Pub Date : 2019-08-01DOI: 10.1109/FiCloud.2019.00023
Yusuf Muhammad Tukur, D. Thakker, I. Awan
The Internet of Things (IoT) is a huge, global, distributed network of interlinked heterogeneous devices armed with embedded sensors, actuators and processors enabling them to connect to the internet, exchange data, communicate and interact seamlessly with one another in real time. It enjoys wide range of applications where it holds important data, hence becoming exposed to varying degrees of security and privacy threats which require careful architectural design to address. Consequently, we present in this paper a multi-layer security aware IoT architecture that aims to secure the entire system by providing security at all layers to ensure secure data collection, transfer, analysis, storage and usage. That is necessary because each layer of the IoT has vulnerabilities and a successful attack on any layer can have far-reaching impact on the whole system. We then proposed a novel security algorithm to protect the IoT system against Denial of Service (DoS) attacks at the application layer, the weakest link in IoT. The algorithm logs record of user activities and actions at given times of the day which it employs to regulate access and prevent DoS attacks. Finally, we evaluated our proposed approach against IoT security and privacy requirements, and it demonstrated better level of security.
{"title":"Multi-layer Approach to Internet of Things (IoT) Security","authors":"Yusuf Muhammad Tukur, D. Thakker, I. Awan","doi":"10.1109/FiCloud.2019.00023","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00023","url":null,"abstract":"The Internet of Things (IoT) is a huge, global, distributed network of interlinked heterogeneous devices armed with embedded sensors, actuators and processors enabling them to connect to the internet, exchange data, communicate and interact seamlessly with one another in real time. It enjoys wide range of applications where it holds important data, hence becoming exposed to varying degrees of security and privacy threats which require careful architectural design to address. Consequently, we present in this paper a multi-layer security aware IoT architecture that aims to secure the entire system by providing security at all layers to ensure secure data collection, transfer, analysis, storage and usage. That is necessary because each layer of the IoT has vulnerabilities and a successful attack on any layer can have far-reaching impact on the whole system. We then proposed a novel security algorithm to protect the IoT system against Denial of Service (DoS) attacks at the application layer, the weakest link in IoT. The algorithm logs record of user activities and actions at given times of the day which it employs to regulate access and prevent DoS attacks. Finally, we evaluated our proposed approach against IoT security and privacy requirements, and it demonstrated better level of security.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125332275","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00064
M. Greguš, O. Liskevych, K. Obelovska, Roman S. Panchyshyn
The modified algorithm for constructing routing tables based on the open shortest path first protocol is proposed. It takes into account three criteria: the channels capacity, the number of intermediate nodes on the path and signal propagation delay. The integral criterion to minimize the total relative deviation, calculated on all three criteria, obtained from optimal values as a result of optimization according to separate criteria is proposed. In addition, it is proposed to use weight coefficients for each criterion. The integral criterion includes the normalized values of the ordinary criteria and their weights. The integral criteria must be determined for each metric. The path to be added to the resulting routing table is the one with the best integral criterion.
{"title":"Packet Routing Based on Integral Normalized Criterion","authors":"M. Greguš, O. Liskevych, K. Obelovska, Roman S. Panchyshyn","doi":"10.1109/FiCloud.2019.00064","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00064","url":null,"abstract":"The modified algorithm for constructing routing tables based on the open shortest path first protocol is proposed. It takes into account three criteria: the channels capacity, the number of intermediate nodes on the path and signal propagation delay. The integral criterion to minimize the total relative deviation, calculated on all three criteria, obtained from optimal values as a result of optimization according to separate criteria is proposed. In addition, it is proposed to use weight coefficients for each criterion. The integral criterion includes the normalized values of the ordinary criteria and their weights. The integral criteria must be determined for each metric. The path to be added to the resulting routing table is the one with the best integral criterion.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126871463","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00054
Ftoon Abu Shaqra, R. Duwairi, M. Al-Ayyoub
Emotions are a crucial aspect of human life and the researchers have tried to build an automatic emotion recognition system that helps to provide important real-world applications. The psychologists have shown that emotions differ across culture, considering this fact, we provide and describe the first audio-visual Arabic emotional dataset which called (AVANEmo). In this work we aim to fill the gap between studies of emotion recognition for Arabic content and other languages by provided an Arabic dataset which is a major and fundamental part of build emotion recognition application. Our dataset contains 3000 clips for video and audio data, and it covers six basic emotional labels (Happy, Sad, Angry, Surprise, Disgust, Neutral). Also, we provide some baseline experiments to measure the primitive performance for automated audio and visual emotion recognition application using the AVANEmo dataset. The best accuracy that we achieved was 54.5% and 57.9% using the audio and visual data respectively. The data will be available for distribution to researchers.
{"title":"The Audio-Visual Arabic Dataset for Natural Emotions","authors":"Ftoon Abu Shaqra, R. Duwairi, M. Al-Ayyoub","doi":"10.1109/FiCloud.2019.00054","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00054","url":null,"abstract":"Emotions are a crucial aspect of human life and the researchers have tried to build an automatic emotion recognition system that helps to provide important real-world applications. The psychologists have shown that emotions differ across culture, considering this fact, we provide and describe the first audio-visual Arabic emotional dataset which called (AVANEmo). In this work we aim to fill the gap between studies of emotion recognition for Arabic content and other languages by provided an Arabic dataset which is a major and fundamental part of build emotion recognition application. Our dataset contains 3000 clips for video and audio data, and it covers six basic emotional labels (Happy, Sad, Angry, Surprise, Disgust, Neutral). Also, we provide some baseline experiments to measure the primitive performance for automated audio and visual emotion recognition application using the AVANEmo dataset. The best accuracy that we achieved was 54.5% and 57.9% using the audio and visual data respectively. The data will be available for distribution to researchers.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131355409","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00016
Umaru Adamu, I. Awan
Malware has become most popular attack vector, among which ransomware remained a threat to individuals and organisations. Ransomware main objectives is extortion by imposing some form of denial of service to either the system or system resources such as files until ransom is paid. This make ransomware different from conventional malware that seek to replicate, delete files, exhilarate data or extensively consume system resources. Unfortunately, detection approaches such as sandboxes analysis and pipelines are inadequate due to lack of luxury of being able to isolate a sample to analyse, and when this occurs is already too late for several users.Therefore, machine learning as prove its efficiency and has been used in research for malware detection. In this paper, we explore machine learning algorithms in ransomware detection. Specifically, the data set used contains 30,000 attributes which is to be use as independent variables to predict ransomware.However, since is difficult to incorporate all the attribute in the analysis, we therefore results to use five attribute to serves a proof of concept for feature selection. Then, after feature selection, we apply support vector machine algorithm of which RMSE of 0.179 was obtained and classifying ransomware with 88.2% accuracy. The Support Vector Machine has high performance in detection and classifying ransomware when compare to other machine learning classifier.
{"title":"Ransomware Prediction Using Supervised Learning Algorithms","authors":"Umaru Adamu, I. Awan","doi":"10.1109/FiCloud.2019.00016","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00016","url":null,"abstract":"Malware has become most popular attack vector, among which ransomware remained a threat to individuals and organisations. Ransomware main objectives is extortion by imposing some form of denial of service to either the system or system resources such as files until ransom is paid. This make ransomware different from conventional malware that seek to replicate, delete files, exhilarate data or extensively consume system resources. Unfortunately, detection approaches such as sandboxes analysis and pipelines are inadequate due to lack of luxury of being able to isolate a sample to analyse, and when this occurs is already too late for several users.Therefore, machine learning as prove its efficiency and has been used in research for malware detection. In this paper, we explore machine learning algorithms in ransomware detection. Specifically, the data set used contains 30,000 attributes which is to be use as independent variables to predict ransomware.However, since is difficult to incorporate all the attribute in the analysis, we therefore results to use five attribute to serves a proof of concept for feature selection. Then, after feature selection, we apply support vector machine algorithm of which RMSE of 0.179 was obtained and classifying ransomware with 88.2% accuracy. The Support Vector Machine has high performance in detection and classifying ransomware when compare to other machine learning classifier.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128323","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00011
Cristian Martín, Daniel Garrido, M. Díaz, B. Rubio
This paper presents a reliable architecture for the IoT considering multiple levels: edge, fog and cloud. This architecture can help to reduce latency and improve resiliency of IoT applications. The platform is based on a set of containerised components replicated at different levels. Fault tolerance mechanisms are provided by means of replication, the Apache Kafka framework and shadow devices. Apache Kafka is used to distribute messages along the multiple levels. Shadow devices include device states, and they can be used to avoid device interruptions using physical replication and state restoration. The architecture is also protocol-agnostic, allowing the use of different adaptors for the most common IoT protocols. A mission-critical use case is presented where this architecture can be applied. Finally, an evaluation has been carried out in order to test the feasibility of the fog infrastructure.
{"title":"From the Edge to the Cloud: Enabling Reliable IoT Applications","authors":"Cristian Martín, Daniel Garrido, M. Díaz, B. Rubio","doi":"10.1109/FiCloud.2019.00011","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00011","url":null,"abstract":"This paper presents a reliable architecture for the IoT considering multiple levels: edge, fog and cloud. This architecture can help to reduce latency and improve resiliency of IoT applications. The platform is based on a set of containerised components replicated at different levels. Fault tolerance mechanisms are provided by means of replication, the Apache Kafka framework and shadow devices. Apache Kafka is used to distribute messages along the multiple levels. Shadow devices include device states, and they can be used to avoid device interruptions using physical replication and state restoration. The architecture is also protocol-agnostic, allowing the use of different adaptors for the most common IoT protocols. A mission-critical use case is presented where this architecture can be applied. Finally, an evaluation has been carried out in order to test the feasibility of the fog infrastructure.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132238300","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00028
Benedikt Pittl, Stefan Starflinger, W. Mach, E. Schikuta
Amazon's EC2 On-Demand marketspace is the dominant platform for trading Cloud services such as virtual machines. On such platforms consumers and providers do not negotiate with each other. Instead, consumers purchase predefined virtual machines at fixed-prices and so this approach is also termed take-it-or-leave-it approach. In the last years more dynamic platforms emerged such as Amazon's spot marketspace which was relaunched at the end of 2017 - here consumers can bid for virtual machines. The recent efforts of Amazon and other Cloud providers such as VirtuStream show that dynamic trading mechanisms are a promising approach for realizing future Cloud markets. Hence, multi-round bilateral negotiations which are executed autonomously have gained popularity in the scientific community. A key challenge towards the adoption of autonomous multi-round bilateral negotiations is to ensure the integrity and transparency so that the generated agreements are legally biding. In the paper at hand we present an approach which uses a smart contract - called Bazaar-Contract - to ensure integrity and transparency during multi-round bilateral negotiations. Thereby, consumers and providers exchange offers by calling methods of the Bazaar-Contract. Moreover, Cloud referees can use these Bazaar-Contracts in order to manage penalties resulting from poor service quality. In order to prove the technical feasibility of our approach we implemented the Bazaar-Contract using Ethereum and the Inter-Planetary File System. We evaluate the economical feasibility of our approach by considering the gas costs.
{"title":"Bazaar-Contract: A Smart Contract for Binding Multi-Round Bilateral Negotiations on Cloud Markets","authors":"Benedikt Pittl, Stefan Starflinger, W. Mach, E. Schikuta","doi":"10.1109/FiCloud.2019.00028","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00028","url":null,"abstract":"Amazon's EC2 On-Demand marketspace is the dominant platform for trading Cloud services such as virtual machines. On such platforms consumers and providers do not negotiate with each other. Instead, consumers purchase predefined virtual machines at fixed-prices and so this approach is also termed take-it-or-leave-it approach. In the last years more dynamic platforms emerged such as Amazon's spot marketspace which was relaunched at the end of 2017 - here consumers can bid for virtual machines. The recent efforts of Amazon and other Cloud providers such as VirtuStream show that dynamic trading mechanisms are a promising approach for realizing future Cloud markets. Hence, multi-round bilateral negotiations which are executed autonomously have gained popularity in the scientific community. A key challenge towards the adoption of autonomous multi-round bilateral negotiations is to ensure the integrity and transparency so that the generated agreements are legally biding. In the paper at hand we present an approach which uses a smart contract - called Bazaar-Contract - to ensure integrity and transparency during multi-round bilateral negotiations. Thereby, consumers and providers exchange offers by calling methods of the Bazaar-Contract. Moreover, Cloud referees can use these Bazaar-Contracts in order to manage penalties resulting from poor service quality. In order to prove the technical feasibility of our approach we implemented the Bazaar-Contract using Ethereum and the Inter-Planetary File System. We evaluate the economical feasibility of our approach by considering the gas costs.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114328125","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00061
M. Antunes, Henrique Aguiar, D. Gomes
With the advent of smart IoT and M2M scenarios it becomes necessary to develop autonomous systems that optimize themselves with minimal human intervention. One possible method to achieve this is through Knee/elbow point estimation. Most of the time these points represent ideal compromises for parameters, methods and algorithms. However, estimating the knee/elbow point in curves is a challenging task. Our focus is on determining the ideal number of clusters autonomously. We analyse and discuss well-known knee/elbow estimators and two extensions based on the theoretical definition. The proposed methods (named AL and S methods) were evaluated against state-of-the-art estimators. The proposed methods are a viable stable solution for knee/elbow estimation.
{"title":"AL and S Methods: Two Extensions for L-Method","authors":"M. Antunes, Henrique Aguiar, D. Gomes","doi":"10.1109/FiCloud.2019.00061","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00061","url":null,"abstract":"With the advent of smart IoT and M2M scenarios it becomes necessary to develop autonomous systems that optimize themselves with minimal human intervention. One possible method to achieve this is through Knee/elbow point estimation. Most of the time these points represent ideal compromises for parameters, methods and algorithms. However, estimating the knee/elbow point in curves is a challenging task. Our focus is on determining the ideal number of clusters autonomously. We analyse and discuss well-known knee/elbow estimators and two extensions based on the theoretical definition. The proposed methods (named AL and S methods) were evaluated against state-of-the-art estimators. The proposed methods are a viable stable solution for knee/elbow estimation.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384929","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00021
N. Zingirian, Mattia Dalla Via
The paper analyzes the scenario of a logistic organization that collects customer inventory telemetries from wide area Wireless Sensor Networks, using mobile sinks installed on the vehicles used for deliveries. The paper analyzes the constraints of this scenario and proposes a distributed data collection algorithm for contextual ad-hoc network route discovery and message delivery, that combines control and telemetry data within the same messages. Simulations of the algorithm performance, based both on abstract parameters and on real data of oil & gas logistics, validate the algorithm and the application feasibility.
{"title":"Vehicular Sinks Over Wide Area Wireless Sensor Networks for Telemetry Applications in Logistics","authors":"N. Zingirian, Mattia Dalla Via","doi":"10.1109/FiCloud.2019.00021","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00021","url":null,"abstract":"The paper analyzes the scenario of a logistic organization that collects customer inventory telemetries from wide area Wireless Sensor Networks, using mobile sinks installed on the vehicles used for deliveries. The paper analyzes the constraints of this scenario and proposes a distributed data collection algorithm for contextual ad-hoc network route discovery and message delivery, that combines control and telemetry data within the same messages. Simulations of the algorithm performance, based both on abstract parameters and on real data of oil & gas logistics, validate the algorithm and the application feasibility.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068113","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00013
M. Alshahrani, I. Traoré, I. Woungang
Cyber attackers are shifting their attention from traditional computers to IoT devices for malignant activities like exposing smart homeowner private information and/or to launch botnet attacks. Like for conventional networks, the security of IoT networks rests on how properly the authentication process is done. However, unlike conventional networks, IoT infrastructure faces an uphill battle in deploying and operating strong authentication schemes because of inherent limitations on the underlying storage and computation capability. In this paper, we propose a new anonymous mutual Inter-device authentication protocol based on transient identities, incremental counter and temporary secret keys for IoT. The proposed protocol is based on symmetric cryptography and somehow follows the ZigBee protocol. It allows IoT devices to anonymously and mutually authenticate in an unlinkable and untraceable manner, and implements essential security requirements for IoT devices. By analyzing the protocol, we evaluate and demonstrate its efficiency and its relatively limited computational and storage overhead. Furthermore, the security of the protocol is assured through informal security analysis and formally by using the automated validation of Internet security protocols and applications (AVISPA) toolkit.
{"title":"Anonymous IoT Mutual Inter-Device Authentication Scheme Based on Incremental Counter (AIMIA-IC)","authors":"M. Alshahrani, I. Traoré, I. Woungang","doi":"10.1109/FiCloud.2019.00013","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00013","url":null,"abstract":"Cyber attackers are shifting their attention from traditional computers to IoT devices for malignant activities like exposing smart homeowner private information and/or to launch botnet attacks. Like for conventional networks, the security of IoT networks rests on how properly the authentication process is done. However, unlike conventional networks, IoT infrastructure faces an uphill battle in deploying and operating strong authentication schemes because of inherent limitations on the underlying storage and computation capability. In this paper, we propose a new anonymous mutual Inter-device authentication protocol based on transient identities, incremental counter and temporary secret keys for IoT. The proposed protocol is based on symmetric cryptography and somehow follows the ZigBee protocol. It allows IoT devices to anonymously and mutually authenticate in an unlinkable and untraceable manner, and implements essential security requirements for IoT devices. By analyzing the protocol, we evaluate and demonstrate its efficiency and its relatively limited computational and storage overhead. Furthermore, the security of the protocol is assured through informal security analysis and formally by using the automated validation of Internet security protocols and applications (AVISPA) toolkit.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132150970","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00052
Muhannad Quwaider, Abdullah Alabed, R. Duwairi
The propagation of video games in the previous years has led to the emergence of new areas associated with the video game industry. One of these areas is exploring the emotions and the behaviors of players after playing a specific game within a controlled environment such as a computer lab. In this paper, we will introduce a new way of analyzing the emotions and the behavior of players outside a controlled environment and using in-game data rather than using traditional questionnaires and interviews. The proposed system is expected to be part of future Internet of Things (IoT) application that is needed for human interaction. We will analyze the player's personality using in-game data. The data is generated and collected using mobile developed video game. Then, the collected data is evaluated using a well-known personality traits model called big Five-Factor Model (FFM). In order to create a set of appropriate scenarios to analyze the player's personality based on FFM we developed a First Person Shooter (FPS) video game. Using this game, we managed to generate and collect in-game data from hundreds of people. The results show that it was able to study the player's behavior over FFM traits. It was shown that the FFM traits scores are improved by repeating the game.
{"title":"In-Video Game Player's Behavior Measurement using Big Five Personal Traits","authors":"Muhannad Quwaider, Abdullah Alabed, R. Duwairi","doi":"10.1109/FiCloud.2019.00052","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00052","url":null,"abstract":"The propagation of video games in the previous years has led to the emergence of new areas associated with the video game industry. One of these areas is exploring the emotions and the behaviors of players after playing a specific game within a controlled environment such as a computer lab. In this paper, we will introduce a new way of analyzing the emotions and the behavior of players outside a controlled environment and using in-game data rather than using traditional questionnaires and interviews. The proposed system is expected to be part of future Internet of Things (IoT) application that is needed for human interaction. We will analyze the player's personality using in-game data. The data is generated and collected using mobile developed video game. Then, the collected data is evaluated using a well-known personality traits model called big Five-Factor Model (FFM). In order to create a set of appropriate scenarios to analyze the player's personality based on FFM we developed a First Person Shooter (FPS) video game. Using this game, we managed to generate and collect in-game data from hundreds of people. The results show that it was able to study the player's behavior over FFM traits. It was shown that the FFM traits scores are improved by repeating the game.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022222","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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