Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051202
R. O. Oveh, G. Aziken
Social engineering is a security concern that can be mitigated most efficiently from the most neglected aspect in the security ecosystem which is humans. Technological advancement focused at devices cannot prevent psychological human manipulation. This paper sort to determine the security practices and disposition of humans in a situation of vulnerability to social engineering attacks. Interview was used for data collection. 70 persons were interviewed using structured questions. The result showed that being a former victim of social engineering activity is not enough to prevent being another victim which is a consequence of security practices by the human. It is recommended that security practices against social engineering should be institutionalised in everyday human living.
{"title":"Mitigating Social Engineering Attack: A Focus on the Weak Human Link","authors":"R. O. Oveh, G. Aziken","doi":"10.1109/ITED56637.2022.10051202","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051202","url":null,"abstract":"Social engineering is a security concern that can be mitigated most efficiently from the most neglected aspect in the security ecosystem which is humans. Technological advancement focused at devices cannot prevent psychological human manipulation. This paper sort to determine the security practices and disposition of humans in a situation of vulnerability to social engineering attacks. Interview was used for data collection. 70 persons were interviewed using structured questions. The result showed that being a former victim of social engineering activity is not enough to prevent being another victim which is a consequence of security practices by the human. It is recommended that security practices against social engineering should be institutionalised in everyday human living.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051398
A. Aniedu, Sandra C. Nwokoye, Chukwunenye S. Okafor, Kingley U. Anyanwu
Non-technical losses (NTL) have rightly been identified as losses arising from energy generated but unaccounted for. They basically occur because of theft and other fraudulent activities surrounding illegal consumption of energy. This loss accounts for massive loss in revenue to utility companies and government and there has been concerted efforts to mitigate such abnormalities thereby saving cost. Although advanced metering infrastructure (AMI) incorporating smart meters have provided some basic organization around management of smart grids and monitoring usage information, it still fails to accurately detect NTL. In this paper therefore a solution to NTL is presented involving the deployment of support vector machines (SVM) as an underlying classifier and integrated with a real-time application interface termed Electricity Usage Classifier interface (ELUCI) for monitoring and pre-processing instantaneous electricity usage time-series data. With this configuration, a classification accuracy of 98.48% was achieved which was a 17.02% improvement over the initial classification models and with a root mean squared error (RMSE) of 0.0894 and an f-measure of 0.979. The developed system can assist governments and utilities to actively monitor and track down energy theft thereby improving revenue and avoiding economic wastages accruing from these activities.
{"title":"Modelling Machine Learning-based Energy Loss Detection and Monitoring System for Advanced Metering Infrastructure","authors":"A. Aniedu, Sandra C. Nwokoye, Chukwunenye S. Okafor, Kingley U. Anyanwu","doi":"10.1109/ITED56637.2022.10051398","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051398","url":null,"abstract":"Non-technical losses (NTL) have rightly been identified as losses arising from energy generated but unaccounted for. They basically occur because of theft and other fraudulent activities surrounding illegal consumption of energy. This loss accounts for massive loss in revenue to utility companies and government and there has been concerted efforts to mitigate such abnormalities thereby saving cost. Although advanced metering infrastructure (AMI) incorporating smart meters have provided some basic organization around management of smart grids and monitoring usage information, it still fails to accurately detect NTL. In this paper therefore a solution to NTL is presented involving the deployment of support vector machines (SVM) as an underlying classifier and integrated with a real-time application interface termed Electricity Usage Classifier interface (ELUCI) for monitoring and pre-processing instantaneous electricity usage time-series data. With this configuration, a classification accuracy of 98.48% was achieved which was a 17.02% improvement over the initial classification models and with a root mean squared error (RMSE) of 0.0894 and an f-measure of 0.979. The developed system can assist governments and utilities to actively monitor and track down energy theft thereby improving revenue and avoiding economic wastages accruing from these activities.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051518
Maryam Abdullahi Musa, A. Gital, Kabiru Musa Ibrahim, H. Chiroma, M. Abdulrahman, Ibrahim Muhammad Umar
The importance of the internet across the globe cannot be over-emphasized as such network security is essential to curb future attack occurrences. Cyber-attacks like DDoS and Ransomware yielded a lot of damage to connected devices by endangering and accessing them, notwithstanding these damages are air marked to be on the rise. To overcome these issues, machine learning has been used in different computing aspects such as cyber–Intrusion Detection. Recently, deep learning, extreme learning, and deep extreme learning networks have superseded machine learning in this context due to their iterative hidden layers that can manipulate complex features of cyber intrusion data. Hence, this research surveys the application of data-driven intelligent algorithms for cyber security attack detection in comparison to conventional machine learning techniques. The review focuses on the performance evaluation of several state-of-the-art intelligent algorithms and provides research gaps and future 2trends in the context of Data Security Attacks and Cyber Intrusion Detection.
{"title":"A Review of Data-Driven Approaches with Emphasis on Machine Learning Base Intrusion Detection Algorithms","authors":"Maryam Abdullahi Musa, A. Gital, Kabiru Musa Ibrahim, H. Chiroma, M. Abdulrahman, Ibrahim Muhammad Umar","doi":"10.1109/ITED56637.2022.10051518","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051518","url":null,"abstract":"The importance of the internet across the globe cannot be over-emphasized as such network security is essential to curb future attack occurrences. Cyber-attacks like DDoS and Ransomware yielded a lot of damage to connected devices by endangering and accessing them, notwithstanding these damages are air marked to be on the rise. To overcome these issues, machine learning has been used in different computing aspects such as cyber–Intrusion Detection. Recently, deep learning, extreme learning, and deep extreme learning networks have superseded machine learning in this context due to their iterative hidden layers that can manipulate complex features of cyber intrusion data. Hence, this research surveys the application of data-driven intelligent algorithms for cyber security attack detection in comparison to conventional machine learning techniques. The review focuses on the performance evaluation of several state-of-the-art intelligent algorithms and provides research gaps and future 2trends in the context of Data Security Attacks and Cyber Intrusion Detection.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121433151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051442
Felicia Cletus, B. Y. Baha, O. Sarjiyus
Mosquito is a disease-causing organism that causes harm to humans and animals alike. Over the years, vector control measures such as the use of insecticides, treated mosquito net, and the prediction of mosquito prevalence using statistical tools and Artificial Neural Network models in different weather terrain have not completely eradicated the problems associated with prevalence of mosquito. More so, there is no research available in literature to predict the prevalence of mosquito using artificial neural network in a warm semi-arid climate such as that of Yola, Northeastern Nigeria. This research endeavored to achieve this aim. This study built a prototype artificial neural network model that is capable of predicting mosquito prevalence. The model is a feed forward multi-layer perceptron that was implemented using the supervised learning method and optimized using the back propagation algorithm. The model has four (4) input features, which are weather data (maximum temperature, minimum temperature, relative humidity and rainfall) which were adopted for the research. After compilation, the new model was trained and validated using sourced data by the researcher. To train the model, 80% of the data was used while 20% was used for the validation. The proposed model is a keras sequential classification model that was built in anaconda using the python programming language. The optimal model has three hidden layers of 40 30 and 20 neurons with Sigmoid and ReLu activation function respectively. The simulation of the prototype model recorded 96.67% accuracy with good fit. This research shows that the artificial neural network model is an effective tool in predicting mosquito prevalence in a warm semi-arid climatic region and thus recommends the use of more data and training epochs to increase accuracy and subsequent implementation of the model in real life for prediction of mosquito prevalence.
{"title":"Prediction of Mosquito Prevalence in a Warm Semi-Arid Climate using Artificial Neural Network (ANN)","authors":"Felicia Cletus, B. Y. Baha, O. Sarjiyus","doi":"10.1109/ITED56637.2022.10051442","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051442","url":null,"abstract":"Mosquito is a disease-causing organism that causes harm to humans and animals alike. Over the years, vector control measures such as the use of insecticides, treated mosquito net, and the prediction of mosquito prevalence using statistical tools and Artificial Neural Network models in different weather terrain have not completely eradicated the problems associated with prevalence of mosquito. More so, there is no research available in literature to predict the prevalence of mosquito using artificial neural network in a warm semi-arid climate such as that of Yola, Northeastern Nigeria. This research endeavored to achieve this aim. This study built a prototype artificial neural network model that is capable of predicting mosquito prevalence. The model is a feed forward multi-layer perceptron that was implemented using the supervised learning method and optimized using the back propagation algorithm. The model has four (4) input features, which are weather data (maximum temperature, minimum temperature, relative humidity and rainfall) which were adopted for the research. After compilation, the new model was trained and validated using sourced data by the researcher. To train the model, 80% of the data was used while 20% was used for the validation. The proposed model is a keras sequential classification model that was built in anaconda using the python programming language. The optimal model has three hidden layers of 40 30 and 20 neurons with Sigmoid and ReLu activation function respectively. The simulation of the prototype model recorded 96.67% accuracy with good fit. This research shows that the artificial neural network model is an effective tool in predicting mosquito prevalence in a warm semi-arid climatic region and thus recommends the use of more data and training epochs to increase accuracy and subsequent implementation of the model in real life for prediction of mosquito prevalence.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121376706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051523
Segun Akintunde, O. R. Vincent, Oreoluwa Tinubu
The electronic auction system has emerged as one of the leading electronic commerce platforms where auctioneers and bidders converge for transactions. With the Internet's proliferation, e-commerce systems' functionalities have greatly been enhanced. Unfortunately, fraudulent activities increasingly hamper the credibility of online auction systems. Shill Bidding is one of the prominent frauds in the e-auction. Due to its similarity with normal bidding behaviour, it is challenging to detect as legitimate bidders could be categorized as fraudulent and vice versa. Several authentic auctioneers have been cheated during online bidding systems because of the diverse ways shill bidding is being perpetrated. It is, therefore, essential to improve the credibility of online bidding systems. In this study, we proposed a machine learning-based prediction system that determines the likelihood of a customer/seller perpetrating shill bidding. Upon deployment, the proposed system would prevent shill bidders from participating in a car action system. A vote ensemble model is trained with public data of 12 attributes comprising Random Forest, Decision Tree, Multi-layer Perception (MLP), and Sequential Maximal Optimization (SMO) base learners. An object-oriented Python programming language is used to implement the shill bidding predictive system. Experimental results show the excellence of the proposed system using metrics such as Precision, Accuracy, Recall, F1-score, and Misclassification error.
{"title":"An Ensemble-based Shill Bidding Prediction Model in Car *Auction System","authors":"Segun Akintunde, O. R. Vincent, Oreoluwa Tinubu","doi":"10.1109/ITED56637.2022.10051523","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051523","url":null,"abstract":"The electronic auction system has emerged as one of the leading electronic commerce platforms where auctioneers and bidders converge for transactions. With the Internet's proliferation, e-commerce systems' functionalities have greatly been enhanced. Unfortunately, fraudulent activities increasingly hamper the credibility of online auction systems. Shill Bidding is one of the prominent frauds in the e-auction. Due to its similarity with normal bidding behaviour, it is challenging to detect as legitimate bidders could be categorized as fraudulent and vice versa. Several authentic auctioneers have been cheated during online bidding systems because of the diverse ways shill bidding is being perpetrated. It is, therefore, essential to improve the credibility of online bidding systems. In this study, we proposed a machine learning-based prediction system that determines the likelihood of a customer/seller perpetrating shill bidding. Upon deployment, the proposed system would prevent shill bidders from participating in a car action system. A vote ensemble model is trained with public data of 12 attributes comprising Random Forest, Decision Tree, Multi-layer Perception (MLP), and Sequential Maximal Optimization (SMO) base learners. An object-oriented Python programming language is used to implement the shill bidding predictive system. Experimental results show the excellence of the proposed system using metrics such as Precision, Accuracy, Recall, F1-score, and Misclassification error.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051450
Ibrahim Hadiza Ndanusa, Solomon Adelowo Adepoju, Adeniyi Oluwaseun Ojerinde
Considering the growth of the credit businesses, machine learning models for granting loan permissions with the minimum amount of risk are becoming increasingly popular among banking sectors. Machine Learning based models has proven to be useful in resolving a variety of banking risk prediction issues. ML Predictions are sometimes unfair and biased because they are heavily dependent on randomly selected training data sample for every prediction made. However, this problem can be address by utilizing a cross-validation strategy. Prediction can be improved by combining decisions from different machine learning algorithms (ensemble decision making). The proposed consensus-based prediction model is evaluated using standard performance metrics, and the proposed model achieved an accuracy of 83 percent.
{"title":"Consensus Based Bank Loan Prediction Model Using Aggregated Decision Making and Cross Fold Validation Techniques","authors":"Ibrahim Hadiza Ndanusa, Solomon Adelowo Adepoju, Adeniyi Oluwaseun Ojerinde","doi":"10.1109/ITED56637.2022.10051450","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051450","url":null,"abstract":"Considering the growth of the credit businesses, machine learning models for granting loan permissions with the minimum amount of risk are becoming increasingly popular among banking sectors. Machine Learning based models has proven to be useful in resolving a variety of banking risk prediction issues. ML Predictions are sometimes unfair and biased because they are heavily dependent on randomly selected training data sample for every prediction made. However, this problem can be address by utilizing a cross-validation strategy. Prediction can be improved by combining decisions from different machine learning algorithms (ensemble decision making). The proposed consensus-based prediction model is evaluated using standard performance metrics, and the proposed model achieved an accuracy of 83 percent.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124792171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051302
S. Owoeye, F. Durodola, Adedayo Akinade, Ahmad Alkali, Olaitan Olaonipekun
Road travelling is among the simplest modes of transportation. Accidents are frequently the consequence of a human error on the road, and they can occasionally be brought on by alcohol intake which alters the victim's way of thinking. Law enforcement agencies have made significant efforts to lower the risk of drunk driving, but none have been able to significantly diminish it. Due to this, the proposed system was developed to lessen the likelihood of accidents on our roads being brought on by intoxicated drivers. The device can prevent a drunk driver from operating the vehicle and, in the event of an accident, send a message to a pre-programmed number informing it of the location of the vehicle. The entire software is built around a microcontroller, an alcohol sensor, and a vibration sensor. The sensor is used to set an alcohol threshold at which an alarm will buzz, and when the set threshold is exceeded, the flow of fuel to the engine will cease, thereby bringing the car to a halt. In case of an auto crash, the microcontroller would receive input from the attached vibration sensor, and send the location of the vehicle to a pre-registered phone number on the subscriber identification module (SIM) of the project. This project is a prototype of what is proposed in a vehicle where the DC motor serves as the fuel pump.
{"title":"Development of Alcohol Detection with Engine Locking and Short Messaging Service Tracking System","authors":"S. Owoeye, F. Durodola, Adedayo Akinade, Ahmad Alkali, Olaitan Olaonipekun","doi":"10.1109/ITED56637.2022.10051302","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051302","url":null,"abstract":"Road travelling is among the simplest modes of transportation. Accidents are frequently the consequence of a human error on the road, and they can occasionally be brought on by alcohol intake which alters the victim's way of thinking. Law enforcement agencies have made significant efforts to lower the risk of drunk driving, but none have been able to significantly diminish it. Due to this, the proposed system was developed to lessen the likelihood of accidents on our roads being brought on by intoxicated drivers. The device can prevent a drunk driver from operating the vehicle and, in the event of an accident, send a message to a pre-programmed number informing it of the location of the vehicle. The entire software is built around a microcontroller, an alcohol sensor, and a vibration sensor. The sensor is used to set an alcohol threshold at which an alarm will buzz, and when the set threshold is exceeded, the flow of fuel to the engine will cease, thereby bringing the car to a halt. In case of an auto crash, the microcontroller would receive input from the attached vibration sensor, and send the location of the vehicle to a pre-registered phone number on the subscriber identification module (SIM) of the project. This project is a prototype of what is proposed in a vehicle where the DC motor serves as the fuel pump.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114518252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051543
A. Imoize, H. I. Obakhena, F. I. Anyasi, J. Isabona, Stephen Ojo, N. Faruk
Reconfigurable intelligent surfaces are rapidly emerging as candidate technology to support massive connectivity in the envisioned 6G wireless networks. By proactively reconfiguring the propagation space into a smart, programmable, and truly controllable entity via an array of inexpensive passive reflecting elements, a robust improvement in the spectrum and/or energy efficiency (EE) of wireless communication systems can be realized. This paper presents state-of-the-art solutions for some critical aspects of RIS-empowered wireless communication systems. In particular, an exposition on the fundamentals of RIS technology, including its structure and operation, competitive advantages over existing frameworks, system models, and potential use cases in wireless communication systems are broached. Last, a few open research issues and key takeaway lessons are highlighted.
{"title":"Reconfigurable Intelligent Surfaces Enabling 6G Wireless Communication Systems: Use Cases and Technical Considerations","authors":"A. Imoize, H. I. Obakhena, F. I. Anyasi, J. Isabona, Stephen Ojo, N. Faruk","doi":"10.1109/ITED56637.2022.10051543","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051543","url":null,"abstract":"Reconfigurable intelligent surfaces are rapidly emerging as candidate technology to support massive connectivity in the envisioned 6G wireless networks. By proactively reconfiguring the propagation space into a smart, programmable, and truly controllable entity via an array of inexpensive passive reflecting elements, a robust improvement in the spectrum and/or energy efficiency (EE) of wireless communication systems can be realized. This paper presents state-of-the-art solutions for some critical aspects of RIS-empowered wireless communication systems. In particular, an exposition on the fundamentals of RIS technology, including its structure and operation, competitive advantages over existing frameworks, system models, and potential use cases in wireless communication systems are broached. Last, a few open research issues and key takeaway lessons are highlighted.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122552065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051520
Kefas Yunana, I. O. Oyefolahan, S. Bashir
Critical Infrastructure (CI) are nowadays linked with IOT devices that communicate data through networks to achieve significant collaboration. With the progress in internet connectivity, IOT has disrupt numerous aspects of CI comprising communication systems, power plants, power grid, gas pipeline, and transportation systems. As a disruptive paradigm, the IOT and Cloud computing utilizing Smart IOT devices equipped with numerous sensors and actuating capabilities play significant roles when deployed in CI surroundings with the aim of monitoring vital observable figures consisting of flow rate, temperature, pressure, and lighting situations. Over the years, oil pipeline infrastructure have been the main economic means for conveying refined oil to assembly and distribution outlets. Though damages to the pipelines in this area by exclusion have influence the normal transport of refined oil to the outlets across the country like Nigeria which has influence the stream of income and damages to the environment. Reinforcement Learning (RL) approach for infrastructure reliability monitoring have receive numerous consideration by researchers denoting that RL centered policy reveals superior operation than regular traditional control systems strategies. Many of the studies utilised mainly algorithms for environment with discrete action and observation spaces unlike others with infinite state space. This study proposed a framework for critical infrastructure monitoring based on Deep Reinforcement Learning (DRL) for oil pipeline network and also developed a pipeline network monitoring (PNM) architecture with expression of the environment dynamics as Markov Decision Process. The sample observation space data and strategy for evaluation of the framework was also presented.
{"title":"A Framework For Critical Infrastructure Monitoring Based On Deep Reinforcement Learning Approach","authors":"Kefas Yunana, I. O. Oyefolahan, S. Bashir","doi":"10.1109/ITED56637.2022.10051520","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051520","url":null,"abstract":"Critical Infrastructure (CI) are nowadays linked with IOT devices that communicate data through networks to achieve significant collaboration. With the progress in internet connectivity, IOT has disrupt numerous aspects of CI comprising communication systems, power plants, power grid, gas pipeline, and transportation systems. As a disruptive paradigm, the IOT and Cloud computing utilizing Smart IOT devices equipped with numerous sensors and actuating capabilities play significant roles when deployed in CI surroundings with the aim of monitoring vital observable figures consisting of flow rate, temperature, pressure, and lighting situations. Over the years, oil pipeline infrastructure have been the main economic means for conveying refined oil to assembly and distribution outlets. Though damages to the pipelines in this area by exclusion have influence the normal transport of refined oil to the outlets across the country like Nigeria which has influence the stream of income and damages to the environment. Reinforcement Learning (RL) approach for infrastructure reliability monitoring have receive numerous consideration by researchers denoting that RL centered policy reveals superior operation than regular traditional control systems strategies. Many of the studies utilised mainly algorithms for environment with discrete action and observation spaces unlike others with infinite state space. This study proposed a framework for critical infrastructure monitoring based on Deep Reinforcement Learning (DRL) for oil pipeline network and also developed a pipeline network monitoring (PNM) architecture with expression of the environment dynamics as Markov Decision Process. The sample observation space data and strategy for evaluation of the framework was also presented.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127295479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ited56637.2022.10051618
Alabi Ayodele John, Olulope Paul Kehinde, F. Ibikunle, Kareem Sunday Babatunde
The reliability of Omu-Aran 132/33kV Transmission Station, Omu-Aran Kwara State, and its associated 33kV feeders was investigated in this study. A situation awareness model was developed by considering customer-based and system-based reliability indices for the transmission station feeders between January 2015 - December 2020. Customer-based indices like System average Interruption frequency index (SAIFI), System average interruption duration index (SAIDI), Customer average interruption duration index (CAIDI), and Average service availability index (ASAI) were calculated for the station's outgoing 33kV feeders. System-based indices like failure rate, mean time between failure (MTBF), and Mean down Time (MDT) were also determined for the 33kV feeders. Results of the analysis showed that Otun 33kV feeder has the least mean SAIFI and SAIDI values of 0.0963interuption/customer and 0.2291hours/customer respectively. Customers on this feeder experience the least number of interruptions and the least duration of the sustained interruption. Omu-Aran 33kV feeder has the least CAIDI of 1.7809 interruptions/customer. Customers on this feeder experience the least number of continuous interruptions. Omu-Aran 33kV feeder also has the highest mean ASAI of 0.8359 and Isanlu-Isin 33kV feeder has the least mean ASAI of 0.5943 and requires special attention to improve supply availability to the customers on this feeder.
{"title":"Reliability Assessment of Omu-Aran 132/33kV Transmission Substation feeders","authors":"Alabi Ayodele John, Olulope Paul Kehinde, F. Ibikunle, Kareem Sunday Babatunde","doi":"10.1109/ited56637.2022.10051618","DOIUrl":"https://doi.org/10.1109/ited56637.2022.10051618","url":null,"abstract":"The reliability of Omu-Aran 132/33kV Transmission Station, Omu-Aran Kwara State, and its associated 33kV feeders was investigated in this study. A situation awareness model was developed by considering customer-based and system-based reliability indices for the transmission station feeders between January 2015 - December 2020. Customer-based indices like System average Interruption frequency index (SAIFI), System average interruption duration index (SAIDI), Customer average interruption duration index (CAIDI), and Average service availability index (ASAI) were calculated for the station's outgoing 33kV feeders. System-based indices like failure rate, mean time between failure (MTBF), and Mean down Time (MDT) were also determined for the 33kV feeders. Results of the analysis showed that Otun 33kV feeder has the least mean SAIFI and SAIDI values of 0.0963interuption/customer and 0.2291hours/customer respectively. Customers on this feeder experience the least number of interruptions and the least duration of the sustained interruption. Omu-Aran 33kV feeder has the least CAIDI of 1.7809 interruptions/customer. Customers on this feeder experience the least number of continuous interruptions. Omu-Aran 33kV feeder also has the highest mean ASAI of 0.8359 and Isanlu-Isin 33kV feeder has the least mean ASAI of 0.5943 and requires special attention to improve supply availability to the customers on this feeder.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132930167","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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