Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.023697
S. Soltani, Seyed Amin Hosseini Seno, J. Rejito, R. Budiarto
: Satellite networks are recognized as the most essential communication infrastructures in the world today, which complement land networks and provide valuable services for their users. Extensive coverage and service stability of these networks have increased their popularity. Since eavesdropping and active intrusion in satellite communications are much easier than in terrestrial networks, securing satellite communications is vital. So far, several protocols have been proposed for authentication and key exchange of satellite communications, but none of them fully meet the security requirements. In this paper, we examine one of these protocols and identify its security vulnerabilities. Moreover, we propose a robust and secure authentication and session key agreement protocol using the elliptic curve cryptography (ECC). We show that the proposed protocol meets common security requirements and is resistant to known security attacks. Moreover, we prove that the proposed scheme satisfies the security features using the Automated Validation of Internet Security Protocols and Applications (AVISPA) formal verification tool and On-the fly Model-Checker (OFMC) and ATtack SEarcher (ATSE) model checkers. We have also proved the security of the session key exchange of our protocol using the Real or Random (RoR) model. Finally, the comparison of our scheme with similar methods shows its superiority.
卫星网络被认为是当今世界上最重要的通信基础设施，它补充了陆地网络并为其用户提供有价值的服务。这些网络的广泛覆盖和服务稳定性使其越来越受欢迎。由于对卫星通信的窃听和主动入侵比地面网络容易得多，因此确保卫星通信的安全至关重要。目前，针对卫星通信的身份验证和密钥交换，已经提出了几种协议，但都不能完全满足安全要求。在本文中，我们研究了其中一个协议并确定了其安全漏洞。此外，我们还提出了一种使用椭圆曲线加密(ECC)的鲁棒安全认证和会话密钥协商协议。我们证明了所提出的协议满足常见的安全需求，并且能够抵抗已知的安全攻击。此外，我们使用互联网安全协议和应用程序的自动验证(AVISPA)形式验证工具和动态模型检查器(OFMC)和攻击搜索器(ATSE)模型检查器证明了所提出的方案满足安全特性。我们还使用Real or Random (RoR)模型证明了我们协议会话密钥交换的安全性。最后，通过与同类方法的比较，证明了该方案的优越性。
{"title":"Robust Authentication and Session Key Agreement Protocol for Satellite Communications","authors":"S. Soltani, Seyed Amin Hosseini Seno, J. Rejito, R. Budiarto","doi":"10.32604/cmc.2022.023697","DOIUrl":"https://doi.org/10.32604/cmc.2022.023697","url":null,"abstract":": Satellite networks are recognized as the most essential communication infrastructures in the world today, which complement land networks and provide valuable services for their users. Extensive coverage and service stability of these networks have increased their popularity. Since eavesdropping and active intrusion in satellite communications are much easier than in terrestrial networks, securing satellite communications is vital. So far, several protocols have been proposed for authentication and key exchange of satellite communications, but none of them fully meet the security requirements. In this paper, we examine one of these protocols and identify its security vulnerabilities. Moreover, we propose a robust and secure authentication and session key agreement protocol using the elliptic curve cryptography (ECC). We show that the proposed protocol meets common security requirements and is resistant to known security attacks. Moreover, we prove that the proposed scheme satisfies the security features using the Automated Validation of Internet Security Protocols and Applications (AVISPA) formal verification tool and On-the fly Model-Checker (OFMC) and ATtack SEarcher (ATSE) model checkers. We have also proved the security of the session key exchange of our protocol using the Real or Random (RoR) model. Finally, the comparison of our scheme with similar methods shows its superiority.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"336 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80782215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.020111
Kazim Ali, Adnan N. Quershi, Ahmad Alauddin Bin Arifin, Muhammad Shahid Bhatti, A. Sohail, Rohail Hassan
These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction. In this scenario, we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again. We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence. We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method. In the end, we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.
{"title":"Deep Image Restoration Model: A Defense Method Against Adversarial Attacks","authors":"Kazim Ali, Adnan N. Quershi, Ahmad Alauddin Bin Arifin, Muhammad Shahid Bhatti, A. Sohail, Rohail Hassan","doi":"10.32604/cmc.2022.020111","DOIUrl":"https://doi.org/10.32604/cmc.2022.020111","url":null,"abstract":"These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction. In this scenario, we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again. We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence. We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method. In the end, we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"34 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85057975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.017910
J. V. Anchitaalagammai, T. Jayasankar, P. Selvaraj, Mohamed Yacin Sikkandar, M. Zakarya, M. Elhoseny, K. Shankar
: Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based clustering, Lion Whale Optimization with Tumbling (LWOT)-based routing and cluster maintenance phase. The proposed EECBRM model was validated through a series of experiments and the results were verified under several aspects. EECBRM model was compared with existing methods in terms of energy efficiency, delay, number of data transmission, and network lifetime. When simulated, in comparison with other methods, EECBRM model yielded excellent results in a significant manner. Thus, the efficiency of the proposed model is established.
{"title":"Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment","authors":"J. V. Anchitaalagammai, T. Jayasankar, P. Selvaraj, Mohamed Yacin Sikkandar, M. Zakarya, M. Elhoseny, K. Shankar","doi":"10.32604/cmc.2022.017910","DOIUrl":"https://doi.org/10.32604/cmc.2022.017910","url":null,"abstract":": Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based clustering, Lion Whale Optimization with Tumbling (LWOT)-based routing and cluster maintenance phase. The proposed EECBRM model was validated through a series of experiments and the results were verified under several aspects. EECBRM model was compared with existing methods in terms of energy efficiency, delay, number of data transmission, and network lifetime. When simulated, in comparison with other methods, EECBRM model yielded excellent results in a significant manner. Thus, the efficiency of the proposed model is established.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"29 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85518367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.021382
A. Saleh Ahmar, Eva Boj del Val, M. A. El Safty, Sami Saleh Alzahrani, Hamed El-Khawaga
: This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083.
{"title":"SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia","authors":"A. Saleh Ahmar, Eva Boj del Val, M. A. El Safty, Sami Saleh Alzahrani, Hamed El-Khawaga","doi":"10.32604/cmc.2022.021382","DOIUrl":"https://doi.org/10.32604/cmc.2022.021382","url":null,"abstract":": This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"83 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83255120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.023500
Mohammed H. Alsharif, Md. Sanwar Hossain, Abu Jahid, Muhammad Asghar Khan, Bong Jun Choi, Samih M. M. Mostafa
{"title":"Milestones of Wireless Communication Networks and Technology Prospect of Next Generation (6G)","authors":"Mohammed H. Alsharif, Md. Sanwar Hossain, Abu Jahid, Muhammad Asghar Khan, Bong Jun Choi, Samih M. M. Mostafa","doi":"10.32604/cmc.2022.023500","DOIUrl":"https://doi.org/10.32604/cmc.2022.023500","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"27 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83255325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.020823
A. Naseer, E. Nava Baro, Sultan Daud Khan, Yolanda Vila, Jennifer Doyle
{"title":"A Novel Cryptocurrency Prediction Method Using Optimum CNN","authors":"A. Naseer, E. Nava Baro, Sultan Daud Khan, Yolanda Vila, Jennifer Doyle","doi":"10.32604/cmc.2022.020823","DOIUrl":"https://doi.org/10.32604/cmc.2022.020823","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"3 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83658648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.020238
Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, U. Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad
{"title":"IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System","authors":"Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, U. Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad","doi":"10.32604/cmc.2022.020238","DOIUrl":"https://doi.org/10.32604/cmc.2022.020238","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"69 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83826061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.019586
Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, U. Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad
: Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.
{"title":"Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks","authors":"Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, U. Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad","doi":"10.32604/cmc.2022.019586","DOIUrl":"https://doi.org/10.32604/cmc.2022.019586","url":null,"abstract":": Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"16 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81799271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.023003
S. Abbas, Z. Raza, Nida Siddiqui, Faheem Khan, T. Whangbo
: Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G , which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC ( n ), and the hexagonal network HX ( n ) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN 1( n ) are also proposed.
{"title":"Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT","authors":"S. Abbas, Z. Raza, Nida Siddiqui, Faheem Khan, T. Whangbo","doi":"10.32604/cmc.2022.023003","DOIUrl":"https://doi.org/10.32604/cmc.2022.023003","url":null,"abstract":": Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G , which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC ( n ), and the hexagonal network HX ( n ) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN 1( n ) are also proposed.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"14 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81856015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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