Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096808
Aisha A. Abdullahi, Khlood Bawazeer, Salwa Alotaibai, Elmaha Almoaither, Mashael M Al-Otaibi, H. Alaskar, Thavavel Vaiyapuri
Deep learning techniques, particularly convolutional neural networks (CNNs), have proved their success and popularity recently in many fields, especially distinguishing and analyzing medical diseases. Motivated by this direction, our work attempts for the first time to investigate the application of a state-of-the-art deep learning technique on genomic sequences to classify tumours of different classes. The novelty of our approach lies in the application of the popular pre-trained AlexNet on an image version of the RNA-Sequence data. Our methodology demonstrated an outstanding performance with good sensitivity results of 98.3%, 94.1%, 96.6%, 100%, and 100% for selected types of breast, colon, kidney, lung and prostate cancers respectively. The outcome of this work is expected to provide a new direction for genomics data classification and designing accurate automated diagnosis tools.
{"title":"Pretrained Convolutional Neural Networks for Cancer Genome Classification","authors":"Aisha A. Abdullahi, Khlood Bawazeer, Salwa Alotaibai, Elmaha Almoaither, Mashael M Al-Otaibi, H. Alaskar, Thavavel Vaiyapuri","doi":"10.1109/ICCAIS48893.2020.9096808","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096808","url":null,"abstract":"Deep learning techniques, particularly convolutional neural networks (CNNs), have proved their success and popularity recently in many fields, especially distinguishing and analyzing medical diseases. Motivated by this direction, our work attempts for the first time to investigate the application of a state-of-the-art deep learning technique on genomic sequences to classify tumours of different classes. The novelty of our approach lies in the application of the popular pre-trained AlexNet on an image version of the RNA-Sequence data. Our methodology demonstrated an outstanding performance with good sensitivity results of 98.3%, 94.1%, 96.6%, 100%, and 100% for selected types of breast, colon, kidney, lung and prostate cancers respectively. The outcome of this work is expected to provide a new direction for genomics data classification and designing accurate automated diagnosis tools.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124467638","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096753
Roa Alharbi, Meshal Alshaye, Maryam M. Alkanhal, Najla M. Alharbi, Mosa A. Alzahrani, Osama A. Alrehaili
Scoliosis is a common back disease which identifies with an irregular spinal condition. In this case, the spine has a side curvature with an angle. Practically, the standard angle estimation method is done by measuring the Cobb angle for the curvature. Cobb angle is the angle between two drawn lines, upper-end line and lower-end line of the curve. However, manual measurement needs time and effort. In this paper, we proposed an automatic measurement algorithm with machine learning. Initially, X-Rays images are processed utilizing CLAHE method. Then, deep convolutional neural networks (CNN) are applied to detect vertebrae in each X-Ray image. At last, the Cobb angle is measured through a novel algorithm using trigonometry. The proposed method is evaluated on X-Rays dataset from King Saud University (KSU), and it detects each vertebra in those images. In addition, Cobb angle measurements are compared with experts’ manual measurements. Our method achieves the estimation of Cobb angles with high accuracy, showing its great potential in clinical use.
{"title":"Deep Learning Based Algorithm For Automatic Scoliosis Angle Measurement","authors":"Roa Alharbi, Meshal Alshaye, Maryam M. Alkanhal, Najla M. Alharbi, Mosa A. Alzahrani, Osama A. Alrehaili","doi":"10.1109/ICCAIS48893.2020.9096753","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096753","url":null,"abstract":"Scoliosis is a common back disease which identifies with an irregular spinal condition. In this case, the spine has a side curvature with an angle. Practically, the standard angle estimation method is done by measuring the Cobb angle for the curvature. Cobb angle is the angle between two drawn lines, upper-end line and lower-end line of the curve. However, manual measurement needs time and effort. In this paper, we proposed an automatic measurement algorithm with machine learning. Initially, X-Rays images are processed utilizing CLAHE method. Then, deep convolutional neural networks (CNN) are applied to detect vertebrae in each X-Ray image. At last, the Cobb angle is measured through a novel algorithm using trigonometry. The proposed method is evaluated on X-Rays dataset from King Saud University (KSU), and it detects each vertebra in those images. In addition, Cobb angle measurements are compared with experts’ manual measurements. Our method achieves the estimation of Cobb angles with high accuracy, showing its great potential in clinical use.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489541","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096719
D. Alboaneen, Dalia Alsaffar, Alyah Alateeq, A. Alqahtani, Amjad Alfahhad, Bashaier Alqahtani, Rahaf Alamri, Lama Alamri
Internet of Things (IoT) allows devices to communicate with each other in different and important places at the same time. Smart things are developed in ways that interact with each other, such as smart doors and smart homes. One of the most important IoT applications is the smart mirror. It is a mirror that acts as a reflective surface and as an interactive screen at the same time. Smart mirrors can be implemented for different purposes such as a simulator for medical students and an assistant in the fitting rooms. This paper presents a review upon applications of smart mirrors.
{"title":"Internet of Things Based Smart Mirrors: A Literature Review","authors":"D. Alboaneen, Dalia Alsaffar, Alyah Alateeq, A. Alqahtani, Amjad Alfahhad, Bashaier Alqahtani, Rahaf Alamri, Lama Alamri","doi":"10.1109/ICCAIS48893.2020.9096719","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096719","url":null,"abstract":"Internet of Things (IoT) allows devices to communicate with each other in different and important places at the same time. Smart things are developed in ways that interact with each other, such as smart doors and smart homes. One of the most important IoT applications is the smart mirror. It is a mirror that acts as a reflective surface and as an interactive screen at the same time. Smart mirrors can be implemented for different purposes such as a simulator for medical students and an assistant in the fitting rooms. This paper presents a review upon applications of smart mirrors.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121091063","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096873
N. Alsulaim, Raghad Abdullah Alolaqi, R. Alhumaidan
Vehicular Ad hoc NETworks (VANETs) is a network aims to improve road safety by sharing information between vehicles. Security is a major concern regarding critical information shared between vehicles, because information sent over the vehicle network is sensitive and can affect important security decisions. But due to VANET characteristics which is high mobility and its large size, it’s become susceptibility to attacks, especially Denial of Service (DoS) attacks that denial the availability of the network for the users and thus becoming a life crucial. In this paper, we focus on studying the Denial of Service attacks, types and cases on which the attacker may achieve DoS attacks on VANET networks as well as exploring potential solutions in detecting and preventing this attack.
{"title":"Proposed Solutions to Detect and Prevent DoS Attacks on VANETs System","authors":"N. Alsulaim, Raghad Abdullah Alolaqi, R. Alhumaidan","doi":"10.1109/ICCAIS48893.2020.9096873","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096873","url":null,"abstract":"Vehicular Ad hoc NETworks (VANETs) is a network aims to improve road safety by sharing information between vehicles. Security is a major concern regarding critical information shared between vehicles, because information sent over the vehicle network is sensitive and can affect important security decisions. But due to VANET characteristics which is high mobility and its large size, it’s become susceptibility to attacks, especially Denial of Service (DoS) attacks that denial the availability of the network for the users and thus becoming a life crucial. In this paper, we focus on studying the Denial of Service attacks, types and cases on which the attacker may achieve DoS attacks on VANET networks as well as exploring potential solutions in detecting and preventing this attack.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1995 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345755","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096728
H. Kagami
The first mathematical model of the process leading to the onset of urolithiasis so as to clarify how a variety of factors affecting urolithiasis influence the pathogenesis quantitatively was derived. Then conditions for not causing the onset of urolithiasis based on the mathematical model were quantitatively discussed. The background from which this mathematical model was derived was as follows. So far various studies for the cause of the onset of urolithiasis has been made and the factors influencing the pathogenesis has been almost clear. On the other hand, though the understanding of individual factor influencing the pathogenesis has progressed biologically and clinically, theoretical study of the integrated dynamics leading to the calculus of urolithiasis through the crystal growth and aggregation from the crystal nucleation using a mathematical model has not been made yet. In the mathematical model, the process leading to the onset of urolithiasis is divided into the following three processes. (1) formation of crystal nuclei. (2) formation of calculi by growth of crystal nuclei. (3) bonding of calculi to urinary tract cells and growth of calculi. In the first mathematical model, the process of dissolving calculi was not taken into account in the process (3) above. However, in clinical, treatment for dissolving calculi using a stone-dissolving drug is also performed. Therefore, in the mathematical model of the pathogenesis of urolithiasis the calculi dissolution effect must be also taken into account. In this study, the modified mathematical model of the pathogenesis of urolithiasis taking the calculi dissolution effect into account is derived and the nature is examined. Through the analysis of the modified mathematical model and the results of numerical simulation, the conditions for suppressing the calculus growth was modified analytically and numerically. And the dependence of the growth of the calculus on the reaction rate constant concerning dissolution of the calculus, the volume of the urinary tract or the flow rate of urine was also clarified analytically and numerically. In particular, it was shown that if the calculi adhered to the urinary tract, increasing the flow rate or reducing the urinary tract volume would not contribute to the suppression of the calculi growth very much.
{"title":"The Modified Mathematical Model of the Pathogenesis of Urolithiasis: Add Calculi Dissolution Effect","authors":"H. Kagami","doi":"10.1109/ICCAIS48893.2020.9096728","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096728","url":null,"abstract":"The first mathematical model of the process leading to the onset of urolithiasis so as to clarify how a variety of factors affecting urolithiasis influence the pathogenesis quantitatively was derived. Then conditions for not causing the onset of urolithiasis based on the mathematical model were quantitatively discussed. The background from which this mathematical model was derived was as follows. So far various studies for the cause of the onset of urolithiasis has been made and the factors influencing the pathogenesis has been almost clear. On the other hand, though the understanding of individual factor influencing the pathogenesis has progressed biologically and clinically, theoretical study of the integrated dynamics leading to the calculus of urolithiasis through the crystal growth and aggregation from the crystal nucleation using a mathematical model has not been made yet. In the mathematical model, the process leading to the onset of urolithiasis is divided into the following three processes. (1) formation of crystal nuclei. (2) formation of calculi by growth of crystal nuclei. (3) bonding of calculi to urinary tract cells and growth of calculi. In the first mathematical model, the process of dissolving calculi was not taken into account in the process (3) above. However, in clinical, treatment for dissolving calculi using a stone-dissolving drug is also performed. Therefore, in the mathematical model of the pathogenesis of urolithiasis the calculi dissolution effect must be also taken into account. In this study, the modified mathematical model of the pathogenesis of urolithiasis taking the calculi dissolution effect into account is derived and the nature is examined. Through the analysis of the modified mathematical model and the results of numerical simulation, the conditions for suppressing the calculus growth was modified analytically and numerically. And the dependence of the growth of the calculus on the reaction rate constant concerning dissolution of the calculus, the volume of the urinary tract or the flow rate of urine was also clarified analytically and numerically. In particular, it was shown that if the calculi adhered to the urinary tract, increasing the flow rate or reducing the urinary tract volume would not contribute to the suppression of the calculi growth very much.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130347623","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096821
Reem M. Alotaibi, Isra M. Al-Turaiki, Fatimah Alakeel
Phishing detection has gained huge attention from both academia and industry. Damages and data breaches affecting private and governmental entities caused by email phishing attacks needed an immediate solution. The diversity of attack patterns and mediums associated with phishing attacks made the development of an optimal solution challenging. Also, attackers usually make legitimate looking content using legitimate wording or legitimate looking URLs and websites. Many of the existing phishing solutions requires manual feature extraction that requires expert domain knowledge and thoughtful selection of valuable features to be efficient. Additionally, most effective phishing solutions suffered from large computational costs. In this paper, we propose CNNPD, an email phishing detection framework based on Convolutional Neural Network (CNN). CNNPD marks incoming emails into phishing or benign. Testing the framework on an email dataset shows promising performance in terms of accuracy, precision, and recall when compared to similar approaches.
{"title":"Mitigating Email Phishing Attacks using Convolutional Neural Networks","authors":"Reem M. Alotaibi, Isra M. Al-Turaiki, Fatimah Alakeel","doi":"10.1109/ICCAIS48893.2020.9096821","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096821","url":null,"abstract":"Phishing detection has gained huge attention from both academia and industry. Damages and data breaches affecting private and governmental entities caused by email phishing attacks needed an immediate solution. The diversity of attack patterns and mediums associated with phishing attacks made the development of an optimal solution challenging. Also, attackers usually make legitimate looking content using legitimate wording or legitimate looking URLs and websites. Many of the existing phishing solutions requires manual feature extraction that requires expert domain knowledge and thoughtful selection of valuable features to be efficient. Additionally, most effective phishing solutions suffered from large computational costs. In this paper, we propose CNNPD, an email phishing detection framework based on Convolutional Neural Network (CNN). CNNPD marks incoming emails into phishing or benign. Testing the framework on an email dataset shows promising performance in terms of accuracy, precision, and recall when compared to similar approaches.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131424647","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096763
Fadyah A. AlShlawi, Nourah K. AlSa’awi, Waad Y. Bin Saleem, Anees Ara
Bitcoin Cryptocurrency is an advanced decentralized electronic payment system which stores all the transaction records in a public ledger connected to the blockchain. Many cyber-attacks including Double-Spending, Distributed denial-of-service (DDOS), Sybil attack and Dust attack, have been targeting Blockchain-based application Cryptocurrency, mainly Bitcoin cryptocurrency. In this paper, the proposed DUST-MASK, a secure Bitcoin system against dust attack, protects Bitcoin’s availability and pseudo-anonymity from attackers sending dust transactions in order to analyze the data and link the transactions to a specific user. The proposed system will inform the user of those kinds of malicious transactions and guarantee the user a choice whether to accept or reject them.
{"title":"DUST-MASK: A Framework for Preventing Bitcoin’s Dust Attacks","authors":"Fadyah A. AlShlawi, Nourah K. AlSa’awi, Waad Y. Bin Saleem, Anees Ara","doi":"10.1109/ICCAIS48893.2020.9096763","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096763","url":null,"abstract":"Bitcoin Cryptocurrency is an advanced decentralized electronic payment system which stores all the transaction records in a public ledger connected to the blockchain. Many cyber-attacks including Double-Spending, Distributed denial-of-service (DDOS), Sybil attack and Dust attack, have been targeting Blockchain-based application Cryptocurrency, mainly Bitcoin cryptocurrency. In this paper, the proposed DUST-MASK, a secure Bitcoin system against dust attack, protects Bitcoin’s availability and pseudo-anonymity from attackers sending dust transactions in order to analyze the data and link the transactions to a specific user. The proposed system will inform the user of those kinds of malicious transactions and guarantee the user a choice whether to accept or reject them.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131556543","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096818
A. Munshi, Nouf Ayadh Alqarni, Nadia Abdullah Almalki
Internet of Things (IoT) is an application of the internet correlation with devices that makes human life easy. The need to use (IoT) in our lives makes this field expands every day without stopping. Which would let everything connected to the internet exposure to penetration. As the need for (IoT) devices grows, the horizon of malicious abuse expands [1]. In this paper, we will study one of the most common violations in IoT devices, which is Distributed Denial of Service (DDoS) attack and study its impact on (IoT) devices in order to be aware to control our utilizations and the need to secure the Internet of Things devices in our lives.
{"title":"DDOS Attack on IOT Devices","authors":"A. Munshi, Nouf Ayadh Alqarni, Nadia Abdullah Almalki","doi":"10.1109/ICCAIS48893.2020.9096818","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096818","url":null,"abstract":"Internet of Things (IoT) is an application of the internet correlation with devices that makes human life easy. The need to use (IoT) in our lives makes this field expands every day without stopping. Which would let everything connected to the internet exposure to penetration. As the need for (IoT) devices grows, the horizon of malicious abuse expands [1]. In this paper, we will study one of the most common violations in IoT devices, which is Distributed Denial of Service (DDoS) attack and study its impact on (IoT) devices in order to be aware to control our utilizations and the need to secure the Internet of Things devices in our lives.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258599","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096832
Y. AlRoshdi, Mohammed AlBadawi, Mohammed Sarrab
Learning systems contain an enormous amount of knowledge that is of high importance for students. This knowledge exists in different learning materials, such as documents, figures, and diagrams, videos, etc. The students can use this information to increase and build their knowledge. However, the searching mechanisms through these materials are difficult and time-consuming. Besides, the student consuming time in from of the social networks. Therefore, there is a need to utilize and enhance the way of learning and summarizing knowledge from the tremendous amount of learning materials that exist in the e-learning systems. Also, we want to benefit from the students’ desire in the social networks environment to disseminate the extracted knowledge among them through those platforms. Thus, this research aims to propose a framework to disseminate the learning content to the target students through the social network environment. This framework seeks to improve the learning experience and increase the learning outcomes of the students.
{"title":"Framework for Socializing Learning Content","authors":"Y. AlRoshdi, Mohammed AlBadawi, Mohammed Sarrab","doi":"10.1109/ICCAIS48893.2020.9096832","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096832","url":null,"abstract":"Learning systems contain an enormous amount of knowledge that is of high importance for students. This knowledge exists in different learning materials, such as documents, figures, and diagrams, videos, etc. The students can use this information to increase and build their knowledge. However, the searching mechanisms through these materials are difficult and time-consuming. Besides, the student consuming time in from of the social networks. Therefore, there is a need to utilize and enhance the way of learning and summarizing knowledge from the tremendous amount of learning materials that exist in the e-learning systems. Also, we want to benefit from the students’ desire in the social networks environment to disseminate the extracted knowledge among them through those platforms. Thus, this research aims to propose a framework to disseminate the learning content to the target students through the social network environment. This framework seeks to improve the learning experience and increase the learning outcomes of the students.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115290023","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 : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096865
Fatimah Alghamdi
Congestion is very common in wireless networks as multiple sensors try to transmit data simultaneously. The Internet Of Things (IOT) is a dynamic system. Using a specific congestion control algorithm with one IOT system provides different results from other IOT systems. This means that an IOT developer cannot use the same congestion control algorithm with different IOT systems, because the efficiency of congestion control algorithms varies from one IOT system to another according to the infrastructure of the smart system and the amount of transmitted data. The primary purpose of this work is to support analysts and designers of congestion control algorithms at IOT companies by understanding the metrics influencing congestion control. This will enable them to select the appropriate metrics depending on the nature of the IOT infrastructure and the amount of transmitted data. This study also conducts a literature review of papers that discuss transport protocols providing congestion control. The data extraction process gathered from 30 transport protocols concerning congestion control. From the reviewed papers, we extract the metrics that influence congestion control detection, notification, and mitigation. After that, we applied some statistical solutions on extracted metrics. We find queue length is the metrics used most often for congestion detection. While additive increase multiplicative decrease (AIMD) for single-bit transmitting is the most used metrics for congestion notication. Whereas rate control is the most used metrics for congestion mitigation
{"title":"Metrics that impact on Congestion Control at Internet Of Things Environment","authors":"Fatimah Alghamdi","doi":"10.1109/ICCAIS48893.2020.9096865","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096865","url":null,"abstract":"Congestion is very common in wireless networks as multiple sensors try to transmit data simultaneously. The Internet Of Things (IOT) is a dynamic system. Using a specific congestion control algorithm with one IOT system provides different results from other IOT systems. This means that an IOT developer cannot use the same congestion control algorithm with different IOT systems, because the efficiency of congestion control algorithms varies from one IOT system to another according to the infrastructure of the smart system and the amount of transmitted data. The primary purpose of this work is to support analysts and designers of congestion control algorithms at IOT companies by understanding the metrics influencing congestion control. This will enable them to select the appropriate metrics depending on the nature of the IOT infrastructure and the amount of transmitted data. This study also conducts a literature review of papers that discuss transport protocols providing congestion control. The data extraction process gathered from 30 transport protocols concerning congestion control. From the reviewed papers, we extract the metrics that influence congestion control detection, notification, and mitigation. After that, we applied some statistical solutions on extracted metrics. We find queue length is the metrics used most often for congestion detection. While additive increase multiplicative decrease (AIMD) for single-bit transmitting is the most used metrics for congestion notication. Whereas rate control is the most used metrics for congestion mitigation","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122897767","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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