Pub Date : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420580
F. Manavi, A. Hamzeh
Ransomware is a type of malware from cryptovirology that perpetually blocks access to a victim’s data unless a ransom is paid. Today, this type of malware has grown dramatically and has targeted the computer systems of some important organizations such as hospitals, banks, and Water Organization. Therefore, early detection of this type of malware is very important. This paper describes a solution to ransomware detection based on executable file headers. Header of the executable file expresses important information about the structure of the program. In other words, the header’s information is a sequence of bytes, and changing it changes the structure of the program file. In the proposed method, using LSTM network, the sequence of bytes that constructs the header is processed and the ransomware samples are separated from the benign samples. The proposed method can detect a ransomware sample with 93.25 accuracy without running the program and using a raw header, so it is suitable for quick detection of suspicious samples.
{"title":"Static Detection of Ransomware Using LSTM Network and PE Header","authors":"F. Manavi, A. Hamzeh","doi":"10.1109/CSICC52343.2021.9420580","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420580","url":null,"abstract":"Ransomware is a type of malware from cryptovirology that perpetually blocks access to a victim’s data unless a ransom is paid. Today, this type of malware has grown dramatically and has targeted the computer systems of some important organizations such as hospitals, banks, and Water Organization. Therefore, early detection of this type of malware is very important. This paper describes a solution to ransomware detection based on executable file headers. Header of the executable file expresses important information about the structure of the program. In other words, the header’s information is a sequence of bytes, and changing it changes the structure of the program file. In the proposed method, using LSTM network, the sequence of bytes that constructs the header is processed and the ransomware samples are separated from the benign samples. The proposed method can detect a ransomware sample with 93.25 accuracy without running the program and using a raw header, so it is suitable for quick detection of suspicious samples.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123833190","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420546
Mohamadreza Bakhtyari, S. Mirzaei
Click-Through Rate (CTR) prediction plays a critical role in online advertisement campaigns and recommendation systems. Most of the state-of-the-art models are based on Factorization Machines and some of these models try to feed mapped field features to a deep learning component for learning users’ interests by modelling feature interactions. Deploying a model for CTR is an online task and should be able to perform well with a limited amount of data and time. While these models are very good at prediction inferences and learning feature interactions, their deep component needs a vast amount of data and time and does not perform well in limited situations.In a recent article, a combination of boosting algorithms with deep factorization machines (XDBoost algorithm) has been proposed. In this paper, we use a boosting algorithm for prediction inference with limited raw data and time. We show that with an appropriate feature engineering and fine parameter tuning for a raw boosting model, we can outperform XDBoost method and get better results. We will use exploratory data analysis to extract the main characteristics of the dataset and eliminate the redundant data. Then, by applying grid search scheme, we select the best values for the hyperparameters of our model.
{"title":"Click-Through Rate Prediction Using Feature Engineered Boosting Algorithms","authors":"Mohamadreza Bakhtyari, S. Mirzaei","doi":"10.1109/CSICC52343.2021.9420546","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420546","url":null,"abstract":"Click-Through Rate (CTR) prediction plays a critical role in online advertisement campaigns and recommendation systems. Most of the state-of-the-art models are based on Factorization Machines and some of these models try to feed mapped field features to a deep learning component for learning users’ interests by modelling feature interactions. Deploying a model for CTR is an online task and should be able to perform well with a limited amount of data and time. While these models are very good at prediction inferences and learning feature interactions, their deep component needs a vast amount of data and time and does not perform well in limited situations.In a recent article, a combination of boosting algorithms with deep factorization machines (XDBoost algorithm) has been proposed. In this paper, we use a boosting algorithm for prediction inference with limited raw data and time. We show that with an appropriate feature engineering and fine parameter tuning for a raw boosting model, we can outperform XDBoost method and get better results. We will use exploratory data analysis to extract the main characteristics of the dataset and eliminate the redundant data. Then, by applying grid search scheme, we select the best values for the hyperparameters of our model.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497037","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420603
M. Imani
A polarimetric synthetic aperture radar (PolSAR) image classification is introduced in this work. The proposed method called as ridge regression-based polarimetric-spatial (RRPS) feature extraction generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The proposed RRPS method with a closed form solution has high performance in PolSAR image classification using small training sets.
{"title":"Polarimetric SAR Classification Using Ridge Regression-Based Polarimetric-Spatial Feature Extraction","authors":"M. Imani","doi":"10.1109/CSICC52343.2021.9420603","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420603","url":null,"abstract":"A polarimetric synthetic aperture radar (PolSAR) image classification is introduced in this work. The proposed method called as ridge regression-based polarimetric-spatial (RRPS) feature extraction generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The proposed RRPS method with a closed form solution has high performance in PolSAR image classification using small training sets.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553091","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420549
Razieh Baradaran, Hossein Amirkhani
One of the main challenges of the machine reading comprehension (MRC) models is their fragile out-of-domain generalization, which makes these models not properly applicable to real-world general-purpose question answering problems. In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generalization in MRC models. In the proposed method, a weight estimation module is used to estimate out-of-domain weights, and an ensemble module aggregate several base models’ predictions based on their weights. The experiments indicate that the proposed method not only improves the final accuracy, but also is robust against domain changes.
{"title":"Zero-Shot Estimation of Base Models’ Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization","authors":"Razieh Baradaran, Hossein Amirkhani","doi":"10.1109/CSICC52343.2021.9420549","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420549","url":null,"abstract":"One of the main challenges of the machine reading comprehension (MRC) models is their fragile out-of-domain generalization, which makes these models not properly applicable to real-world general-purpose question answering problems. In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generalization in MRC models. In the proposed method, a weight estimation module is used to estimate out-of-domain weights, and an ensemble module aggregate several base models’ predictions based on their weights. The experiments indicate that the proposed method not only improves the final accuracy, but also is robust against domain changes.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125207748","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420619
F. Farahani, F. Rezaei
The Internet of things (IoT) is generating a huge amount of data and big data management is of key importance. One of the important applications of IoT is smart meter networks and one of the key issues in establishing smart meter networks is managing the large volume of data sent by the meters. In this paper, we present a data management system implemented for monitoring and managing the data collected from the smart meters and controlling them in a large-scale network. IoT infrastructure with LPWAN (Low Power Wide Area Network) class is considered in this system. Moreover, two methods are proposed to improve the performance in terms of scalability and response time. It is shown that the implemented data management system using the proposed methods achieves significant performance improvement in large scale networks.
{"title":"Implementing a Scalable Data Management System for Collected Data by Smart Meters","authors":"F. Farahani, F. Rezaei","doi":"10.1109/CSICC52343.2021.9420619","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420619","url":null,"abstract":"The Internet of things (IoT) is generating a huge amount of data and big data management is of key importance. One of the important applications of IoT is smart meter networks and one of the key issues in establishing smart meter networks is managing the large volume of data sent by the meters. In this paper, we present a data management system implemented for monitoring and managing the data collected from the smart meters and controlling them in a large-scale network. IoT infrastructure with LPWAN (Low Power Wide Area Network) class is considered in this system. Moreover, two methods are proposed to improve the performance in terms of scalability and response time. It is shown that the implemented data management system using the proposed methods achieves significant performance improvement in large scale networks.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122783190","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420620
Armin Shoughi, M. B. Dowlatshahi
ECG beats have a key role in the reduction of fatality rate arising from cardiovascular diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the important information from patient cardiac conditions to the specialist. However, the accuracy and speed of arrhythmia diagnosis are challenging in ECG classification systems, and the existence of noise, instability nature, and imbalance in heartbeats challenged these systems. Accurate and on-time diagnosis of CVDs is a vital and important factor. So it has a significant effect on the treatment and recovery of patients. In this study, with the aim of accurate diagnosis of CVDs types, according to arrhythmia in ECG heartbeats, we implement an automatic ECG heartbeats classification by using discrete wavelet transformation on db2 mother wavelet and SMOTE oversampling algorithm as pre-processing level, and a classifier that consists of Convolutional neural network and BLSTM network. Then evaluate the proposed system on MIT-BIH imbalanced dataset, according to AAMI standards. The evaluations results show this approach with 50 epoch training achieved 99.78% accuracy for category F, 98.85% accuracy for category N, 99.43% accuracy for category S, 99.49% accuracy for category V, 99.87% accuracy for category Q. The source code is available at https://gitlab.com/arminshoughi/cnnlstmecg-classification. Our proposed classification system can be used as a tool for the automatic diagnosis of arrhythmia for CVDs specialists with the aim of primary screening of patients with heart arrhythmia.
{"title":"A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset","authors":"Armin Shoughi, M. B. Dowlatshahi","doi":"10.1109/CSICC52343.2021.9420620","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420620","url":null,"abstract":"ECG beats have a key role in the reduction of fatality rate arising from cardiovascular diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the important information from patient cardiac conditions to the specialist. However, the accuracy and speed of arrhythmia diagnosis are challenging in ECG classification systems, and the existence of noise, instability nature, and imbalance in heartbeats challenged these systems. Accurate and on-time diagnosis of CVDs is a vital and important factor. So it has a significant effect on the treatment and recovery of patients. In this study, with the aim of accurate diagnosis of CVDs types, according to arrhythmia in ECG heartbeats, we implement an automatic ECG heartbeats classification by using discrete wavelet transformation on db2 mother wavelet and SMOTE oversampling algorithm as pre-processing level, and a classifier that consists of Convolutional neural network and BLSTM network. Then evaluate the proposed system on MIT-BIH imbalanced dataset, according to AAMI standards. The evaluations results show this approach with 50 epoch training achieved 99.78% accuracy for category F, 98.85% accuracy for category N, 99.43% accuracy for category S, 99.49% accuracy for category V, 99.87% accuracy for category Q. The source code is available at https://gitlab.com/arminshoughi/cnnlstmecg-classification. Our proposed classification system can be used as a tool for the automatic diagnosis of arrhythmia for CVDs specialists with the aim of primary screening of patients with heart arrhythmia.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"54 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131875044","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420562
Davod Karimpour, M. Z. Chahooki, Ali Hashemi
Over the past decade, social networks and messengers have found a special place in the creation and development of businesses. User recommendation is a very important feature in social networks that has attracted the attention of many users to these environments. Using this system in an instant messenger environment is very useful. Telegram is a cloud-based messenger with more than 400 million monthly active users. Telegram is used as a social network in Iran, but does not offer the most widely used features of social networks, such as recommending users. This feature is important for marketers to find target audience. This paper presents a hybrid filtering-based algorithm to recommend Telegram users. This method combines the membership graph of users with the profile of groups. The membership graph, models users based on their membership in groups. Also, the profile of each group includes the name and description of the group. We have created a bag of words for each group based on natural language processing methods to combine it with the membership graph. After combination process, users are recommended based on the list of groups obtained. The data used in this study is the information of more than 120 million users and 900,000 supergroups in Telegram. This data is obtained through Telegram API by Idekav system. The evaluation of the proposed method has been done separately on two categories of specialized supergroups. Each category includes 25 specialized supergroups in Telegram. Selected supergroups for evaluation have between 2,000 and 10,000 members. Experimental results show the integrity of the model and error reduction in RMSE.
{"title":"User recommendation based on Hybrid filtering in Telegram messenger","authors":"Davod Karimpour, M. Z. Chahooki, Ali Hashemi","doi":"10.1109/CSICC52343.2021.9420562","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420562","url":null,"abstract":"Over the past decade, social networks and messengers have found a special place in the creation and development of businesses. User recommendation is a very important feature in social networks that has attracted the attention of many users to these environments. Using this system in an instant messenger environment is very useful. Telegram is a cloud-based messenger with more than 400 million monthly active users. Telegram is used as a social network in Iran, but does not offer the most widely used features of social networks, such as recommending users. This feature is important for marketers to find target audience. This paper presents a hybrid filtering-based algorithm to recommend Telegram users. This method combines the membership graph of users with the profile of groups. The membership graph, models users based on their membership in groups. Also, the profile of each group includes the name and description of the group. We have created a bag of words for each group based on natural language processing methods to combine it with the membership graph. After combination process, users are recommended based on the list of groups obtained. The data used in this study is the information of more than 120 million users and 900,000 supergroups in Telegram. This data is obtained through Telegram API by Idekav system. The evaluation of the proposed method has been done separately on two categories of specialized supergroups. Each category includes 25 specialized supergroups in Telegram. Selected supergroups for evaluation have between 2,000 and 10,000 members. Experimental results show the integrity of the model and error reduction in RMSE.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748794","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420558
Ahmad Jahanbin, M. S. Haghighi
Bitcoin, as the first and the most adopted cryptocurrency, offers many features one of which is contingent payment, that is, the owner of money can programmatically describe the condition upon which his/her money is spent. The condition is determined using a set of instructions written in the Bitcoin scripting language. Unfortunately, this scripting language is not sophisticated enough to create complex conditions or smart contracts in general. Many admirable efforts have been made to build a smart contract infrastructure on top of the Bitcoin platform. In this paper, given the inherent limitations of the Bitcoin scripting language, we critically analyze the practical effectiveness of these methods. Afterwards, we formally define what a smart contract is and introduce seven requirements that if are satisfied, can make creation of smart contracts for Bitcoin possible. Based on the introduced requirements, we examine the ability of the current methods that use secure Multi-party Computation (MPC) to create smart contracts for Bitcoin and show where they fall short. We additionally compare their pros and cons and give clues on how a comprehensive smart contract platform can be possibly built for Bitcoin.
{"title":"On the Possibility of Creating Smart Contracts on Bitcoin by MPC-based Approaches","authors":"Ahmad Jahanbin, M. S. Haghighi","doi":"10.1109/CSICC52343.2021.9420558","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420558","url":null,"abstract":"Bitcoin, as the first and the most adopted cryptocurrency, offers many features one of which is contingent payment, that is, the owner of money can programmatically describe the condition upon which his/her money is spent. The condition is determined using a set of instructions written in the Bitcoin scripting language. Unfortunately, this scripting language is not sophisticated enough to create complex conditions or smart contracts in general. Many admirable efforts have been made to build a smart contract infrastructure on top of the Bitcoin platform. In this paper, given the inherent limitations of the Bitcoin scripting language, we critically analyze the practical effectiveness of these methods. Afterwards, we formally define what a smart contract is and introduce seven requirements that if are satisfied, can make creation of smart contracts for Bitcoin possible. Based on the introduced requirements, we examine the ability of the current methods that use secure Multi-party Computation (MPC) to create smart contracts for Bitcoin and show where they fall short. We additionally compare their pros and cons and give clues on how a comprehensive smart contract platform can be possibly built for Bitcoin.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128718645","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420604
Ali Beikmohammadi, N. Zahabi
Handwritten digit classification considers one of the crucial subjects in machine vision due to its numerous practical usages in many recognition systems. In this regard, Kannada-MNIST was introduced as a challenging dataset. On the other hand, deep neural networks, especially convolutional neural networks, give us an encouraging promise to solve such a problem. In this paper, as a result, we propose a new hierarchically combination method with the help of two CNN models designed from scratch. The results of this novel approach on the Kannada-MNIST dataset indicate its excellent performance because the accuracy on the training, validation, and test sets are 99.86%, 99.66%, and 99.80%, respectively. Fortunately, this proposed method has been able to overcome all the state-of-the-art solutions with the best performance on this dataset.
{"title":"A Hierarchical Method for Kannada-MNIST Classification Based on Convolutional Neural Networks","authors":"Ali Beikmohammadi, N. Zahabi","doi":"10.1109/CSICC52343.2021.9420604","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420604","url":null,"abstract":"Handwritten digit classification considers one of the crucial subjects in machine vision due to its numerous practical usages in many recognition systems. In this regard, Kannada-MNIST was introduced as a challenging dataset. On the other hand, deep neural networks, especially convolutional neural networks, give us an encouraging promise to solve such a problem. In this paper, as a result, we propose a new hierarchically combination method with the help of two CNN models designed from scratch. The results of this novel approach on the Kannada-MNIST dataset indicate its excellent performance because the accuracy on the training, validation, and test sets are 99.86%, 99.66%, and 99.80%, respectively. Fortunately, this proposed method has been able to overcome all the state-of-the-art solutions with the best performance on this dataset.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458679","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 : 2021-03-03DOI: 10.1109/CSICC52343.2021.9420609
M. A. Nourian, A. Kusedghi, A. Akbari
Network slicing is a promising approach to meet the diverse requirements of the various use cases in the 5G networks. Hence, the mobile operators are moving forward to leveraging network slicing in order to measure up with the individual service expectations in their networks. Deploying different network slice types requires the global view of the network and the automated orchestration and management of the underlying resources. This is facilitated by utilizing software-defined networking and network function virtualization as the 5G key-enabler technologies. In this paper, we propose a practical network slicing resource management scheme which is comprised of a dynamic, priority-based resource allocation cooperating with an admission control unit. Adopting the proposed dynamic resource allocation would allow the admission control to comply with more NS requests while ensuring the desired requirements of the existing network slices. To validate the effectiveness of such a mechanism in a real environment, we take advantage of the features provided by OpenAirInterface and FlexRAN to efficiently manage multiple isolated network slices. In particular, we evaluate the significance of the network slicing, the isolation degree among created slices, and the effectiveness of the proposed scheme through several practical scenarios.
{"title":"A practical resource management prototype for mobile networks","authors":"M. A. Nourian, A. Kusedghi, A. Akbari","doi":"10.1109/CSICC52343.2021.9420609","DOIUrl":"https://doi.org/10.1109/CSICC52343.2021.9420609","url":null,"abstract":"Network slicing is a promising approach to meet the diverse requirements of the various use cases in the 5G networks. Hence, the mobile operators are moving forward to leveraging network slicing in order to measure up with the individual service expectations in their networks. Deploying different network slice types requires the global view of the network and the automated orchestration and management of the underlying resources. This is facilitated by utilizing software-defined networking and network function virtualization as the 5G key-enabler technologies. In this paper, we propose a practical network slicing resource management scheme which is comprised of a dynamic, priority-based resource allocation cooperating with an admission control unit. Adopting the proposed dynamic resource allocation would allow the admission control to comply with more NS requests while ensuring the desired requirements of the existing network slices. To validate the effectiveness of such a mechanism in a real environment, we take advantage of the features provided by OpenAirInterface and FlexRAN to efficiently manage multiple isolated network slices. In particular, we evaluate the significance of the network slicing, the isolation degree among created slices, and the effectiveness of the proposed scheme through several practical scenarios.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132628987","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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