Pub Date : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233977
D. Hayes, M. Kyobe
Cybercrime has increased considerably over the past years, emphasizing the need for efficient investigations. Currently, some tools and processes are manual and lead to long and inaccurate investigations. This report provides a descriptive review of the of published research in the field of cyber forensics, in order to identify current practices, challenges and the adoption of automation. A pragmatist approach was taken to accommodate the multitude of theories and views presented in the literature. The research illustrates how the use of technology could simplify an investigators task and solving difficulties that currently exist in cyber forensics. The paper concludes in motivating for the use of automated practices in the cyber forensic process.
{"title":"The Adoption of Automation in Cyber Forensics","authors":"D. Hayes, M. Kyobe","doi":"10.1109/ICTAS47918.2020.233977","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233977","url":null,"abstract":"Cybercrime has increased considerably over the past years, emphasizing the need for efficient investigations. Currently, some tools and processes are manual and lead to long and inaccurate investigations. This report provides a descriptive review of the of published research in the field of cyber forensics, in order to identify current practices, challenges and the adoption of automation. A pragmatist approach was taken to accommodate the multitude of theories and views presented in the literature. The research illustrates how the use of technology could simplify an investigators task and solving difficulties that currently exist in cyber forensics. The paper concludes in motivating for the use of automated practices in the cyber forensic process.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117248090","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 : 2020-03-01DOI: 10.1109/ictas47918.2020.9082443
{"title":"ICTAS 2020 Breaker Page","authors":"","doi":"10.1109/ictas47918.2020.9082443","DOIUrl":"https://doi.org/10.1109/ictas47918.2020.9082443","url":null,"abstract":"","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650450","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 : 2020-03-01DOI: 10.1109/ictas47918.2020.9082471
{"title":"ICTAS 2020 Preface","authors":"","doi":"10.1109/ictas47918.2020.9082471","DOIUrl":"https://doi.org/10.1109/ictas47918.2020.9082471","url":null,"abstract":"","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415058","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 : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233975
Fazlyn Petersen, Ziyaad Luckan, S. Pather
The exponential increases in the number of patients with diabetes warrants the use of innovative health solutions, especially in low socio-economic areas. Yet the acceptance of Information Communication and Technology (ICT) for diabetes self-management remains low, especially in developing countries. The study used key constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) model and introduced education as a moderator. It analysed the survey data from 430 respondent using purposive sampling. It found that all four variables (effort expectancy (EE), performance expectancy (PE), social influence (SI) and facilitating conditions (FC)) all influenced behavioural intention (BI). Gender did not provide any moderation effect, contrary to literature. Age and education proved to have a moderating effect on the relationship between PE and BI and SI and BI. Only age had a moderating effect on the relationship between EE and BI and FC and BI.
{"title":"Impact of demographics on patients' acceptance of ICT for diabetes self-management: Applying the UTAUT model in low socio-economic areas","authors":"Fazlyn Petersen, Ziyaad Luckan, S. Pather","doi":"10.1109/ICTAS47918.2020.233975","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233975","url":null,"abstract":"The exponential increases in the number of patients with diabetes warrants the use of innovative health solutions, especially in low socio-economic areas. Yet the acceptance of Information Communication and Technology (ICT) for diabetes self-management remains low, especially in developing countries. The study used key constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) model and introduced education as a moderator. It analysed the survey data from 430 respondent using purposive sampling. It found that all four variables (effort expectancy (EE), performance expectancy (PE), social influence (SI) and facilitating conditions (FC)) all influenced behavioural intention (BI). Gender did not provide any moderation effect, contrary to literature. Age and education proved to have a moderating effect on the relationship between PE and BI and SI and BI. Only age had a moderating effect on the relationship between EE and BI and FC and BI.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758462","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 : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233999
Abigail Singh, Serestina Viriri
Signature verification is a technique used to counter signature forgery. In the past, the process began with staff at a bank, who is an expert, would confirm if a signature is genuine or forged. With the development of technology, now people no longer sign on paper, rather on a digital pad which can take more data which is recorded on paper, for example, pressure, azimuth and altitude angles. Examples of details captured from a digital pen include pen pressure, azimuth and altitude angles. This data is now used in various dynamic signature verification systems that achieve high accuracy on evaluation tests using different forms of artificial intelligence. This paper investigates using artificial intelligence in the form of a Convolutional Neural Network (CNN) followed by a Recurrent Neural Network (RNN) to verify signatures using the SVC 2004 and SigComp2009 online datasets and it achieved a testing accuracy of 97.05%.
{"title":"Online Signature Verification using Deep Descriptors","authors":"Abigail Singh, Serestina Viriri","doi":"10.1109/ICTAS47918.2020.233999","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233999","url":null,"abstract":"Signature verification is a technique used to counter signature forgery. In the past, the process began with staff at a bank, who is an expert, would confirm if a signature is genuine or forged. With the development of technology, now people no longer sign on paper, rather on a digital pad which can take more data which is recorded on paper, for example, pressure, azimuth and altitude angles. Examples of details captured from a digital pen include pen pressure, azimuth and altitude angles. This data is now used in various dynamic signature verification systems that achieve high accuracy on evaluation tests using different forms of artificial intelligence. This paper investigates using artificial intelligence in the form of a Convolutional Neural Network (CNN) followed by a Recurrent Neural Network (RNN) to verify signatures using the SVC 2004 and SigComp2009 online datasets and it achieved a testing accuracy of 97.05%.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272719","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 : 2020-03-01DOI: 10.1109/ictas47918.2020.9082464
{"title":"ICTAS 2020 Index","authors":"","doi":"10.1109/ictas47918.2020.9082464","DOIUrl":"https://doi.org/10.1109/ictas47918.2020.9082464","url":null,"abstract":"","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115305","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 : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233972
L. Butgereit
The term customer churn is used to describe a situation where a customer leaves one merchant or supplier and moves to a competitor of that original merchant or supplier. This is also know as customer attrition. Prior to churning, however, there are often hints or clues in the customer’s buying patterns that he or she is ready to leave the supplier. This paper looks at the use of Machine Learning algorithms to predict when customers are ready to churn or in the process of churning. These predictions are then used to look at free text unformatted log data to find any reasons why this customer might be churning. This free text log data would include textual error messages that the customer might have received or financial problems which might have arisen such as not having sufficient funds in his or her account. This merged information is then forwarded to an outbound call queue so that trained call center agents could make human-to-human voice calls to the customer and entice them to stay with the merchant or supplier by offering some financial incentive. All of the technicalities were orchestrated using Spring Boot microservices. Design Science Research was used for the this project and a number of iterations were executed until results were satisfactory. These iterations included changing from an AutoEncoder to a MultiLayerPerceptron, included changing from one Java library providing neural network objects to another Java library, included better searching of log files for possible reasons that customers were churning and included many experiments with the quantity of sales data required in order for the neural networks to create reasonable predictions.
{"title":"Big Data and Machine Learning for Forestalling Customer Churn Using Hybrid Software","authors":"L. Butgereit","doi":"10.1109/ICTAS47918.2020.233972","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233972","url":null,"abstract":"The term customer churn is used to describe a situation where a customer leaves one merchant or supplier and moves to a competitor of that original merchant or supplier. This is also know as customer attrition. Prior to churning, however, there are often hints or clues in the customer’s buying patterns that he or she is ready to leave the supplier. This paper looks at the use of Machine Learning algorithms to predict when customers are ready to churn or in the process of churning. These predictions are then used to look at free text unformatted log data to find any reasons why this customer might be churning. This free text log data would include textual error messages that the customer might have received or financial problems which might have arisen such as not having sufficient funds in his or her account. This merged information is then forwarded to an outbound call queue so that trained call center agents could make human-to-human voice calls to the customer and entice them to stay with the merchant or supplier by offering some financial incentive. All of the technicalities were orchestrated using Spring Boot microservices. Design Science Research was used for the this project and a number of iterations were executed until results were satisfactory. These iterations included changing from an AutoEncoder to a MultiLayerPerceptron, included changing from one Java library providing neural network objects to another Java library, included better searching of log files for possible reasons that customers were churning and included many experiments with the quantity of sales data required in order for the neural networks to create reasonable predictions.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315898","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 : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233980
Busisizwe Kelvin Nkomo, T. Breetzke
Credit cards play a role in economic growth because they allow for a cashless society which in turn reduces government expenditure on the manufacturing and distribution of monetary notes. A cashless society would allow governments to save billions of money that can be ploughed back into the economy for other purposes. However, mediums of achieving a cashless society such as credit cards are under attack from fraudsters. Recent studies show that more and more money is being fraudulently withdrawn from accounts. This paper aims to evaluate the credit card fraud detection methods used by banks and the difficulties in implementing the said methods. The study suggests the use of artificial intelligence, geolocation and data mining in credit card fraud detection methods to mitigate the weaknesses that current credit card fraud detection methods have. The use of artificial intelligence, data mining and geolocation would enable credit card fraud detection methods to analyse and identify trends in customer spending to identify fraudulent transactions. A model is introduced to help mitigate the weaknesses. An indepth literature review was undertaken and secondary research was used throughout the study as the main source of information.
{"title":"A conceptual model for the use of artificial intelligence for credit card fraud detection in banks","authors":"Busisizwe Kelvin Nkomo, T. Breetzke","doi":"10.1109/ICTAS47918.2020.233980","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233980","url":null,"abstract":"Credit cards play a role in economic growth because they allow for a cashless society which in turn reduces government expenditure on the manufacturing and distribution of monetary notes. A cashless society would allow governments to save billions of money that can be ploughed back into the economy for other purposes. However, mediums of achieving a cashless society such as credit cards are under attack from fraudsters. Recent studies show that more and more money is being fraudulently withdrawn from accounts. This paper aims to evaluate the credit card fraud detection methods used by banks and the difficulties in implementing the said methods. The study suggests the use of artificial intelligence, geolocation and data mining in credit card fraud detection methods to mitigate the weaknesses that current credit card fraud detection methods have. The use of artificial intelligence, data mining and geolocation would enable credit card fraud detection methods to analyse and identify trends in customer spending to identify fraudulent transactions. A model is introduced to help mitigate the weaknesses. An indepth literature review was undertaken and secondary research was used throughout the study as the main source of information.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132190751","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 : 2020-03-01DOI: 10.1109/ictas47918.2020.9082479
{"title":"ICTAS 2020 Ad Page","authors":"","doi":"10.1109/ictas47918.2020.9082479","DOIUrl":"https://doi.org/10.1109/ictas47918.2020.9082479","url":null,"abstract":"","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114693192","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 : 2020-03-01DOI: 10.1109/ICTAS47918.2020.233995
L. Bopape, B. Nleya, Phumzile P. Khumalo
Long-Term-Evolution (LTE) based Device-to-Device (D2D) communication in future generation networks are envisaged to become the basis for deployment of various applications and services in Smart Grids (SGs). However related privacy and security aspects are also under serious consideration especially when dealing with large-scale deployment of services and applications related D2D groups. Current and legacy related algorithms cannot be applied directly to this new paradigm shift (i.e D2D communication and group formations). Using the IoT as the pillar communication subsystem for SGs, the service providers can deploy several applications and services some of which may include the acquisition and storage of personal information of individual SG users. However, the challenge will always be in the strict preservation of privacy and security of their personal data and thus a necessity in eliminating such concerns. In this paper we propose a general framework that employs a Group Key Management (GKM) mechanism to ensure enhanced privacy and security especially during the discovery and communication phases. We further mitigate on the impact of enhanced privacy and security in SG services and applications.
{"title":"A Privacy and Security Preservation Framework for D2D Communication Based Smart Grid Services","authors":"L. Bopape, B. Nleya, Phumzile P. Khumalo","doi":"10.1109/ICTAS47918.2020.233995","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233995","url":null,"abstract":"Long-Term-Evolution (LTE) based Device-to-Device (D2D) communication in future generation networks are envisaged to become the basis for deployment of various applications and services in Smart Grids (SGs). However related privacy and security aspects are also under serious consideration especially when dealing with large-scale deployment of services and applications related D2D groups. Current and legacy related algorithms cannot be applied directly to this new paradigm shift (i.e D2D communication and group formations). Using the IoT as the pillar communication subsystem for SGs, the service providers can deploy several applications and services some of which may include the acquisition and storage of personal information of individual SG users. However, the challenge will always be in the strict preservation of privacy and security of their personal data and thus a necessity in eliminating such concerns. In this paper we propose a general framework that employs a Group Key Management (GKM) mechanism to ensure enhanced privacy and security especially during the discovery and communication phases. We further mitigate on the impact of enhanced privacy and security in SG services and applications.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881635","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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