Pub Date : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624278
R. Umar, Ridho Surya Kusuma
Corporate organizations, governments and public services seek to protect their valuable assets such as databases from cyberattacks. Attacks on security systems allow breaches, theft, and manipulation of data. Therefore, a system is needed to reduce vulnerabilities and threats. The security model implemented is the implementation of laws that guarantee data protection, blockchain, and authentication. This study offers designers a recovery system prototype that uses network management based on Software-Defined Networking (SDN) technology. The SDN technology applied is Ryu Controller, which allows access to specific databases in a specially programmed network. This research phase begins with needs analysis, virtual environment, examination, analysis, and cyber-attack testing. The results obtained in this study successfully implemented a recovery system with a total time for backing up four servers in one period was ±90 minutes, and the recovery time was ±90 minutes. Based on this, this research is fit for the purpose and can be used to reduce cyberattacks.
{"title":"Recovery System using SDN Technology for Cyber Attack Solution","authors":"R. Umar, Ridho Surya Kusuma","doi":"10.23919/eecsi53397.2021.9624278","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624278","url":null,"abstract":"Corporate organizations, governments and public services seek to protect their valuable assets such as databases from cyberattacks. Attacks on security systems allow breaches, theft, and manipulation of data. Therefore, a system is needed to reduce vulnerabilities and threats. The security model implemented is the implementation of laws that guarantee data protection, blockchain, and authentication. This study offers designers a recovery system prototype that uses network management based on Software-Defined Networking (SDN) technology. The SDN technology applied is Ryu Controller, which allows access to specific databases in a specially programmed network. This research phase begins with needs analysis, virtual environment, examination, analysis, and cyber-attack testing. The results obtained in this study successfully implemented a recovery system with a total time for backing up four servers in one period was ±90 minutes, and the recovery time was ±90 minutes. Based on this, this research is fit for the purpose and can be used to reduce cyberattacks.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129544312","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624264
Victor Lamboy Sinaga, R. F. Sari
Software Define Wide Area Network (SDWAN) is a solution for utilizing technology in sending information. SDWAN is designed by separating the control plane and data plane by applying the Software Define Network (SDN) concept; therefore, it can use physical devices more effectively and efficiently. With many nodes and spread, node grouping is needed to facilitate control and supervision that requires a controller in each cluster. Installing many nodes on SDWAN will require several controllers to make it more effective and efficient placement. Optimal controller placement will improve the performance of the network. In this study, determining the optimal controller location requires various methods that are interconnected with each other. The algorithms used are the Haversine method, Johnson's Algorithm, Partition Around Medoids (PAM), and Silhouette Analysis. The number of nodes and locations obtained from Zootopology in this research using the Indonesia Biznet network, therefore the recommendation for the optimal number of controllers is obtained using the Silhouette, Gap, Calinski-Harabasz, and Davies-Bouldien evaluation methods. The algorithms aim to get the optimal point by determining the number of controllers and recommendations for the optimal number of controllers as an initial recommendation for a company that uses this method. In this study, the most optimal number of controllers on the Biznet network with 29 nodes were two controllers and an average value of Silhouette analysis calculation of 0.51846.
{"title":"Optimization of Multi-Controller Locations in SDWAN using Various Method","authors":"Victor Lamboy Sinaga, R. F. Sari","doi":"10.23919/eecsi53397.2021.9624264","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624264","url":null,"abstract":"Software Define Wide Area Network (SDWAN) is a solution for utilizing technology in sending information. SDWAN is designed by separating the control plane and data plane by applying the Software Define Network (SDN) concept; therefore, it can use physical devices more effectively and efficiently. With many nodes and spread, node grouping is needed to facilitate control and supervision that requires a controller in each cluster. Installing many nodes on SDWAN will require several controllers to make it more effective and efficient placement. Optimal controller placement will improve the performance of the network. In this study, determining the optimal controller location requires various methods that are interconnected with each other. The algorithms used are the Haversine method, Johnson's Algorithm, Partition Around Medoids (PAM), and Silhouette Analysis. The number of nodes and locations obtained from Zootopology in this research using the Indonesia Biznet network, therefore the recommendation for the optimal number of controllers is obtained using the Silhouette, Gap, Calinski-Harabasz, and Davies-Bouldien evaluation methods. The algorithms aim to get the optimal point by determining the number of controllers and recommendations for the optimal number of controllers as an initial recommendation for a company that uses this method. In this study, the most optimal number of controllers on the Biznet network with 29 nodes were two controllers and an average value of Silhouette analysis calculation of 0.51846.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130774364","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624273
Benni Purnama, D. Stiawan, Darmawijoyo Hanapi, E. Winanto, R. Budiarto, Mohd Yazid Bin Idris
Malware is a threat that can compromise cyber security. Currently, the development of malware is becoming increasingly complex and difficult to detect. One way to improve detection accuracy is to implement the n-gram feature extraction. n-gram is one of method to analyze malware, by capturing the frequency of string/opcode which often appear from malware. This work aims to improve the performance of malware detection by evaluating the best number of n-grams to extract the opcode. Selection of n number in n-gram process will be very influencing in malware classification result. This research work investigates the effect the n value of n-gram on the accuracy detection by varying the value n = 1 to n = 5. The best accuracy detection in the experiments using Multilayer Perceptron (MLP) classifier reaches 89 percent.
{"title":"n-gram Effect in Malware Detection Using Multilayer Perceptron (MLP)","authors":"Benni Purnama, D. Stiawan, Darmawijoyo Hanapi, E. Winanto, R. Budiarto, Mohd Yazid Bin Idris","doi":"10.23919/eecsi53397.2021.9624273","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624273","url":null,"abstract":"Malware is a threat that can compromise cyber security. Currently, the development of malware is becoming increasingly complex and difficult to detect. One way to improve detection accuracy is to implement the n-gram feature extraction. n-gram is one of method to analyze malware, by capturing the frequency of string/opcode which often appear from malware. This work aims to improve the performance of malware detection by evaluating the best number of n-grams to extract the opcode. Selection of n number in n-gram process will be very influencing in malware classification result. This research work investigates the effect the n value of n-gram on the accuracy detection by varying the value n = 1 to n = 5. The best accuracy detection in the experiments using Multilayer Perceptron (MLP) classifier reaches 89 percent.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131050253","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624228
D. K. Agustika, N. Ariyanti, I Nyoman Kusuma Wardana, D. Iliescu, M. Leeson
The geographical origin of the plants can affect the growth and hence the quality of the plants. In this research, the origin of the chili plants has been investigated by using Fourier transform infrared (FTIR) spectroscopy. The spectroscopy generated 3734 data with a wavenumber range from 4000–400 cm−1. The pre-processing of the spectra was done by using baseline correction and vector normalization. The analysis was then taken in the biofingerprint area of 1800–900 cm−1 range which has 934 data points. Feature extraction for dimension reduction was achieved using principal component analysis (PCA). The PC scores from PCA were then fed into a k-means and a multilayer perceptron neural network (MLPNN). The k-means clustering shows that the samples can be distinguished into three different groups. Meanwhile, for the MLPNN, the number of the hidden layer's neurons and the learning rate of the system were optimized to get the best classification result. A hidden layer with twenty neurons had the highest accuracy, while a learning rate of 0.001 had the highest value of 100%.
{"title":"Classification of Chili Plant Origin by Using Multilayer Perceptron Neural Network","authors":"D. K. Agustika, N. Ariyanti, I Nyoman Kusuma Wardana, D. Iliescu, M. Leeson","doi":"10.23919/eecsi53397.2021.9624228","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624228","url":null,"abstract":"The geographical origin of the plants can affect the growth and hence the quality of the plants. In this research, the origin of the chili plants has been investigated by using Fourier transform infrared (FTIR) spectroscopy. The spectroscopy generated 3734 data with a wavenumber range from 4000–400 cm−1. The pre-processing of the spectra was done by using baseline correction and vector normalization. The analysis was then taken in the biofingerprint area of 1800–900 cm−1 range which has 934 data points. Feature extraction for dimension reduction was achieved using principal component analysis (PCA). The PC scores from PCA were then fed into a k-means and a multilayer perceptron neural network (MLPNN). The k-means clustering shows that the samples can be distinguished into three different groups. Meanwhile, for the MLPNN, the number of the hidden layer's neurons and the learning rate of the system were optimized to get the best classification result. A hidden layer with twenty neurons had the highest accuracy, while a learning rate of 0.001 had the highest value of 100%.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885076","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624315
M. Rahmawaty, H. Hendriko, Engla Puspita Haryanisa
Biodiesel is one of the renewable energies that is environmentally friendly. Generally, it is made from renewable materials that consisting of fatty acids. Biodiesel can also be made from vegetable oil, animal oil and waste cooking oil. Producing biodiesel is divided into three stages: heating and stirring process, cooling and washing process, and drying and filtration process. In this study, a machine for the first stage processing of biodiesel-based waste cooking oil was made. The machine is used for heating and mixing waste cooking oil and catalyst materials. The developed machine is equipped with a control system that is aimed for detecting the temperature of mixture materials. The data obtained by the sensor is then used to regulate the flow rate of the gas. The flow of the gas is controlled by a valve that is actuated by a servo motor. Several tests have been carried out. The tests were aimed to determine several control parameters that is used in programming. Moreover, the objective of the tests is also to determine the effectiveness of the machine in producing fatty acid methyl ester (FAME). The test results show that the gas flow rate should be reduced when the temperature of mixture material reaches 55 Celsius. The gas flow rate was reduced by changing the gas valve angle from 90 to 13. The machine's ability in producing FAME has been tested using three different volumes of waste cooking oil. The results showed that the amount of FAME produced is quite large.
{"title":"Development of Heater and Mixer Machine With Control System for Biodiesel Production","authors":"M. Rahmawaty, H. Hendriko, Engla Puspita Haryanisa","doi":"10.23919/eecsi53397.2021.9624315","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624315","url":null,"abstract":"Biodiesel is one of the renewable energies that is environmentally friendly. Generally, it is made from renewable materials that consisting of fatty acids. Biodiesel can also be made from vegetable oil, animal oil and waste cooking oil. Producing biodiesel is divided into three stages: heating and stirring process, cooling and washing process, and drying and filtration process. In this study, a machine for the first stage processing of biodiesel-based waste cooking oil was made. The machine is used for heating and mixing waste cooking oil and catalyst materials. The developed machine is equipped with a control system that is aimed for detecting the temperature of mixture materials. The data obtained by the sensor is then used to regulate the flow rate of the gas. The flow of the gas is controlled by a valve that is actuated by a servo motor. Several tests have been carried out. The tests were aimed to determine several control parameters that is used in programming. Moreover, the objective of the tests is also to determine the effectiveness of the machine in producing fatty acid methyl ester (FAME). The test results show that the gas flow rate should be reduced when the temperature of mixture material reaches 55 Celsius. The gas flow rate was reduced by changing the gas valve angle from 90 to 13. The machine's ability in producing FAME has been tested using three different volumes of waste cooking oil. The results showed that the amount of FAME produced is quite large.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"64 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131471555","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624269
Widhi Winata Sakti, K. Anam, Satryo B Utomo, B. Marhaenanto, Safri Nahela
People with disabilities such as hand amputations have limited motor activity. Several robotic prosthetic arms were developed to help them. The challenge arises when the robot's control source comes from the user's wishes extracted from brain signals via electroencephalography (EEG) signals. This research develops a raspberry-based embedded system device that is connected to EEG electrodes and functions as an artificial intelligence internet of things (AIoT) so that it can be controlled via the internet in real-time. The deep learning model used is convolutional neural networks (CNN) and autonomous deep learning (ADL). The results of the training with 5-fold cross-validation achieved an accuracy of about 98% in the four classes. The results of real-time testing over the network produce a pretty good response time of about 1 second.
{"title":"Artificial Intelligence IoT based EEG Application using Deep Learning for Movement Classification","authors":"Widhi Winata Sakti, K. Anam, Satryo B Utomo, B. Marhaenanto, Safri Nahela","doi":"10.23919/eecsi53397.2021.9624269","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624269","url":null,"abstract":"People with disabilities such as hand amputations have limited motor activity. Several robotic prosthetic arms were developed to help them. The challenge arises when the robot's control source comes from the user's wishes extracted from brain signals via electroencephalography (EEG) signals. This research develops a raspberry-based embedded system device that is connected to EEG electrodes and functions as an artificial intelligence internet of things (AIoT) so that it can be controlled via the internet in real-time. The deep learning model used is convolutional neural networks (CNN) and autonomous deep learning (ADL). The results of the training with 5-fold cross-validation achieved an accuracy of about 98% in the four classes. The results of real-time testing over the network produce a pretty good response time of about 1 second.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127280455","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624247
N. Setyawan, Tri Septiana Nadia Puspita Putri, Mohamad Al Fikih, N. Kasan
Coronavirus Disease (COVID-19) is gaining special concern from entire world population. The transmission of the COVID-19 virus is spreading almost in whole the world, including Indonesia which undergoing a crisis, especially in the health and economic sector. In prevention, the government is implementing Large-Scale Social Restrictions which public services or public places require people to wear masks. During this time, the detection of masks is done manually with observations from security personnel, which is time consuming. This study will apply a mask detection system (Face Mask Detection) using deep learning image processing. This study apply the most popular deep learning model which consist Convolutional Neural Networks (CNN) and You Only Look Once (YOLOv3) method. In training step, the datasets taken vary with images of faces that using head attribute such as hijabs, hats, and not using attributes. In addition, the images were taken from various countries such as Asia including Indonesia mostly, Europe, and the Americas. The system used a combination of object detection classification, image, and object tracking to develop a system that detects using a mask or not using a mask faces in images or camera videos. From the comparative analysis which developed in training and deploying step with image and camera video stream, YOLOv3 can detect accurately and faster with 4.8 FPS than CNN.
冠状病毒病(COVID-19)正受到全世界人民的特别关注。COVID-19病毒的传播几乎在全世界蔓延，包括正在经历危机的印度尼西亚，特别是在卫生和经济部门。在预防方面，政府正在实施大规模的社会限制，公共服务或公共场所要求人们戴口罩。在此期间，口罩的检测是由安保人员手工观察完成的，耗时较长。本研究将应用深度学习图像处理的面具检测系统(Face mask detection)。本研究采用了目前最流行的深度学习模型，包括卷积神经网络(CNN)和You Only Look Once (YOLOv3)方法。在训练步骤中，使用头部属性(如hijab, hats)和不使用属性的人脸图像所获取的数据集有所不同。此外，这些照片主要拍摄于印度尼西亚等亚洲、欧洲、美洲等多个国家。该系统将目标检测分类、图像和目标跟踪相结合，开发了一种检测图像或摄像机视频中使用掩模或不使用掩模人脸的系统。通过对图像和摄像机视频流在训练和部署阶段进行的对比分析，YOLOv3能够以4.8 FPS的速度比CNN更快准确地进行检测。
{"title":"Comparative Study of CNN and YOLOv3 in Public Health Face Mask Detection","authors":"N. Setyawan, Tri Septiana Nadia Puspita Putri, Mohamad Al Fikih, N. Kasan","doi":"10.23919/eecsi53397.2021.9624247","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624247","url":null,"abstract":"Coronavirus Disease (COVID-19) is gaining special concern from entire world population. The transmission of the COVID-19 virus is spreading almost in whole the world, including Indonesia which undergoing a crisis, especially in the health and economic sector. In prevention, the government is implementing Large-Scale Social Restrictions which public services or public places require people to wear masks. During this time, the detection of masks is done manually with observations from security personnel, which is time consuming. This study will apply a mask detection system (Face Mask Detection) using deep learning image processing. This study apply the most popular deep learning model which consist Convolutional Neural Networks (CNN) and You Only Look Once (YOLOv3) method. In training step, the datasets taken vary with images of faces that using head attribute such as hijabs, hats, and not using attributes. In addition, the images were taken from various countries such as Asia including Indonesia mostly, Europe, and the Americas. The system used a combination of object detection classification, image, and object tracking to develop a system that detects using a mask or not using a mask faces in images or camera videos. From the comparative analysis which developed in training and deploying step with image and camera video stream, YOLOv3 can detect accurately and faster with 4.8 FPS than CNN.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368680","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624311
Muhammad Fauzan Ridho, Irwan
Strawberry has a tremendous economic value as well as being visually appealing. Therefore, strawberry farmers need to ensure that they only harvest good quality strawberries. However, assessing the quality of strawberries is not an easy problem, especially for local plantations which do not have enough human resources. As robotics becomes accessible and widely used for agriculture work such as harvesting fruit, the real-time embedded system computation power becomes much more powerful nowadays. This paper discusses the harvesting robot's ability to distinguish the quality of strawberries in realtime detection using computer vision technology in the form of object detection by utilizing a deep neural network in a single board computer (SBC). The robot software is built on Robot Operating System (ROS) framework. The proposed method is tested on a robot equipped with a monocular camera. The learning process shows that the robot can detect and differentiate between good and bad quality strawberries with 90% accuracy and maintain a high frame rate.
{"title":"Strawberry Fruit Quality Assessment for Harvesting Robot using SSD Convolutional Neural Network","authors":"Muhammad Fauzan Ridho, Irwan","doi":"10.23919/eecsi53397.2021.9624311","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624311","url":null,"abstract":"Strawberry has a tremendous economic value as well as being visually appealing. Therefore, strawberry farmers need to ensure that they only harvest good quality strawberries. However, assessing the quality of strawberries is not an easy problem, especially for local plantations which do not have enough human resources. As robotics becomes accessible and widely used for agriculture work such as harvesting fruit, the real-time embedded system computation power becomes much more powerful nowadays. This paper discusses the harvesting robot's ability to distinguish the quality of strawberries in realtime detection using computer vision technology in the form of object detection by utilizing a deep neural network in a single board computer (SBC). The robot software is built on Robot Operating System (ROS) framework. The proposed method is tested on a robot equipped with a monocular camera. The learning process shows that the robot can detect and differentiate between good and bad quality strawberries with 90% accuracy and maintain a high frame rate.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121591924","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624255
M. M. Arifin, D. Stiawan, Susanto, J. Rejito, Mohd Yazid Bin Idris, R. Budiarto
SCADA was designed to be used in an isolated area however, in modern SCADA, its connection to the Internet has become essential due to performance and commercial needs. This extended SCADA interconnection creates new vulnerabilities in the SCADA network. One of the attacks that may occur caused by the extended interconnection of SCADA networks to heterogeneous networks is Denial of Service attacks (DoS). DoS attack is launched by sending many messages from nodes. The development of easily accessible and simple DoS tools has increased the frequency of attacks. Ease of access and use of DoS tools made reduced the level of expertise needed to launch an attack. This study uses a SCADA dataset containing DoS attacks and running IEC 60870-5-104 protocol where this protocol will be encapsulated into TCP/IP protocol before being transmitted so that the treatment in detecting DoS attack in SCADA networks using the IEC 104 protocol is not much different from a traditional computer network. This study implements three machine learning approaches, i.e.: Decision Tree, Support Vector Machine, and Gaussian Naïve Bayes in creating an Intrusion Detection System (IDS) model to recognize DoS attack on the SCADA Network. Experimental results show that the performance of the Decision Tree approach has the best performance detection on the Testing dataset and Training dataset with an accuracy of 99.99% in all experiments.
{"title":"Denial of Service Attacks Detection on SCADA Network IEC 60870-5-104 using Machine Learning","authors":"M. M. Arifin, D. Stiawan, Susanto, J. Rejito, Mohd Yazid Bin Idris, R. Budiarto","doi":"10.23919/eecsi53397.2021.9624255","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624255","url":null,"abstract":"SCADA was designed to be used in an isolated area however, in modern SCADA, its connection to the Internet has become essential due to performance and commercial needs. This extended SCADA interconnection creates new vulnerabilities in the SCADA network. One of the attacks that may occur caused by the extended interconnection of SCADA networks to heterogeneous networks is Denial of Service attacks (DoS). DoS attack is launched by sending many messages from nodes. The development of easily accessible and simple DoS tools has increased the frequency of attacks. Ease of access and use of DoS tools made reduced the level of expertise needed to launch an attack. This study uses a SCADA dataset containing DoS attacks and running IEC 60870-5-104 protocol where this protocol will be encapsulated into TCP/IP protocol before being transmitted so that the treatment in detecting DoS attack in SCADA networks using the IEC 104 protocol is not much different from a traditional computer network. This study implements three machine learning approaches, i.e.: Decision Tree, Support Vector Machine, and Gaussian Naïve Bayes in creating an Intrusion Detection System (IDS) model to recognize DoS attack on the SCADA Network. Experimental results show that the performance of the Decision Tree approach has the best performance detection on the Testing dataset and Training dataset with an accuracy of 99.99% in all experiments.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133251875","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 : 2021-08-23DOI: 10.23919/eecsi53397.2021.9624291
Moontasir Rafique, Abdullah Alavi, Aadnan Farhad, M. T. Kawser
Non-Orthogonal Multiple Access (NOMA) is a popular solution for supporting a high number of users and along with significant bandwidth in 5G cellular communication. By using a technique called cooperative relaying, the same data is sent to all the users, and one user can relay data to the other. In order to provide enough power for the users, energy harvesting techniques have been introduced with Simultaneous Wireless Information and Power Transfer (SWIPT) coming to prominence in recent times. In this paper, analysis has been made comparing two different power allocation schemes in NOMA, Fixed Power allocation Scheme (FPS) and Dynamic Power allocation Scheme (DPS). The comparisons were made in terms of their performance and characteristics while undergoing SWIPT. It has been found that by using DPS, an almost 25% increase in peak spectral efficiency can be obtained compared to FPS. However, DPS suffers from a higher outage probability as the increase of power causes the signal bandwidth to drop below the target rate a significant number of times. Based on the detailed results, conclusions were drawn as to which power allocation coefficient scheme would be used in real-time and non-real time communication standards, respectively. The results suggest that for real-time communication, FPS is more suitable while for non-real time communication, DPS appears to work better than FPS.
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