Jian Lin, Lin Huang, Tao Zhou, Dongming Xie, Bo Yu
Cloud-native big data services become popular in recent years. Two pillars of these services are identified: the separation architecture of compute and storage, and the application-specific controller mechanism. In terms of storage for big data on the cloud, current practices focus on managing a single on-premise storage cluster or building independent PaaS storage services. This paper focuses on the cloud-native containerized storage. An on-demand provisioning design is proposed, which extends the mainstream storage architecture and supports the provisioning of storage clusters for multi-tenancy in a dynamic manner. Its instance of HDFS-on-Kubernetes is implemented. With the mechanisms of global endpoint provisioning and dynamic volume provisioning, this provisioner enables the creation and management of multiple on-demand storage clusters with full-stack resources in an automated way. It guarantees the native performance of host network and local storage, which has been validated through experiments and production applications. It is also easy to use because of its high-level abstraction and single-point configuration mechanism. The design as well as the provisioner has served real business in industrial scenarios.
{"title":"An On-Demand Cloud-Native Containerized Storage Design and its Practice of HDFS-on-Kubernetes","authors":"Jian Lin, Lin Huang, Tao Zhou, Dongming Xie, Bo Yu","doi":"10.1145/3589845.3589846","DOIUrl":"https://doi.org/10.1145/3589845.3589846","url":null,"abstract":"Cloud-native big data services become popular in recent years. Two pillars of these services are identified: the separation architecture of compute and storage, and the application-specific controller mechanism. In terms of storage for big data on the cloud, current practices focus on managing a single on-premise storage cluster or building independent PaaS storage services. This paper focuses on the cloud-native containerized storage. An on-demand provisioning design is proposed, which extends the mainstream storage architecture and supports the provisioning of storage clusters for multi-tenancy in a dynamic manner. Its instance of HDFS-on-Kubernetes is implemented. With the mechanisms of global endpoint provisioning and dynamic volume provisioning, this provisioner enables the creation and management of multiple on-demand storage clusters with full-stack resources in an automated way. It guarantees the native performance of host network and local storage, which has been validated through experiments and production applications. It is also easy to use because of its high-level abstraction and single-point configuration mechanism. The design as well as the provisioner has served real business in industrial scenarios.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121883807","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}
Aashika Varadharajan, Aishwarya Deshpande, Yuni Xia, S. Fang
Generative Adversarial Network (GAN) is an unsupervised learning technique in performing task such as prediction, classification and clustering. The GAN algorithm can learn the internal representation of data and can act as good features extractor. Training on a dataset of faces, we show convincing evidence that our deep convolutional adversarial pair learnt well and generated new images of fake human faces that look as realistic as possible. The unsupervised clustering model divides and groups faces based on their characteristics. In this paper, we present DCGAN (Deep Convolutional Generative Adversarial Network) in performing classification and clustering.
{"title":"Efficient Face Generation and Clustering Using Generative Adversarial Networks","authors":"Aashika Varadharajan, Aishwarya Deshpande, Yuni Xia, S. Fang","doi":"10.1145/3589845.3589853","DOIUrl":"https://doi.org/10.1145/3589845.3589853","url":null,"abstract":"Generative Adversarial Network (GAN) is an unsupervised learning technique in performing task such as prediction, classification and clustering. The GAN algorithm can learn the internal representation of data and can act as good features extractor. Training on a dataset of faces, we show convincing evidence that our deep convolutional adversarial pair learnt well and generated new images of fake human faces that look as realistic as possible. The unsupervised clustering model divides and groups faces based on their characteristics. In this paper, we present DCGAN (Deep Convolutional Generative Adversarial Network) in performing classification and clustering.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131706293","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}
In indoor scenes, VSLAM-based mobile robots face the challenges of poor closed-loop detection and low localization accuracy. Based on a monocular camera, we propose a closed-loop detection algorithm based on an improved real-time updating bag-of-words model. By extracting feature descriptors of online images and fusing them with loaded offline words, a fused bag of words related to the mobile robot application scenario is generated, which changes with the robot application scenario. In this paper, the improved bag-of-words and the original bag-of-words are combined with ORB-SLAM3 for closed-loop detection experiments, respectively. The experimental results show that the error between the predicted trajectory and the real trajectory of the ORB-SLAM3 system combined with the improved bag-of-words model is significantly reduced, and the robustness of the system is also improved, resulting in a certain improvement in the closed-loop detection capability of the small mobile robot.
{"title":"A Closed-loop Detection Algorithm for Online Updating of Bag-Of-Words Model","authors":"Xiuqiang Shen, Lihang Chen, Zhuhua Hu, Yuexin Fu, Hao Qi, Yunfeng Xiang, Jiaqi Wu","doi":"10.1145/3589845.3589847","DOIUrl":"https://doi.org/10.1145/3589845.3589847","url":null,"abstract":"In indoor scenes, VSLAM-based mobile robots face the challenges of poor closed-loop detection and low localization accuracy. Based on a monocular camera, we propose a closed-loop detection algorithm based on an improved real-time updating bag-of-words model. By extracting feature descriptors of online images and fusing them with loaded offline words, a fused bag of words related to the mobile robot application scenario is generated, which changes with the robot application scenario. In this paper, the improved bag-of-words and the original bag-of-words are combined with ORB-SLAM3 for closed-loop detection experiments, respectively. The experimental results show that the error between the predicted trajectory and the real trajectory of the ORB-SLAM3 system combined with the improved bag-of-words model is significantly reduced, and the robustness of the system is also improved, resulting in a certain improvement in the closed-loop detection capability of the small mobile robot.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133974108","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}
Rui Mu, Yasuhiro Mimura, M. Yamazaki, Yusuke Suzuki, Toshiyasu Takakuwa
Due to various changes during the COVID-19 pandemic, special changes of road traffic flow are assumed. Changes of detected road traffic flow (DRTF) compared to that of 2019 under the same conditions in Toyota city are analyzed firstly. Generally, the DRTF decrease. Monthly change rate of the DRTF fluctuated during 2020 in 83.6%∼98.3%, however, they keep relatively stable during 2021 in 88.7%∼93.2%. Change rate of one-day-average DRTF for different weekdays, and for three long holidays also have different trends in 2020 and 2021. Moreover, change rate of one-day-average DRTF for different time of state of emergency declarations (SED) have special characteristics. Regarding the analysis above, a Long Short-Term Memory (LSTM) Model which consider impact of COVID-19 is developed to predict one-day DRTF. Sequence-to-sequence (StS) model is introduced, one-to-one and many-to-one models is designed separately to do the prediction. The results demonstrate that MAE, MAPE, and RMSE of one-to-one model is better than many-to-one model, although relationship of DRTF in one week is considered in many-to-one model.
{"title":"Prediction of road traffic flow applying Long Short-Term Memory Model considering impact of COVID-19 in Toyota City","authors":"Rui Mu, Yasuhiro Mimura, M. Yamazaki, Yusuke Suzuki, Toshiyasu Takakuwa","doi":"10.1145/3589845.3589855","DOIUrl":"https://doi.org/10.1145/3589845.3589855","url":null,"abstract":"Due to various changes during the COVID-19 pandemic, special changes of road traffic flow are assumed. Changes of detected road traffic flow (DRTF) compared to that of 2019 under the same conditions in Toyota city are analyzed firstly. Generally, the DRTF decrease. Monthly change rate of the DRTF fluctuated during 2020 in 83.6%∼98.3%, however, they keep relatively stable during 2021 in 88.7%∼93.2%. Change rate of one-day-average DRTF for different weekdays, and for three long holidays also have different trends in 2020 and 2021. Moreover, change rate of one-day-average DRTF for different time of state of emergency declarations (SED) have special characteristics. Regarding the analysis above, a Long Short-Term Memory (LSTM) Model which consider impact of COVID-19 is developed to predict one-day DRTF. Sequence-to-sequence (StS) model is introduced, one-to-one and many-to-one models is designed separately to do the prediction. The results demonstrate that MAE, MAPE, and RMSE of one-to-one model is better than many-to-one model, although relationship of DRTF in one week is considered in many-to-one model.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114969664","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}
The integration development of rural industries can promote the high-quality development of rural commerce, cultural industry and tourism. In this paper, we propose an improved RBF neural network-based rural industry integration prediction method to address the current problem of insufficient accuracy of rural industry integration prediction. Firstly, we use the entropy value method to obtain the influencing factors indexes of rural industry integration, and then use the RBF neural network as the basic prediction model. On the premise that the prediction results of RBF neural network are greatly influenced by the network parameters, this paper innovatively adopts the artificial fish swarm algorithm improved by Lévy flight to optimize the RBF parameters, thus finally obtaining the prediction model of rural industry integration based on the improved RBF neural network. Finally, the integration degree evaluation indexes obtained by entropy weighting method are input into the prediction model for experiments. The experimental results show that the rural industry integration prediction method proposed in this paper can predict the rural industry integration degree more accurately and has better computing efficiency, which is helpful for the study of digital transformation of rural industry in the context of digital economy.
{"title":"Research on the Prediction Method of Rural Industry Integration Based on Improved RBF Neural Network Model","authors":"Jianhua Zhao, Tao Yan","doi":"10.1145/3589845.3589856","DOIUrl":"https://doi.org/10.1145/3589845.3589856","url":null,"abstract":"The integration development of rural industries can promote the high-quality development of rural commerce, cultural industry and tourism. In this paper, we propose an improved RBF neural network-based rural industry integration prediction method to address the current problem of insufficient accuracy of rural industry integration prediction. Firstly, we use the entropy value method to obtain the influencing factors indexes of rural industry integration, and then use the RBF neural network as the basic prediction model. On the premise that the prediction results of RBF neural network are greatly influenced by the network parameters, this paper innovatively adopts the artificial fish swarm algorithm improved by Lévy flight to optimize the RBF parameters, thus finally obtaining the prediction model of rural industry integration based on the improved RBF neural network. Finally, the integration degree evaluation indexes obtained by entropy weighting method are input into the prediction model for experiments. The experimental results show that the rural industry integration prediction method proposed in this paper can predict the rural industry integration degree more accurately and has better computing efficiency, which is helpful for the study of digital transformation of rural industry in the context of digital economy.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122551094","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}
Automatic car damage assessment is an intriguing problem in the practice of artificial intelligence. With the help of car damage assessment algorithms, automobile insurance companies, car rental, and car-sharing businesses could attain automatic auxiliary loss assessment or identify the insurance fraud problem. It would save amounts of time and money to replace the manual examination process in traditional car damage assessment with computer-aided damage examination. In this paper, we introduce an anchor-free object detection method for auxiliary car damage assessment adopting a car damage dataset. We use the coordinate attention mechanism and focal loss design to get higher accuracy with fewer parameters and GFLOPs compared to the baseline model. On the test set, our model gets 59.2% AP50 and 39.9% AP, outperforming the baseline model by 5.5%, and 8.7%, respectively. And the method reduces parameters by about 1.42M and GFLOPs by about 1.18.
{"title":"An Anchor Free Car Damage Detection Method","authors":"Haoran Jin, Xinkuang Wang, Z. Wu","doi":"10.1145/3589845.3589848","DOIUrl":"https://doi.org/10.1145/3589845.3589848","url":null,"abstract":"Automatic car damage assessment is an intriguing problem in the practice of artificial intelligence. With the help of car damage assessment algorithms, automobile insurance companies, car rental, and car-sharing businesses could attain automatic auxiliary loss assessment or identify the insurance fraud problem. It would save amounts of time and money to replace the manual examination process in traditional car damage assessment with computer-aided damage examination. In this paper, we introduce an anchor-free object detection method for auxiliary car damage assessment adopting a car damage dataset. We use the coordinate attention mechanism and focal loss design to get higher accuracy with fewer parameters and GFLOPs compared to the baseline model. On the test set, our model gets 59.2% AP50 and 39.9% AP, outperforming the baseline model by 5.5%, and 8.7%, respectively. And the method reduces parameters by about 1.42M and GFLOPs by about 1.18.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614177","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}
Ehtsham Rasool, Muhammad Junaid Anwar, Bilawal Shaker, Muhammad Harris Hashmi, K. Rehman, Yousaf Seed
Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women`s at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies.
{"title":"Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches","authors":"Ehtsham Rasool, Muhammad Junaid Anwar, Bilawal Shaker, Muhammad Harris Hashmi, K. Rehman, Yousaf Seed","doi":"10.1145/3589845.3589849","DOIUrl":"https://doi.org/10.1145/3589845.3589849","url":null,"abstract":"Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women`s at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124985791","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}
Xiaogang Zhao, Siwei Dong, Yiwei Dang, Hai Shen, Jun Hou, Ge Li
Cultural tourism, as one of the most popular forms of tourism, has recently witnessed a remarkable development. However, rapid development of cultural tourism has brought fierce competition. In order to increase the attractiveness of scenic spots, it is very necessary for tourism enterprises to accurately understand cultural tourists‘ preference. This paper proposes a systematic method for profiling cultural tourists based on user generated big data. In this method, topic model, sentiment analysis and clustering algorithms are combined to cluster tourists, and then multinomial logistic regression model are applied to match tourists’ basic attributes. Calculation results show that cultural tourists are mainly divided into four groups with different characteristics. According to the characteristics of each group, suggestions for improving the management of scenic spots are put forward.
{"title":"Profiling Cultural Tourists by Using User Generated Big Data from Online Travel Agencies","authors":"Xiaogang Zhao, Siwei Dong, Yiwei Dang, Hai Shen, Jun Hou, Ge Li","doi":"10.1145/3589845.3589850","DOIUrl":"https://doi.org/10.1145/3589845.3589850","url":null,"abstract":"Cultural tourism, as one of the most popular forms of tourism, has recently witnessed a remarkable development. However, rapid development of cultural tourism has brought fierce competition. In order to increase the attractiveness of scenic spots, it is very necessary for tourism enterprises to accurately understand cultural tourists‘ preference. This paper proposes a systematic method for profiling cultural tourists based on user generated big data. In this method, topic model, sentiment analysis and clustering algorithms are combined to cluster tourists, and then multinomial logistic regression model are applied to match tourists’ basic attributes. Calculation results show that cultural tourists are mainly divided into four groups with different characteristics. According to the characteristics of each group, suggestions for improving the management of scenic spots are put forward.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125820045","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}
In view of the nonlinear and nonstationary characteristics of atmospheric PM2.5 mass concentration, in order to improve the prediction accuracy of PM2.5 mass concentration. Herein, we use the "decomposition and integration" prediction method, established a mixed prediction model of local average decomposition (LOCAL Mean Decomposition, LMD) and minimum daily support vector machines (LSSVM). Firstly, LMD was used to decompose the original time series of PM2.5 mass concentration, and several relatively stationary components with different time scales are obtained, then the SVR algorithm is used to predict each component separately, at last, obtaining the sum of the predictive values of each component as the prediction result of the original PM2.5 quality concentration. We select the PM2.5 daily average mass concentration from March 1, 2014 to April 30, 2015 from the Wanliu Monitoring Station in Haidian District, Beijing. The PM2.5 daily the average mass concentration is used as an experimental sample set. The results of the research were compared with EEMD-LSSVM, EMD-LSSVM and a single LSSVM model, indicating that the LMD-LSSVM model effectively improves the predictive accuracy of PM2.5 quality concentration.
针对大气PM2.5质量浓度的非线性和非平稳特征，为了提高PM2.5质量浓度的预测精度。本文采用“分解与积分”预测方法，建立了局部平均分解(local Mean decomposition, LMD)与最小日支持向量机(minimum daily support vector machines, LSSVM)混合预测模型。首先利用LMD对PM2.5质量浓度原始时间序列进行分解，得到几个具有不同时间尺度的相对平稳分量，然后利用SVR算法对各分量分别进行预测，最后得到各分量预测值之和作为原始PM2.5质量浓度的预测结果。选取北京市海淀区万柳监测站2014年3月1日至2015年4月30日PM2.5日平均质量浓度。采用PM2.5日平均质量浓度作为实验样本集。将研究结果与EEMD-LSSVM、EMD-LSSVM和单个LSSVM模型进行比较，结果表明LMD-LSSVM模型有效提高了PM2.5质量浓度的预测精度。
{"title":"PM2.5 Quality Concentration Prediction Based on Local Average Decomposition and Support Vector Regression","authors":"Yuan-Hang Ye, Wen-Bo Wang","doi":"10.1145/3589845.3589857","DOIUrl":"https://doi.org/10.1145/3589845.3589857","url":null,"abstract":"In view of the nonlinear and nonstationary characteristics of atmospheric PM2.5 mass concentration, in order to improve the prediction accuracy of PM2.5 mass concentration. Herein, we use the \"decomposition and integration\" prediction method, established a mixed prediction model of local average decomposition (LOCAL Mean Decomposition, LMD) and minimum daily support vector machines (LSSVM). Firstly, LMD was used to decompose the original time series of PM2.5 mass concentration, and several relatively stationary components with different time scales are obtained, then the SVR algorithm is used to predict each component separately, at last, obtaining the sum of the predictive values of each component as the prediction result of the original PM2.5 quality concentration. We select the PM2.5 daily average mass concentration from March 1, 2014 to April 30, 2015 from the Wanliu Monitoring Station in Haidian District, Beijing. The PM2.5 daily the average mass concentration is used as an experimental sample set. The results of the research were compared with EEMD-LSSVM, EMD-LSSVM and a single LSSVM model, indicating that the LMD-LSSVM model effectively improves the predictive accuracy of PM2.5 quality concentration.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128291512","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}
The normal operation of industrial control system (ICS) is the fundamental to ensure the stable production of industry. However, the existence of loopholes in ICS seriously threatens the normal operation of ICS. Fuzzy testing technology is one of the important technical means to find undisclosed vulnerabilities. This paper is based on the peach framework. Firstly, this paper excavates the vulnerabilities of HTTP protocol, and then this method is applied to the 0xf0 function code of industrial control S7 protocol. The results show that this method is effective in the vulnerability discovery of industrial control protocol.
{"title":"Security Analysis of Industrial Control S7 Protocol based on Peach","authors":"Quanjiang Shen, Liangliang Wang, Lei Zhang, Binbin Wang, Changjiang Liu, Ju-Wei Sha","doi":"10.1145/3589845.3589851","DOIUrl":"https://doi.org/10.1145/3589845.3589851","url":null,"abstract":"The normal operation of industrial control system (ICS) is the fundamental to ensure the stable production of industry. However, the existence of loopholes in ICS seriously threatens the normal operation of ICS. Fuzzy testing technology is one of the important technical means to find undisclosed vulnerabilities. This paper is based on the peach framework. Firstly, this paper excavates the vulnerabilities of HTTP protocol, and then this method is applied to the 0xf0 function code of industrial control S7 protocol. The results show that this method is effective in the vulnerability discovery of industrial control protocol.","PeriodicalId":302027,"journal":{"name":"Proceedings of the 2023 9th International Conference on Computing and Data Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121784778","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}
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