻量化模型

2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013826

Tang Nguyen-Tan, Quan Le-Trung

{"title":"Lightweight Model Using Graph Neural Networks for Air Quality Impact Assessment on Human Health","authors":"Tang Nguyen-Tan, Quan Le-Trung","doi":"10.1109/RIVF55975.2022.10013826","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013826","url":null,"abstract":"Current air monitoring stations can only tell us the purity or pollution of the air through the Air Quality Index (AQI). Using the Air Quality Index is not enough when we need to monitor the air environment in areas such as factories, industrial parks, and space areas where many people are working. In this article, we propose a lightweight deep learning model to assess the impact of air quality on human health using the following layers: i) The Timestamp Graph Message Passing layer for graph-based data aggregation affecting prediction results; ii) The Timestamp Graph Convolution layer for graph-based feature expansion affecting prediction results; iii) The Temporal Graph Message Passing layer, which is used to aggregate data based on time series graphs; iv) The Temporal Graph Convolution layer for feature extraction based on time series graphs; v) The Simple Artificial Neural Network for graph classification based on data of nodes. The input is four time series containing data on CO, NO2, O3, and PM 2.5 concentrations in the air. The output of the model is one of the following cases: Fresh, Polluted, Headaches, Pneumonia, Convulsions or Nausea, or Death. The results show that our model can be used for early warning of adverse human health impacts due to air pollution, which has achieved an accuracy of up to 98.15% for the samples in the training set and 91.86% when performing the evaluation with the samples in the test set.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131127385","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