Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9921991
Muhammad Naveed, Syed Muhammad Usman, Muhammad Islam Satti, Sama Aleshaiker, Aamir Anwar
An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures.
{"title":"Intrusion Detection in Smart IoT Devices for People with Disabilities","authors":"Muhammad Naveed, Syed Muhammad Usman, Muhammad Islam Satti, Sama Aleshaiker, Aamir Anwar","doi":"10.1109/ISC255366.2022.9921991","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921991","url":null,"abstract":"An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126680334","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922346
Sheila Maulida Intani, B. I. Nasution, M. E. Aminanto, Y. Nugraha, Nurhaya Muchtar, J. Kanggrawan
The DKI Jakarta provincial government is ready to support the digital transformation program with a series of digitally integrated policies. Residents of DKI Jakarta can now easily submit complaints about problems in their surrounding environment through the JakLapor service feature on the JAKI application. However, incoming reports are still manually classified. As a result, many citizens still report unsuitable complaints based on their category. This research aims to compare and find the best complaint classification model by applying multiple machine learning models to classify texts automatically. We also use feature extraction to see which model performs the best. This study employed Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms as the machine learning model. Meanwhile, we use Count Vectorizer, Terms Frequency-Inverse Document Frequency (TF-IDF), N-Gram, and Latent Semantic Analysis (LSA) as the feature extraction algorithms. The classification results show that the Random Forest method model with TFIDF feature extraction is the most accurate and optimal model among the others, with a 90% accuracy rate.
{"title":"Automating Public Complaint Classification Through JakLapor Channel: A Case Study of Jakarta, Indonesia","authors":"Sheila Maulida Intani, B. I. Nasution, M. E. Aminanto, Y. Nugraha, Nurhaya Muchtar, J. Kanggrawan","doi":"10.1109/ISC255366.2022.9922346","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922346","url":null,"abstract":"The DKI Jakarta provincial government is ready to support the digital transformation program with a series of digitally integrated policies. Residents of DKI Jakarta can now easily submit complaints about problems in their surrounding environment through the JakLapor service feature on the JAKI application. However, incoming reports are still manually classified. As a result, many citizens still report unsuitable complaints based on their category. This research aims to compare and find the best complaint classification model by applying multiple machine learning models to classify texts automatically. We also use feature extraction to see which model performs the best. This study employed Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms as the machine learning model. Meanwhile, we use Count Vectorizer, Terms Frequency-Inverse Document Frequency (TF-IDF), N-Gram, and Latent Semantic Analysis (LSA) as the feature extraction algorithms. The classification results show that the Random Forest method model with TFIDF feature extraction is the most accurate and optimal model among the others, with a 90% accuracy rate.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133541346","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922067
Daniel Mejia, N. Villanueva-Rosales
Data is a crucial factor for monitoring and understanding events related to Smart Cities. Data can be discovered and integrated from different sources and has the potential to be interpreted in multiple ways. Traffic crashes, for example, are common events that occur in cities. A significant amount of historical data related to traffic crashes is publicly available for analysis and can be used by a wide range of stakeholders. Measuring the impact of Smart Cities solutions usually relies on data collection, analysis, and metrics before and after such solutions are implemented. This paper presents an observable data-driven bottom-up methodology to create the Critical Composite Index (CCI), a Key Performance Indicator developed to measure traffic crash severity as a singular value. The CCI can be used by both domain experts and non-domain experts to be informed about traffic crashes on the roadways. This paper the development of the CCI using historical, government agency reported, and publicly accessible traffic crash data. The CCI can be modified or extended to align with specific reporting traffic crash criteria by modifying the weights of traffic crash features. The observable data-driven bottom-up methodology development enables the transformation of raw data into a metric that can contribute to the observability of Smart Cities.
{"title":"Data-Driven Metrics Applied to Traffic Crashes to Improve Observability in Smart Cities","authors":"Daniel Mejia, N. Villanueva-Rosales","doi":"10.1109/ISC255366.2022.9922067","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922067","url":null,"abstract":"Data is a crucial factor for monitoring and understanding events related to Smart Cities. Data can be discovered and integrated from different sources and has the potential to be interpreted in multiple ways. Traffic crashes, for example, are common events that occur in cities. A significant amount of historical data related to traffic crashes is publicly available for analysis and can be used by a wide range of stakeholders. Measuring the impact of Smart Cities solutions usually relies on data collection, analysis, and metrics before and after such solutions are implemented. This paper presents an observable data-driven bottom-up methodology to create the Critical Composite Index (CCI), a Key Performance Indicator developed to measure traffic crash severity as a singular value. The CCI can be used by both domain experts and non-domain experts to be informed about traffic crashes on the roadways. This paper the development of the CCI using historical, government agency reported, and publicly accessible traffic crash data. The CCI can be modified or extended to align with specific reporting traffic crash criteria by modifying the weights of traffic crash features. The observable data-driven bottom-up methodology development enables the transformation of raw data into a metric that can contribute to the observability of Smart Cities.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"27 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131805771","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922077
Flores Tinoco, Moisés Ariste, Aspilcueta Narvaez, Alvaro Martín, Rojas Povis, Carlos Enrique, Rojas Garcia, Piero Sebastian, Zarate Torres, Dennis Joel
This research responds to the necessity to understand the most efficient way to produce a successful transition from a traditional university campus to a smart campus. The methodology proposed was based on a Smart Campus approach and it was applied at the FIEE UNI Library (Library of the Faculty of Electrical and Electronic Engineering of the National University of Engineering), in order to improve their services, by focusing on increasing the attention-span and well-being of the students. The result of applying this methodology is a module that contains the projects that should be prioritized for the transformation of the traditional campus into an intelligent campus; as well as the necessary indicators and variables to measure, monitor and evaluate the impact and performance of these projects. The procedure carried out is the following: after a preliminary inspection a structure is defined in order to classify the problems and the projects in a better way. Next, a methodology for the identification and prioritization of the problems is defined based on a weighing methodology where researchers define criteria and depending on the impact each problem has a score. After creating a list of problems, solutions are proposed and prioritized, in a similar way as the problems were prioritized, taking into account some criteria, such as the cost of implementation, the time of implementation, etc. Lastly, high impact and easy to implement projects based on a weighing methodology were proposed for the transformation of the library into a Smart Library following different scenarios.
{"title":"Methodology for the development of a scalable module based on a Smart Campus approach to transform a traditional library into a smart library through Indicators and planning of projects","authors":"Flores Tinoco, Moisés Ariste, Aspilcueta Narvaez, Alvaro Martín, Rojas Povis, Carlos Enrique, Rojas Garcia, Piero Sebastian, Zarate Torres, Dennis Joel","doi":"10.1109/ISC255366.2022.9922077","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922077","url":null,"abstract":"This research responds to the necessity to understand the most efficient way to produce a successful transition from a traditional university campus to a smart campus. The methodology proposed was based on a Smart Campus approach and it was applied at the FIEE UNI Library (Library of the Faculty of Electrical and Electronic Engineering of the National University of Engineering), in order to improve their services, by focusing on increasing the attention-span and well-being of the students. The result of applying this methodology is a module that contains the projects that should be prioritized for the transformation of the traditional campus into an intelligent campus; as well as the necessary indicators and variables to measure, monitor and evaluate the impact and performance of these projects. The procedure carried out is the following: after a preliminary inspection a structure is defined in order to classify the problems and the projects in a better way. Next, a methodology for the identification and prioritization of the problems is defined based on a weighing methodology where researchers define criteria and depending on the impact each problem has a score. After creating a list of problems, solutions are proposed and prioritized, in a similar way as the problems were prioritized, taking into account some criteria, such as the cost of implementation, the time of implementation, etc. Lastly, high impact and easy to implement projects based on a weighing methodology were proposed for the transformation of the library into a Smart Library following different scenarios.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128725712","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922177
Adnane Founoun, L. E. Ghazouani, A. Haqiq, A. Hayar, H. Radoine
To provide different services to smart cities, many new approaches were used, including the strategic approach and the techno-human-centric approach. When it combines both the human aspect through its sensitivities and the technical aspect of data management to enable the city's stakeholders to act better. Indeed, a new governance framework is possible through the introduction of human intelligence to answer specific questions. It's about putting citizens at the center of the smart city issue while putting a coalition with new technologies. In this paper, we suggest validation of weather data, temperature, and wind, according to citizens' perceptions through a gaming scenario. The use of this mobile platform will assist stakeholders towards a roadmap for future urban design. This will be done by building up a database of citizens' perceptions and appreciations. Also, the wide range of sites of interest will allow an urban promotion and accompany the cultural life of the city. This will be possible by inviting subscribers to go and visit these places subject to promotions.
{"title":"Bringing human perception to validate weather measurements in Smart City: Human-Techno Centric Approach","authors":"Adnane Founoun, L. E. Ghazouani, A. Haqiq, A. Hayar, H. Radoine","doi":"10.1109/ISC255366.2022.9922177","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922177","url":null,"abstract":"To provide different services to smart cities, many new approaches were used, including the strategic approach and the techno-human-centric approach. When it combines both the human aspect through its sensitivities and the technical aspect of data management to enable the city's stakeholders to act better. Indeed, a new governance framework is possible through the introduction of human intelligence to answer specific questions. It's about putting citizens at the center of the smart city issue while putting a coalition with new technologies. In this paper, we suggest validation of weather data, temperature, and wind, according to citizens' perceptions through a gaming scenario. The use of this mobile platform will assist stakeholders towards a roadmap for future urban design. This will be done by building up a database of citizens' perceptions and appreciations. Also, the wide range of sites of interest will allow an urban promotion and accompany the cultural life of the city. This will be possible by inviting subscribers to go and visit these places subject to promotions.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129444049","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921948
B. I. Nasution, Irfan Dwiki Bhaswara, Y. Nugraha, J. Kanggrawan
Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset.
{"title":"Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia","authors":"B. I. Nasution, Irfan Dwiki Bhaswara, Y. Nugraha, J. Kanggrawan","doi":"10.1109/ISC255366.2022.9921948","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921948","url":null,"abstract":"Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604074","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922326
M. Asprou, A. Akrytov, L. Hadjidemetriou, C. Charalambous, I. Ciornei, G. Ellinas, C. Panayiotou
The fast deployment of the Phasor Measurement Units (PMUs), especially in the transmission level of the power systems, enables the development of wide area monitoring, protection and control (WAMPC) applications that enhance the situational awareness of the power system operator as well as the stability of the power system. Such applications are dependent on the communication network that supports the transfer of the PMU measurements to a central monitoring application or to a local protection application (situated in a substation). It is therefore of paramount importance to ensure the transfer of measurements with the least delay, while at the same time to ensure the integrity of the PMU measurements. In this work, the impact of using a wireless communication network for transferring the PMU measurements to the WAMPC applications is examined and the advantage of the 5G communication network over 4G and 3G in such real-time monitoring and control applications is demonstrated.
{"title":"The Impact of Wireless Communication Networks on Wide Area Monitoring and Protection Applications","authors":"M. Asprou, A. Akrytov, L. Hadjidemetriou, C. Charalambous, I. Ciornei, G. Ellinas, C. Panayiotou","doi":"10.1109/ISC255366.2022.9922326","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922326","url":null,"abstract":"The fast deployment of the Phasor Measurement Units (PMUs), especially in the transmission level of the power systems, enables the development of wide area monitoring, protection and control (WAMPC) applications that enhance the situational awareness of the power system operator as well as the stability of the power system. Such applications are dependent on the communication network that supports the transfer of the PMU measurements to a central monitoring application or to a local protection application (situated in a substation). It is therefore of paramount importance to ensure the transfer of measurements with the least delay, while at the same time to ensure the integrity of the PMU measurements. In this work, the impact of using a wireless communication network for transferring the PMU measurements to the WAMPC applications is examined and the advantage of the 5G communication network over 4G and 3G in such real-time monitoring and control applications is demonstrated.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119398","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921985
Zahraa Khais Shahid, S. Saguna, C. Åhlund
The need for smart healthcare tools and techniques has increased due to the availability of low-cost IoT sensors and devices and the growing aging population in the world. Early detection of health conditions such as dementia and Parkinsons are important for treatment and medication. Out of the many symptoms of such health conditions, one critical behavior is sleep activity changes. In this paper, we evaluate and apply an unsupervised machine learning: K-Means, to detect changes in long-term sleep behavior in the bedroom using smart-home motion sensors installed in 6 real-life single resident elderly homes for approximately three years. Our method analyses the transformation of clusters for a participant over three years, 2019, 2020, and 2021. This is done using three features: duration of stay, the hour of the day, and duration frequency. Data clustering is used to group durations of being in the bedroom at different hours of the day. This is done to see if there is a shift in these clusters for elderly persons with healthy aging and those developing health conditions like dementia and Parkinsons. We foresee that such methods to detect long-term behavior changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.
{"title":"Recognizing Long-term Sleep Behaviour Change using Clustering for Elderly in Smart Homes","authors":"Zahraa Khais Shahid, S. Saguna, C. Åhlund","doi":"10.1109/ISC255366.2022.9921985","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921985","url":null,"abstract":"The need for smart healthcare tools and techniques has increased due to the availability of low-cost IoT sensors and devices and the growing aging population in the world. Early detection of health conditions such as dementia and Parkinsons are important for treatment and medication. Out of the many symptoms of such health conditions, one critical behavior is sleep activity changes. In this paper, we evaluate and apply an unsupervised machine learning: K-Means, to detect changes in long-term sleep behavior in the bedroom using smart-home motion sensors installed in 6 real-life single resident elderly homes for approximately three years. Our method analyses the transformation of clusters for a participant over three years, 2019, 2020, and 2021. This is done using three features: duration of stay, the hour of the day, and duration frequency. Data clustering is used to group durations of being in the bedroom at different hours of the day. This is done to see if there is a shift in these clusters for elderly persons with healthy aging and those developing health conditions like dementia and Parkinsons. We foresee that such methods to detect long-term behavior changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128257958","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922020
Shainen M. Davidson, Kenton White
Origin and Destination (O&D) studies provide invaluable information for planning transportation infrastructure; however, they require very large sample sizes, and thus are becoming increasingly expensive as response rates to traditional surveys fall. At the same time, adoption of social media is on the rise. This study examines using social media data to replace traditional survey data to construct an O&D study. Specifically, with the cooperation of Quebec City's public transit provider, an online based O&D study was conducted of Quebec City. The results are compared with a Quebec City O&D survey conducted in 2011 which used traditional methods.
{"title":"Using Twitter data to conduct an Origin and Destination study of Quebec City","authors":"Shainen M. Davidson, Kenton White","doi":"10.1109/ISC255366.2022.9922020","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922020","url":null,"abstract":"Origin and Destination (O&D) studies provide invaluable information for planning transportation infrastructure; however, they require very large sample sizes, and thus are becoming increasingly expensive as response rates to traditional surveys fall. At the same time, adoption of social media is on the rise. This study examines using social media data to replace traditional survey data to construct an O&D study. Specifically, with the cooperation of Quebec City's public transit provider, an online based O&D study was conducted of Quebec City. The results are compared with a Quebec City O&D survey conducted in 2011 which used traditional methods.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458556","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922102
Nauman Nazar, Iman Darvishi, Abel Yeboah-Ofori
The impact of COVID-19 pandemic affected the whole world leading to threats to the healthcare, economies, governments, and education sectors. During this challenging period, online learning and educational tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex gained immense popularity in academic institutions. However, these tools provided vulnerabilities for malicious attackers to exploit these online platforms. That posed a huge cyber threat to the online educational system to continue and survive under such circumstances. The paper aims to explore and analyze the cyber threats to these online learning platforms to understand the security posture and mitigation techniques. The contribution of this paper is threefold: First, we explore the various attacks on online tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex and determine how much security and privacy they offer. Secondly, we analyze the encryption's capabilities to assess the level of confidentiality, integrity, and availability they provide to the users and present the results as a table. Finally, we discussed a common vulnerability framework comprising common threats faced by users and the service provider for the mitigation techniques to improve security.
{"title":"Cyber Threat Analysis on Online Learning and Its Mitigation Techniques Amid Covid-19","authors":"Nauman Nazar, Iman Darvishi, Abel Yeboah-Ofori","doi":"10.1109/ISC255366.2022.9922102","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922102","url":null,"abstract":"The impact of COVID-19 pandemic affected the whole world leading to threats to the healthcare, economies, governments, and education sectors. During this challenging period, online learning and educational tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex gained immense popularity in academic institutions. However, these tools provided vulnerabilities for malicious attackers to exploit these online platforms. That posed a huge cyber threat to the online educational system to continue and survive under such circumstances. The paper aims to explore and analyze the cyber threats to these online learning platforms to understand the security posture and mitigation techniques. The contribution of this paper is threefold: First, we explore the various attacks on online tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex and determine how much security and privacy they offer. Secondly, we analyze the encryption's capabilities to assess the level of confidentiality, integrity, and availability they provide to the users and present the results as a table. Finally, we discussed a common vulnerability framework comprising common threats faced by users and the service provider for the mitigation techniques to improve security.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122425865","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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