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

Detection of DDoS Attack on Smart Home Infrastructure Using Artificial Intelligence Models 基于人工智能模型的智能家居基础设施DDoS攻击检测

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00014

Thejavathy Raja, Z. Ezziane, Jun-zhong He, Xiaoqi Ma, Asmau Wali-Zubai Kazaure

{"title":"Detection of DDoS Attack on Smart Home Infrastructure Using Artificial Intelligence Models","authors":"Thejavathy Raja, Z. Ezziane, Jun-zhong He, Xiaoqi Ma, Asmau Wali-Zubai Kazaure","doi":"10.1109/CyberC55534.2022.00014","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00014","url":null,"abstract":"The whole web world is concerned and constantly threatened by security intrusion. From the topmost corporate companies to the recently established start-ups, every company focuses on their network, system, and information security as it is the core of any company. Even a simple small security breach can cause a considerable loss to the company and compromises the CIA Triad (Confidentiality, Integrity, and Availability). Security concerns and hacking activities such as Distributed Denial of Service (DDoS) attacks are also experienced within home networks which could be saturated reaching a crashing point. This work focuses on using Artificial Intelligence (AI) and identifying suitable models to train, identify, and detect DDoS attacks. In addition, it aims to implement on smart home datasets and find the best model from those which performs with a high accuracy rate on the smart home dataset. The novelty of this project is identifying one best AI model among many of the existing models that works best on smart home datasets and in identifying and detecting DDoS attacks.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"79 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":"114510534","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

Comparison of ARIMA Model and GM(1,1) Model in Passenger Flow Prediction of Sanya Airport ARIMA模型与GM(1,1)模型在三亚机场客流预测中的比较

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00059

Yuanye Li, Zhao Wen, Hai-yun Han, Zhipeng Ou, L. Xia

引用次数: 1

Point cloud objective recognition method combining SHOT features and ESF features 结合SHOT特征和ESF特征的点云目标识别方法

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00052

Junfeng Ding, Hao Chen, Jian Zhou, Deyong Wu, Xuan Chen, Lei Wang

引用次数: 0

Design and Implementation of Industrial Control Cyber Range System 工业控制网络测距系统的设计与实现

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00034

Xuan Low, Dequan Yang, D. Yang

{"title":"Design and Implementation of Industrial Control Cyber Range System","authors":"Xuan Low, Dequan Yang, D. Yang","doi":"10.1109/CyberC55534.2022.00034","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00034","url":null,"abstract":"In the 21st century, world-leading industries are under the accelerated development of digital transformation. Along with information and data resources becoming more transparent on the Internet, many new network technologies were introduced, but cyber-attack also became a severe problem in cyberspace. Over time, industrial control networks are also forced to join the nodes of the Internet. Therefore, cybersecurity is much more complicated than before, and suffering risk of browsing unknown websites also increases. To practice defenses against cyber-attack effectively, Cyber Range is the best platform to emulate all cyber-attacks and defenses. This article will use VMware virtual machine emulation technology, research cyber range systems under industrial control network architecture, and design and implement an industrial control cyber range system. Using the industrial cyber range to perform vulnerability analyses and exploits on web servers, web applications, and operating systems. The result demonstrates the consequences of the vulnerability attack and raises awareness of cyber security among government, enterprises, education, and other related fields, improving the practical ability to defend against cybersecurity threats.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"19 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":"115193774","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 Privacy-Preserving Blockchain-based Energy Supply Chain System Supporting Supervision 基于区块链的支持监管的保护隐私的能源供应链系统

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00021

Zhihu Li, Bing Zhao, Hongxia Guo, Feng Zhai, Lin Li

{"title":"A Privacy-Preserving Blockchain-based Energy Supply Chain System Supporting Supervision","authors":"Zhihu Li, Bing Zhao, Hongxia Guo, Feng Zhai, Lin Li","doi":"10.1109/CyberC55534.2022.00021","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00021","url":null,"abstract":"With the continuous development of science and technology, more and more energy has poured into human life and production activities. The energy supply chain is responsible for managing the entire life cycle of energy, but there are still some problems in the current energy supply chain. On the one hand, energy data lacks an effective sharing mechanism, and on the other hand, the privacy of user data cannot be guaranteed. In addition, different entities in the current energy supply chain system manage their data, and there will be data barriers, so it is difficult to effectively supervise and track energy data. Based on the consortium blockchain, this paper proposes an energy management system to provide a credible data collaboration environment for each entity in the energy supply chain. Data encryption ensures the privacy of data, better realizes the sharing of energy data, and breaks the data barriers between participants. The system uses smart contracts to set a unified data operation specification, and at the same time designs an energy management mechanism that is friendly to regulators, so as to better realize the supervision of the whole process of energy supply. Experiments show that the proposed system is practical.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"25 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":"114488861","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 Task Allocation Method in Edge Computing Based on Multi-Objective Optimization 基于多目标优化的边缘计算任务分配方法

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/cyberc55534.2022.00048

Yang Xiao

{"title":"A Task Allocation Method in Edge Computing Based on Multi-Objective Optimization","authors":"Yang Xiao","doi":"10.1109/cyberc55534.2022.00048","DOIUrl":"https://doi.org/10.1109/cyberc55534.2022.00048","url":null,"abstract":"Edge computing has been widely used in many scenarios because it is able to improve the performance of cloud computing. With the feature of distribution in edge computing, computing tasks can be allocated to edge computing system for reducing the workload of cloud centers. And to compute these tasks in edge nodes can make some aggregation and calculation process happen close to users. Therefore, some transmission time can be saved when data from edge nodes are sent to users compared with that when data are sent from cloud centers. And it is important to find appropriate task allocation strategies because of less computing resources in edge devices. In this work, a task allocation method AWHA is put up for allocating tasks to edge nodes. It optimizes total time cost, computing resource and storage utilization for computing these tasks in edge nodes. Then, for the scenario where the computation results of each edge node need to be aggregated and do further calculation, a result aggregation strategy FDFA is put up for optimizing the aggregation process of results by allocating all the calculation process to each edge computing node. The experiment results show that AWHA and FDFA methods have optimizing ability.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"47 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":"123689057","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 Fusion Network for Non-Uniform Deblurring* 一种非均匀去模糊的融合网络*

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00046

Qi Qing

{"title":"A Fusion Network for Non-Uniform Deblurring*","authors":"Qi Qing","doi":"10.1109/CyberC55534.2022.00046","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00046","url":null,"abstract":"In the field of computer vision, non-uniform image deblurring is a crucial and difficult task. By learning features from receptive fields, existing image deblurring algorithms have made advanced progress. However, non-local feature representations, which depict the global data distribution of blurry images are not taken into account. In this paper, we investigate non-uniform task by integrating local and non-local features. Specifically, we develop a DRDDBU (Dense-in-Residual Dense Dilation Blocks Unit) and a SAB (Scale Attention Block) to implement local and non-local, respectively. DRDDBU has the virtue of dense connections embodied in locally dense blocks and globally dense connections, which reuses and enhances all intermediate features. SAB is developed to preserve significant and suppress irrelevant responses for generating latent images. In addition, multiple loss functions are proposed to enhance network training and encourage convergence. Subjective and objective comparison experiments on various datasets are done to illustrate the efficiency of the suggested strategy. On synthetic datasets and real images, our non-uniform deblurring method outperforms state-of-the-art (SOTA) methods.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"27 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":"131971204","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

MDAEN: Multi-Dimensional Attention-based Ensemble Network in Deep Reinforcement Learning Framework for Portfolio Management 基于多维关注的组合管理深度强化学习框架集成网络

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00031

Ruiyu Zhang, Xiaotian Ren, Fengchen Gu, Angelos Stefanidis, Ruoyu Sun, Jionglong Su

{"title":"MDAEN: Multi-Dimensional Attention-based Ensemble Network in Deep Reinforcement Learning Framework for Portfolio Management","authors":"Ruiyu Zhang, Xiaotian Ren, Fengchen Gu, Angelos Stefanidis, Ruoyu Sun, Jionglong Su","doi":"10.1109/CyberC55534.2022.00031","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00031","url":null,"abstract":"Reinforcement Learning algorithms are widely applied in many diverse fields, including portfolio management. Ensemble of Identical Independent Evaluators (EIIE) framework proposed by Jiang et al. achieved portfolio management based on their deep reinforcement learning algorithm. In the implementation of EIIE framework, a neural network such as the Convolutional Neural Network is applied as the policy network, to uncover more patterns in the data. However, this network typology is inefficient due to its simple structure. To overcome the shortcoming of EIIE framework, this paper introduces a novel algorithm, the Multi-Dimensional Attention-based Ensemble Network (MDAEN) strategy, which consists of a features-attention module and an assets-attention module. The MDAEN applies different types of attention mechanisms to extract information from the assets. Having adopted the reinforcement learning framework from Jiang et al., the agent is able to process transactions through MDAEN in a market. In our portfolio establishment, Bitcoin together with eleven other cryptocurrencies is selected to validate the performance of MDAEN against seven traditional portfolio strategies and EIIE. The experimental result demonstrates the efficacy of our strategy outperforming all other strategies by at least 35% in profitability and at least 30% in Sharpe Ratio.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"14 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":"121232947","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

Multi-task Assignment Research for Heterogeneous UAVs based on Improved Simulated Annealing Particle Swarm Optimization Algorithm 基于改进模拟退火粒子群优化算法的异构无人机多任务分配研究

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00054

Jie Zhang, Pengcheng Wen, Ai Xiong

引用次数: 0

Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor 基于代码张量生成对抗网络的柔性Android恶意软件检测模型

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00015

Zhao Yang, Fengyang Deng, Linxi Han

{"title":"Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor","authors":"Zhao Yang, Fengyang Deng, Linxi Han","doi":"10.1109/CyberC55534.2022.00015","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00015","url":null,"abstract":"The behavior of malware threats is gradually increasing, heightened the need for malware detection. However, existing malware detection methods only target at the existing malicious samples, the detection of fresh malicious code and variants of malicious code is limited. In this paper, we propose a novel scheme that detects malware and its variants efficiently. Based on the idea of the generative adversarial networks (GANs), we obtain the ‘true’ sample distribution that satisfies the characteristics of the real malware, use them to deceive the discriminator, thus achieve the defense against malicious code attacks and improve malware detection. Firstly, a new Android malware APK to image texture feature extraction segmentation method is proposed, which is called segment self-growing texture segmentation algorithm. Secondly, tensor singular value decomposition (tSVD) based on the low-tubal rank transforms malicious features with different sizes into a fixed third-order tensor uniformly, which is entered into the neural network for training and learning. Finally, a flexible Android malware detection model based on GANs with code tensor (MTFD-GANs) is proposed. Experiments show that the proposed model can generally surpass the traditional malware detection model, with a maximum improvement efficiency of 41.6%. At the same time, the newly generated samples of the GANs generator greatly enrich the sample diversity. And retraining malware detector can effectively improve the detection efficiency and robustness of traditional models.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 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":"131785628","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