Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760831
R. Jaswanthi, E. Amruthatulasi, Ch. Bhavyasree, Ashutosh Satapathy
Calories play an essential role in health aspects that lead to diseases like coronary heart disease, liver disease, cancer, and cholesterol. A study from 2020 reported that globally, overweight adults outnumber underweight individuals by more than 1.9 billion, while obese adults outnumber underweight ones by 650 million. Statistics from India show that abdominal obesity is the most significant risk factor, and it varies from 16.9% to 36.3%. Deep learning is an advanced image processing technology that solves problems and ensures food challenges because deeper networks have a better ability to process many features in an image. In our study, we propose a hybrid framework to predict the calorie content of food items on a plate. This includes three main parts: segmentation to segment the food from the image, image classification for classifying the food items, and calculating the calories present in those food items. A generative adversarial network is used for the segmentation, while a convolutional neural network is used for the classification and calorie estimation. The above models trained on the food images from the UNIMIB 2016 dataset have correctly recognized and estimated the calories of a food item with an accuracy of 95.21%.
{"title":"A Hybrid Network Based on GAN and CNN for Food Segmentation and Calorie Estimation","authors":"R. Jaswanthi, E. Amruthatulasi, Ch. Bhavyasree, Ashutosh Satapathy","doi":"10.1109/ICSCDS53736.2022.9760831","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760831","url":null,"abstract":"Calories play an essential role in health aspects that lead to diseases like coronary heart disease, liver disease, cancer, and cholesterol. A study from 2020 reported that globally, overweight adults outnumber underweight individuals by more than 1.9 billion, while obese adults outnumber underweight ones by 650 million. Statistics from India show that abdominal obesity is the most significant risk factor, and it varies from 16.9% to 36.3%. Deep learning is an advanced image processing technology that solves problems and ensures food challenges because deeper networks have a better ability to process many features in an image. In our study, we propose a hybrid framework to predict the calorie content of food items on a plate. This includes three main parts: segmentation to segment the food from the image, image classification for classifying the food items, and calculating the calories present in those food items. A generative adversarial network is used for the segmentation, while a convolutional neural network is used for the classification and calorie estimation. The above models trained on the food images from the UNIMIB 2016 dataset have correctly recognized and estimated the calories of a food item with an accuracy of 95.21%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127355939","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760802
J. Friska, M. N. Jenefa
Wireless Communication are engaged with large number of antennas for attaining satisfactory data rate. Millimeter Wave Massive Multiple- Input and Multiple-Output (MIMO) has been implemented in Orthogonal Frequency Division Multiplexing (OFDM) in order to achieve the data rate and to enhance the Signal to Noise Ratio (SNR). With the increase in large number of antennas, the systems Spectral efficiency and Energy efficiency are deteriorated, and for an OFDM Massive MIMO various subcarriers are diverse in time and frequency domains. So as to deal with this issue, dynamic channel estimation is very much desired. If Spectral and Energy efficiencies are in good performance, then diversity gain will be achieved. In this paper, the best channel is evaluated by using the suggested system Grey Wolf Optimization (GWO) and by utilizing BICMB - OFDM in an mm-wave massive MIMO systems.
{"title":"Grey Wolf Optimization based BICMB-OFDM for millimeter wave Massive MIMO systems","authors":"J. Friska, M. N. Jenefa","doi":"10.1109/ICSCDS53736.2022.9760802","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760802","url":null,"abstract":"Wireless Communication are engaged with large number of antennas for attaining satisfactory data rate. Millimeter Wave Massive Multiple- Input and Multiple-Output (MIMO) has been implemented in Orthogonal Frequency Division Multiplexing (OFDM) in order to achieve the data rate and to enhance the Signal to Noise Ratio (SNR). With the increase in large number of antennas, the systems Spectral efficiency and Energy efficiency are deteriorated, and for an OFDM Massive MIMO various subcarriers are diverse in time and frequency domains. So as to deal with this issue, dynamic channel estimation is very much desired. If Spectral and Energy efficiencies are in good performance, then diversity gain will be achieved. In this paper, the best channel is evaluated by using the suggested system Grey Wolf Optimization (GWO) and by utilizing BICMB - OFDM in an mm-wave massive MIMO systems.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121573565","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760869
Xiang. Cao
Intelligent capture and analysis of data stream images of rural e-commerce live broadcast platform based on deep stochastic neural network is the focus of this study. In the designed data stream image analytic model, statistical analysis of the encryption algorithm is carried out. By analyzing the histogram of the encrypted image, the correlation of adjacent pixels, and the information entropy, the confusion and diffusion performance of the algorithm and the ability to resist statistical attacks are tested. The key point of FPGA for image processing is that it can perform real-time pipeline operations to achieve the highest real-time performance. Hecne, the hardware optimization combining the deep stochastic neural network is discussed. The application scenario is selected to be rural e-commerce live broadcast platform. Therough the experimental analysis, the performance is validated.
{"title":"Intelligent Capture and Analysis of Data Stream Images of Rural E-Commerce Live Broadcast Platform based on Deep Stochastic Neural Network","authors":"Xiang. Cao","doi":"10.1109/ICSCDS53736.2022.9760869","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760869","url":null,"abstract":"Intelligent capture and analysis of data stream images of rural e-commerce live broadcast platform based on deep stochastic neural network is the focus of this study. In the designed data stream image analytic model, statistical analysis of the encryption algorithm is carried out. By analyzing the histogram of the encrypted image, the correlation of adjacent pixels, and the information entropy, the confusion and diffusion performance of the algorithm and the ability to resist statistical attacks are tested. The key point of FPGA for image processing is that it can perform real-time pipeline operations to achieve the highest real-time performance. Hecne, the hardware optimization combining the deep stochastic neural network is discussed. The application scenario is selected to be rural e-commerce live broadcast platform. Therough the experimental analysis, the performance is validated.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716693","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9761032
Wenpu Wang, Wei-Ting Lin
Based on network mining of root data, the basic features of business English semantics are analyzed, and the application of this theory in business English semantic analysis is discussed. Based on the fourth-generation semantic analysis tool of CQ Pweb, the collocation features of high-frequency business English words were extracted and reduced in multiple directions through research on collocations, class connections, semantic tendency and semantic prosody, and the data volume was compressed to 51.2%. Using the high-precision definition algorithm of root data network mining to reorganize, collocate and parse semantic features, the experimental results show that the effect of business English parsing is increased by 6.7%.
{"title":"Research on Intelligent Parsing of Business English Semantics based on Root Data Network Mining","authors":"Wenpu Wang, Wei-Ting Lin","doi":"10.1109/ICSCDS53736.2022.9761032","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761032","url":null,"abstract":"Based on network mining of root data, the basic features of business English semantics are analyzed, and the application of this theory in business English semantic analysis is discussed. Based on the fourth-generation semantic analysis tool of CQ Pweb, the collocation features of high-frequency business English words were extracted and reduced in multiple directions through research on collocations, class connections, semantic tendency and semantic prosody, and the data volume was compressed to 51.2%. Using the high-precision definition algorithm of root data network mining to reorganize, collocate and parse semantic features, the experimental results show that the effect of business English parsing is increased by 6.7%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550620","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760993
B. R, S. Deepajothi, Prabaharan G, Daniya T, P. Karthikeyan, V. S
The enormous development of information sent through the IoT devices to end-user devices has expanded the significance of creating intrusion detection systems. Intrusion detection system plays a vital role in the smart home, smart city, agriculture, and business organizations. The intruder crate attack and send the data through the IoT sensor device to attack the IoT environment. There is numerous deep learning model is developed and deployed in the IoT environment to detect the intrusion's activity in the IoT environment. This survey paper explores the deep supervised learning model, deep unsupervised learning model, and data set used in the IoT environment for the intrusions detection system. Finally, the open research problem in the intrusion detection system in the IoT environment is presented.
{"title":"Survey on Intrusions Detection System using Deep learning in IoT Environment","authors":"B. R, S. Deepajothi, Prabaharan G, Daniya T, P. Karthikeyan, V. S","doi":"10.1109/ICSCDS53736.2022.9760993","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760993","url":null,"abstract":"The enormous development of information sent through the IoT devices to end-user devices has expanded the significance of creating intrusion detection systems. Intrusion detection system plays a vital role in the smart home, smart city, agriculture, and business organizations. The intruder crate attack and send the data through the IoT sensor device to attack the IoT environment. There is numerous deep learning model is developed and deployed in the IoT environment to detect the intrusion's activity in the IoT environment. This survey paper explores the deep supervised learning model, deep unsupervised learning model, and data set used in the IoT environment for the intrusions detection system. Finally, the open research problem in the intrusion detection system in the IoT environment is presented.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373271","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760867
L. Yang
Based on virtual reality, this paper proposes a robot step length automation planning algorithm based on a linear inverted pendulum model. The algorithm first obtains the position of the center of mass when the support leg switches according to the step length to be achieved; for multi-robot groups, a hierarchical system is adopted Structure, hierarchical structure combines the advantages of centralized structure and distributed structure. In general, there is a master robot to master global information, and locally controlled robots can exchange information with each other. This structure makes group robots suitable for complex and changeable Work under the environment. So that it can reach the required footing position at the right time to switch the supporting feet. The algorithm can make the biped robot change the step length during the movement, which can improve its walking flexibility to 6.7%.
{"title":"Robot Step Length Automatic Control Method based on Virtual Reality Technology","authors":"L. Yang","doi":"10.1109/ICSCDS53736.2022.9760867","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760867","url":null,"abstract":"Based on virtual reality, this paper proposes a robot step length automation planning algorithm based on a linear inverted pendulum model. The algorithm first obtains the position of the center of mass when the support leg switches according to the step length to be achieved; for multi-robot groups, a hierarchical system is adopted Structure, hierarchical structure combines the advantages of centralized structure and distributed structure. In general, there is a master robot to master global information, and locally controlled robots can exchange information with each other. This structure makes group robots suitable for complex and changeable Work under the environment. So that it can reach the required footing position at the right time to switch the supporting feet. The algorithm can make the biped robot change the step length during the movement, which can improve its walking flexibility to 6.7%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116812513","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760812
S. K. Barik, Srikanta Mohapatra, Subhra Debdas
In this paper, a novel architecture, multi-neuron functional link artificial neural network (MNFLANN), has been proposed and its performance in predicting wind energy is compared with the other conventional network models, i.e. ANN, multi-layer perceptrons (MLP) and functional link artificial neural networks (FLANN). The name, i.e. MNFLANN is given as per its structure which consists of multiple neurons unlike the conventional FLANN that consists of only one neuron in the output layer. The real-time wind energy data of October month of recent three years from Sotavento wind farm located in Spain has been taken into consideration to evaluate the performance of MNFLANN. Results show that the mean absolute percentage error (MAPE) during testing is so less, i.e. -1.32% for MNFLANN, compared to other conventional architectures, i.e. -9.47% for ANN, - 8.44% for MLP and 15.19% for FLANN. The proposed MNFLANN architecture effectively handles the nonlinearity in input data compared to other conventional architectures due to its improved structure.
{"title":"Multi-Neuron Functional Link Artificial Neural Network: A Novel Architecture and its Performance for Wind Energy Prediction","authors":"S. K. Barik, Srikanta Mohapatra, Subhra Debdas","doi":"10.1109/ICSCDS53736.2022.9760812","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760812","url":null,"abstract":"In this paper, a novel architecture, multi-neuron functional link artificial neural network (MNFLANN), has been proposed and its performance in predicting wind energy is compared with the other conventional network models, i.e. ANN, multi-layer perceptrons (MLP) and functional link artificial neural networks (FLANN). The name, i.e. MNFLANN is given as per its structure which consists of multiple neurons unlike the conventional FLANN that consists of only one neuron in the output layer. The real-time wind energy data of October month of recent three years from Sotavento wind farm located in Spain has been taken into consideration to evaluate the performance of MNFLANN. Results show that the mean absolute percentage error (MAPE) during testing is so less, i.e. -1.32% for MNFLANN, compared to other conventional architectures, i.e. -9.47% for ANN, - 8.44% for MLP and 15.19% for FLANN. The proposed MNFLANN architecture effectively handles the nonlinearity in input data compared to other conventional architectures due to its improved structure.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407430","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760938
V. Gaur, R. Kumar
Distributed Denial of Service (DDoS) attack is a type of network attack that can be launched from multiple sources to bring the network down. Several detection algorithms have been adopted to diagnose Distributed Denial of Service attacks. In this paper, the authors proposed an ET-RF (Extra Tree-Random Forest) model on CICDDoS2019 dataset to detect DDoS attacks. The system has been tested in two scenarios on CICDDoS2019 dataset. In scenario 1 the performance of different classifiers Random Forest, Decision Tree and KNN (K-Nearest Neighbor) have been evaluated. Analysis using ROC Curve gives 99% accuracy for Random Forest with Extra Tree feature selection on complete dataset. In scenario 2 the authors explored tests with different types of DDoS attacks. Since, all the attacks are analyzed independently and recall, f-1 score and precision close to 99% are achieved using this model.
{"title":"ET-RF based Model for Detection of Distributed Denial of Service Attacks","authors":"V. Gaur, R. Kumar","doi":"10.1109/ICSCDS53736.2022.9760938","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760938","url":null,"abstract":"Distributed Denial of Service (DDoS) attack is a type of network attack that can be launched from multiple sources to bring the network down. Several detection algorithms have been adopted to diagnose Distributed Denial of Service attacks. In this paper, the authors proposed an ET-RF (Extra Tree-Random Forest) model on CICDDoS2019 dataset to detect DDoS attacks. The system has been tested in two scenarios on CICDDoS2019 dataset. In scenario 1 the performance of different classifiers Random Forest, Decision Tree and KNN (K-Nearest Neighbor) have been evaluated. Analysis using ROC Curve gives 99% accuracy for Random Forest with Extra Tree feature selection on complete dataset. In scenario 2 the authors explored tests with different types of DDoS attacks. Since, all the attacks are analyzed independently and recall, f-1 score and precision close to 99% are achieved using this model.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131868787","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760786
S. S, J. S. Raj
With vehicles becoming the primary mode of transportation in almost all parts of the world, it has now become increasingly essential to develop advanced realtime applications that provide users with safety and entertainment regardless of their geographical location by leveraging vehicular communication. The basic communication network structure for building an efficient communication among vehicles has been formed by Vehicular Ad-hoc Networks (VANET). As a subset of Mobile Ad-hoc Networks (MANETs), the recent innovations in Vehicular Ad-hoc Networks have revolutionized the Intelligent Transportation System (ITS) paradigm. On the other hand, due to high mobility, VANET lacks the ability to develop a stable topology, which in turn results in frequent network interruptions. Amongst all the existing challenges, this paper focuses on enhancing the Quality of Service [QoS] of the VANET architectures with an optimal route selection approach - ISFF [Intelligent Swarm based Firefly Fly]. When compared to other traditional approaches, the proposed approach will help the vehicles to obtain a decision on optimal routing path. The proposed approach was simulated based on Packet Delivery Ratio [PDR] and End-to-End Delay parameters and achieved an average PDR of 62.6% and End-to-End Delay of 171.4 sec, which is comparatively higher than the existing method.
{"title":"ISFF Based Optimal Route Selection and QoS Enhancement in VANETs","authors":"S. S, J. S. Raj","doi":"10.1109/ICSCDS53736.2022.9760786","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760786","url":null,"abstract":"With vehicles becoming the primary mode of transportation in almost all parts of the world, it has now become increasingly essential to develop advanced realtime applications that provide users with safety and entertainment regardless of their geographical location by leveraging vehicular communication. The basic communication network structure for building an efficient communication among vehicles has been formed by Vehicular Ad-hoc Networks (VANET). As a subset of Mobile Ad-hoc Networks (MANETs), the recent innovations in Vehicular Ad-hoc Networks have revolutionized the Intelligent Transportation System (ITS) paradigm. On the other hand, due to high mobility, VANET lacks the ability to develop a stable topology, which in turn results in frequent network interruptions. Amongst all the existing challenges, this paper focuses on enhancing the Quality of Service [QoS] of the VANET architectures with an optimal route selection approach - ISFF [Intelligent Swarm based Firefly Fly]. When compared to other traditional approaches, the proposed approach will help the vehicles to obtain a decision on optimal routing path. The proposed approach was simulated based on Packet Delivery Ratio [PDR] and End-to-End Delay parameters and achieved an average PDR of 62.6% and End-to-End Delay of 171.4 sec, which is comparatively higher than the existing method.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131876519","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}
Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760776
Shipu Jin
A formal modeling language MCD for concurrent systems is proposed, and its syntax, semantics and formal definitions are given. MCD uses modules as basic components, and that the detection rules are not perfect, resulting in packets that do not belong to intrusion attacks being misjudged as attacks, respectively. Then the data detection algorithm based on MCD concurrency model protects hidden computer viruses and security threats, and the efficiency is increased by 7.5% Finally, the computer network security protection system is researched based on security modeling.
{"title":"Research on Computer Network Security Framework Based on Concurrent Data Detection and Security Modelling","authors":"Shipu Jin","doi":"10.1109/ICSCDS53736.2022.9760776","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760776","url":null,"abstract":"A formal modeling language MCD for concurrent systems is proposed, and its syntax, semantics and formal definitions are given. MCD uses modules as basic components, and that the detection rules are not perfect, resulting in packets that do not belong to intrusion attacks being misjudged as attacks, respectively. Then the data detection algorithm based on MCD concurrency model protects hidden computer viruses and security threats, and the efficiency is increased by 7.5% Finally, the computer network security protection system is researched based on security modeling.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131901263","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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