Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00121
Taishi Nemoto, T. Fujimoto
Since the 2000s, the third artificial intelligence boom has occurred. Research on machine learning and deep learning is progressing, but challenges remain regarding realizing so-called ‘human-like’ general-purpose AI (Artificial Intelligence). In recent years, artificial intelligence research has been linked to cognitive science, and the question of what ‘humanity’ is and how to design ‘humanity’ has been raised as issues. The more robots resemble humans, the more the ‘uncanny valley phenomenon’ increases and the more people feel uncomfortable. Even though technology has advanced and realistic textures can be expressed, robots seem to be just ‘artifacts.’ The point of this study is to determine at what point humans feel ‘human-like’ and how to reproduce ‘human-like’ using a computer. In this study, in order to express human personality and characteristic gestures, we generate an ‘artificial personality’ (AP), and let people find the human touch that a real person possesses through that AP. For example, artificial reproduction of the intelligence of a deceased person is difficult with today's technology. However, AP enables to extract the characteristics of a person's gestures, routines, habits, and facial expressions in his or her lifetime, and to digitally recreate the person's personality based on the accumulation of ‘flesh and blood’ data. This study discusses the two elements and basic mechanisms that are necessary for AP research.
{"title":"Design of Humanity by the Concept of Artificial Personalities","authors":"Taishi Nemoto, T. Fujimoto","doi":"10.1109/CSCI51800.2020.00121","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00121","url":null,"abstract":"Since the 2000s, the third artificial intelligence boom has occurred. Research on machine learning and deep learning is progressing, but challenges remain regarding realizing so-called ‘human-like’ general-purpose AI (Artificial Intelligence). In recent years, artificial intelligence research has been linked to cognitive science, and the question of what ‘humanity’ is and how to design ‘humanity’ has been raised as issues. The more robots resemble humans, the more the ‘uncanny valley phenomenon’ increases and the more people feel uncomfortable. Even though technology has advanced and realistic textures can be expressed, robots seem to be just ‘artifacts.’ The point of this study is to determine at what point humans feel ‘human-like’ and how to reproduce ‘human-like’ using a computer. In this study, in order to express human personality and characteristic gestures, we generate an ‘artificial personality’ (AP), and let people find the human touch that a real person possesses through that AP. For example, artificial reproduction of the intelligence of a deceased person is difficult with today's technology. However, AP enables to extract the characteristics of a person's gestures, routines, habits, and facial expressions in his or her lifetime, and to digitally recreate the person's personality based on the accumulation of ‘flesh and blood’ data. This study discusses the two elements and basic mechanisms that are necessary for AP research.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00237
Jinho Ahn
A communication-induced checkpointing algorithm, named HMNR, was introduced to effectively use control information of every other process piggybacked on each sent message for minimizing the number of forced checkpoints. Then, an improved algorithm, called Lazy-HMNR, was presented to lower the possibility of taking forced checkpoints incurred by the asymmetry between checkpointing frequencies of processes. Despite these two different minimization techniques, if the high message interaction traffic occurs, Lazy-HMNR may considerably lower the probability of detecting Z-cycle free patterns due to its shortcoming. Also, there is no prior research attempt to design the algorithms considering network topologies for making the number of forced checkpoints as few as possible with control information piggybacked on each message. This paper introduces a new Lazy-HMNR algorithm for group communication-based distributed systems to synergistically decrease the number of forced checkpoints in a more effective manner.
{"title":"Scalable Distributed Checkpointing Algorithm","authors":"Jinho Ahn","doi":"10.1109/CSCI51800.2020.00237","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00237","url":null,"abstract":"A communication-induced checkpointing algorithm, named HMNR, was introduced to effectively use control information of every other process piggybacked on each sent message for minimizing the number of forced checkpoints. Then, an improved algorithm, called Lazy-HMNR, was presented to lower the possibility of taking forced checkpoints incurred by the asymmetry between checkpointing frequencies of processes. Despite these two different minimization techniques, if the high message interaction traffic occurs, Lazy-HMNR may considerably lower the probability of detecting Z-cycle free patterns due to its shortcoming. Also, there is no prior research attempt to design the algorithms considering network topologies for making the number of forced checkpoints as few as possible with control information piggybacked on each message. This paper introduces a new Lazy-HMNR algorithm for group communication-based distributed systems to synergistically decrease the number of forced checkpoints in a more effective manner.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00256
Zalán Heszberger, A. Gulyás, András Majdán, András Bíró, László Balázs, Szabolcs Mezei, J. Bíró
The structural navigability of complex networks is an important question in the function-structure perspective of complex network analysis. This may provide hints on the underlying mechanisms that have been forming the structure of networks for a desirable level of navigation. It has been already discovered that greedy navigational cores as minimalistic networks with 100% greedy navigability considerably present in many real networks, including the structural networks of the human brain. Because the greedy navigational core is not unique, the connection between the level of its presence in a network and the structural navigability of that network is far from clear. In this paper, we deal with a special subset of the greedy navigational core, the so-called greedy proximity links (GPL), whose presence is necessary for 100% greedy navigability of a network. We show that the greedy proximity links are highly present in the brain networks, and the presence is consistent throughout the the individual subjects.
{"title":"Proximity in the Brain","authors":"Zalán Heszberger, A. Gulyás, András Majdán, András Bíró, László Balázs, Szabolcs Mezei, J. Bíró","doi":"10.1109/CSCI51800.2020.00256","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00256","url":null,"abstract":"The structural navigability of complex networks is an important question in the function-structure perspective of complex network analysis. This may provide hints on the underlying mechanisms that have been forming the structure of networks for a desirable level of navigation. It has been already discovered that greedy navigational cores as minimalistic networks with 100% greedy navigability considerably present in many real networks, including the structural networks of the human brain. Because the greedy navigational core is not unique, the connection between the level of its presence in a network and the structural navigability of that network is far from clear. In this paper, we deal with a special subset of the greedy navigational core, the so-called greedy proximity links (GPL), whose presence is necessary for 100% greedy navigability of a network. We show that the greedy proximity links are highly present in the brain networks, and the presence is consistent throughout the the individual subjects.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123389034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00230
Adedolapo Okanlawon, Huichen Yang, Avishek Bose, W. Hsu, Dan Andresen, Mohammed Tanash
We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud. This task was cast as both supervised classification and regression learning, specifically, sequential problem solving suitable for reinforcement learning. Selecting relevant features can improve training accuracy, reduce training time, and produce a more comprehensible model, with an intelligent system that can explain predictions and inferences. We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features: linear regression, lasso, and ridge regression. Our data set represented both HPC jobs that failed and those that succeeded, so our model was reliable, less likely to overfit, and generalizable. Our model achieved an R2 of 95% with 99% accuracy. We identified five predictors for both CPU and memory properties.
{"title":"Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support","authors":"Adedolapo Okanlawon, Huichen Yang, Avishek Bose, W. Hsu, Dan Andresen, Mohammed Tanash","doi":"10.1109/CSCI51800.2020.00230","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00230","url":null,"abstract":"We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud. This task was cast as both supervised classification and regression learning, specifically, sequential problem solving suitable for reinforcement learning. Selecting relevant features can improve training accuracy, reduce training time, and produce a more comprehensible model, with an intelligent system that can explain predictions and inferences. We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features: linear regression, lasso, and ridge regression. Our data set represented both HPC jobs that failed and those that succeeded, so our model was reliable, less likely to overfit, and generalizable. Our model achieved an R2 of 95% with 99% accuracy. We identified five predictors for both CPU and memory properties.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"67 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123587156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00035
M. Blair, Davis Jeffords, Eric Lilling, Shankar Banik
In this paper, we discuss the importance and proof of concept for a picture-taking app that will remind the user of their surroundings. The background to this issue is that several people die a year because they are in a situation unsafe for taking pictures, but are too preoccupied with their phone to realise the danger. This application will take many factors into consideration, including user velocity, local emergency contact information, and geological hazards to warn the user of safety issues. The goal of this application is to reduce the amount of accidental injuries or deaths related to taking pictures in unsafe areas.
{"title":"Safe Selfie","authors":"M. Blair, Davis Jeffords, Eric Lilling, Shankar Banik","doi":"10.1109/CSCI51800.2020.00035","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00035","url":null,"abstract":"In this paper, we discuss the importance and proof of concept for a picture-taking app that will remind the user of their surroundings. The background to this issue is that several people die a year because they are in a situation unsafe for taking pictures, but are too preoccupied with their phone to realise the danger. This application will take many factors into consideration, including user velocity, local emergency contact information, and geological hazards to warn the user of safety issues. The goal of this application is to reduce the amount of accidental injuries or deaths related to taking pictures in unsafe areas.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121642386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00056
J. Atoum
In recent years with the widespread of social media platforms across the globe especially among young people, cyberbullying and aggression have become a serious and annoying problem that communities must deal with. Such platforms provide various ways for bullies to attack and threaten others in their communities. Various techniques and methodologies have been used or proposed to combat cyberbullying through early detection and alerts to discover and/or protect victims from such attacks. Machine learning (ML) techniques have been widely used to detect some language patterns that are exploited by bullies to attack their victims. Also. Sentiment Analysis (SA) of social media content has become one of the growing areas of research in machine learning. SA provides the ability to detect cyberbullying in real-time. SA provides the ability to detect cyberbullying in real-time. This paper proposes a SA model for identifying cyberbullying texts in Twitter social media. Support Vector Machines (SVM) and Naïve Bayes (NB) are used in this model as supervised machine learning classification tools. The results of the experiments conducted on this model showed encouraging outcomes when a higher n-grams language model is applied on such texts in comparison with similar previous research. Also, the results showed that SVM classifiers have better performance measures than NB classifiers on such tweets.
{"title":"Cyberbullying Detection Through Sentiment Analysis","authors":"J. Atoum","doi":"10.1109/CSCI51800.2020.00056","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00056","url":null,"abstract":"In recent years with the widespread of social media platforms across the globe especially among young people, cyberbullying and aggression have become a serious and annoying problem that communities must deal with. Such platforms provide various ways for bullies to attack and threaten others in their communities. Various techniques and methodologies have been used or proposed to combat cyberbullying through early detection and alerts to discover and/or protect victims from such attacks. Machine learning (ML) techniques have been widely used to detect some language patterns that are exploited by bullies to attack their victims. Also. Sentiment Analysis (SA) of social media content has become one of the growing areas of research in machine learning. SA provides the ability to detect cyberbullying in real-time. SA provides the ability to detect cyberbullying in real-time. This paper proposes a SA model for identifying cyberbullying texts in Twitter social media. Support Vector Machines (SVM) and Naïve Bayes (NB) are used in this model as supervised machine learning classification tools. The results of the experiments conducted on this model showed encouraging outcomes when a higher n-grams language model is applied on such texts in comparison with similar previous research. Also, the results showed that SVM classifiers have better performance measures than NB classifiers on such tweets.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123900938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00043
I. Avramovic, D. Richards
Perpetual gossiping is an all-to-all communication problem on social networks, or any coordinated distributed system in general. In perpetual gossiping, a state of universal reachability is maintained by a continuous sequence of communications between participants. Unlike the well-understood static case, perpetual gossiping is a difficult problem, with some NP-complete classes of solutions. A basic question which remains open is whether an optimal scheme of contiguous calls is guaranteed to exist for a tree. This paper presents a series of theoretical tools directed towards answering the question.
{"title":"Rules for Optimal Perpetual Gossiping Schemes","authors":"I. Avramovic, D. Richards","doi":"10.1109/CSCI51800.2020.00043","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00043","url":null,"abstract":"Perpetual gossiping is an all-to-all communication problem on social networks, or any coordinated distributed system in general. In perpetual gossiping, a state of universal reachability is maintained by a continuous sequence of communications between participants. Unlike the well-understood static case, perpetual gossiping is a difficult problem, with some NP-complete classes of solutions. A basic question which remains open is whether an optimal scheme of contiguous calls is guaranteed to exist for a tree. This paper presents a series of theoretical tools directed towards answering the question.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125857064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00103
Helton Agbewonou Yawovi, Tadachika Ozono, T. Shintani
With the increasing number of motorized vehicles, road accidents are increasing year by year all over the world.. After an accident, the police investigate the circumstances of the incident and determine each actor’s responsibilities. Our goal is to create a police support system. We focused on a multi-agent system that predicts each actor’s responsibility in a road accident (especially crossroad accidents). The system uses the driving recorder video of a vehicle as the input data source, and it outputs the prediction of the responsibility of each actor in the accident. It consists of three agents: (1) Crash time detection and crash video split into images; (2) Traffic signs detection in the crash video; (3) Responsibility prediction using a knowledge system.
{"title":"Crossroad Accident Responsibility Prediction Based on a Multi-agent System","authors":"Helton Agbewonou Yawovi, Tadachika Ozono, T. Shintani","doi":"10.1109/CSCI51800.2020.00103","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00103","url":null,"abstract":"With the increasing number of motorized vehicles, road accidents are increasing year by year all over the world.. After an accident, the police investigate the circumstances of the incident and determine each actor’s responsibilities. Our goal is to create a police support system. We focused on a multi-agent system that predicts each actor’s responsibility in a road accident (especially crossroad accidents). The system uses the driving recorder video of a vehicle as the input data source, and it outputs the prediction of the responsibility of each actor in the accident. It consists of three agents: (1) Crash time detection and crash video split into images; (2) Traffic signs detection in the crash video; (3) Responsibility prediction using a knowledge system.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129300083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00089
J. Wallace, Angelica Valdivia
A framework to integrate structurally different artificial intelligence, machine learning, and control algorithms is combined with an execution framework to create a powerful embedded system development platform. Control, decision, or algorithms providing an emulation of intelligent behavior in both declarative (interpreted) and imperative (compiled) paradigms can now be combined, for example Prolog and neural networks, respectively. This hybridization of algorithms provides more efficient overall control of systems in terms of resources such as compute cycles, network bandwidth and throughput, and memory speed and capacity. By providing an execution framework and control software that is native to embedded system and cloud architectures, and supports interactivity and time synchronization, the true utility of cloud computing and "big data systems" can be increased.
{"title":"A Hybrid Artificial Intelligence, Machine Learning, and Control Algorithm Integration Framework for Embedded Systems using Semantic Web Technology","authors":"J. Wallace, Angelica Valdivia","doi":"10.1109/CSCI51800.2020.00089","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00089","url":null,"abstract":"A framework to integrate structurally different artificial intelligence, machine learning, and control algorithms is combined with an execution framework to create a powerful embedded system development platform. Control, decision, or algorithms providing an emulation of intelligent behavior in both declarative (interpreted) and imperative (compiled) paradigms can now be combined, for example Prolog and neural networks, respectively. This hybridization of algorithms provides more efficient overall control of systems in terms of resources such as compute cycles, network bandwidth and throughput, and memory speed and capacity. By providing an execution framework and control software that is native to embedded system and cloud architectures, and supports interactivity and time synchronization, the true utility of cloud computing and \"big data systems\" can be increased.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129381381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00039
Ahmed Al Guqhaiman, Oluwatobi Akanbi, Amer Aljaedi, C. E. Chow
Underwater Wireless Sensor Networks (UWSNs) are liable to malicious attacks due to limited bandwidth, limited power, high propagation delay, path loss, and variable speed. The major differences between UWSNs and Terrestrial Wireless Sensor Networks (TWSNs) necessitate a new mechanism to secure UWSNs. The existing Media Access Control (MAC) and routing protocols have addressed the network performance of UWSNs, but are vulnerable to several attacks. The secure MAC and routing protocols must exist to detect Sybil, Blackhole, Wormhole, Hello Flooding, Acknowledgment Spoofing, Selective Forwarding, Sinkhole, and Exhaustion attacks. These attacks can disrupt or disable the network connection. Hence, these attacks can degrade the network performance and total loss can be catastrophic in some applications, like monitoring oil/gas spills. Several researchers have studied the security of UWSNs, but most of the works detect malicious attacks solely based on a certain predefined threshold. It is not optimal to detect malicious attacks after the threshold value is met. In this paper, we propose a multi-factor authentication model that is based on zero-knowledge proof to detect malicious activities and secure UWSNs from several attacks.
{"title":"Lightweight Multi-factor Authentication for Underwater Wireless Sensor Networks","authors":"Ahmed Al Guqhaiman, Oluwatobi Akanbi, Amer Aljaedi, C. E. Chow","doi":"10.1109/CSCI51800.2020.00039","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00039","url":null,"abstract":"Underwater Wireless Sensor Networks (UWSNs) are liable to malicious attacks due to limited bandwidth, limited power, high propagation delay, path loss, and variable speed. The major differences between UWSNs and Terrestrial Wireless Sensor Networks (TWSNs) necessitate a new mechanism to secure UWSNs. The existing Media Access Control (MAC) and routing protocols have addressed the network performance of UWSNs, but are vulnerable to several attacks. The secure MAC and routing protocols must exist to detect Sybil, Blackhole, Wormhole, Hello Flooding, Acknowledgment Spoofing, Selective Forwarding, Sinkhole, and Exhaustion attacks. These attacks can disrupt or disable the network connection. Hence, these attacks can degrade the network performance and total loss can be catastrophic in some applications, like monitoring oil/gas spills. Several researchers have studied the security of UWSNs, but most of the works detect malicious attacks solely based on a certain predefined threshold. It is not optimal to detect malicious attacks after the threshold value is met. In this paper, we propose a multi-factor authentication model that is based on zero-knowledge proof to detect malicious activities and secure UWSNs from several attacks.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130868500","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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