Moatsum Alawida, A. Samsudin, Wafa’ Hamdan Alshoura
The chaotic map has attracted more researchers in chaotic cryptography domain. That is because to large similarity among their characteristics such as unpredictability and sensitivity to an initial condition. Moreover, chaotic maps are a source of entropy that provide diffusion and confusion strength in cryptographic applications. Although existing one dimensional chaotic maps are widely used, many security issues are found in their behaviors. This paper tackles those issues through the new proposed solution model that uses bitstream dividing model. The proposed model uses the fixed-point format to extract bitstream and dividing a chaotic state into N equal parts, each part is a new chaotic state. In order to show the effectiveness of the proposed model, the unimodal maps are used in the simulation, it is shown that the proposed model has better chaotic complexity and performance than the corresponding maps. Moreover, it is a simple structure but effective and can be used as a chaotification model to generate digital chaos. In chaos-based cryptography, the enhanced maps have a better security strength which is highly needed for the development of chaos-based ciphers.
{"title":"Enhancing One-Dimensional Chaotic Map Based on Bitstream Dividing Model","authors":"Moatsum Alawida, A. Samsudin, Wafa’ Hamdan Alshoura","doi":"10.1145/3316615.3316657","DOIUrl":"https://doi.org/10.1145/3316615.3316657","url":null,"abstract":"The chaotic map has attracted more researchers in chaotic cryptography domain. That is because to large similarity among their characteristics such as unpredictability and sensitivity to an initial condition. Moreover, chaotic maps are a source of entropy that provide diffusion and confusion strength in cryptographic applications. Although existing one dimensional chaotic maps are widely used, many security issues are found in their behaviors. This paper tackles those issues through the new proposed solution model that uses bitstream dividing model. The proposed model uses the fixed-point format to extract bitstream and dividing a chaotic state into N equal parts, each part is a new chaotic state. In order to show the effectiveness of the proposed model, the unimodal maps are used in the simulation, it is shown that the proposed model has better chaotic complexity and performance than the corresponding maps. Moreover, it is a simple structure but effective and can be used as a chaotification model to generate digital chaos. In chaos-based cryptography, the enhanced maps have a better security strength which is highly needed for the development of chaos-based ciphers.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129227682","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}
Muhammad Umar Aftab, Zhiguang Qin, Syed Falahuddin Quadri, Zakria, A. Javed, Xuyun Nie
RBAC and ABAC are well-known access control models due to their least privileges and dynamic behavior respectively. They also have some drawbacks like RBAC is unable to provide dynamic behavior and flexibility as well as ABAC is unable to provide tight security and ease of management of permissions as the RBAC can do. In this paper, a hybrid access control model is proposed and developed that combines the strengths of both models. The proposed model implements the concept of roles between a user and the user's attributes as well as between the object and object attributes, in the ABAC system. The proposed model decreases the load of the administrator, provides least of privileges concept in ABAC due to the addition of roles. Authors also implemented the proposed model and discussed with respect to a case study.
{"title":"Role-Based ABAC Model for Implementing Least Privileges","authors":"Muhammad Umar Aftab, Zhiguang Qin, Syed Falahuddin Quadri, Zakria, A. Javed, Xuyun Nie","doi":"10.1145/3316615.3316667","DOIUrl":"https://doi.org/10.1145/3316615.3316667","url":null,"abstract":"RBAC and ABAC are well-known access control models due to their least privileges and dynamic behavior respectively. They also have some drawbacks like RBAC is unable to provide dynamic behavior and flexibility as well as ABAC is unable to provide tight security and ease of management of permissions as the RBAC can do. In this paper, a hybrid access control model is proposed and developed that combines the strengths of both models. The proposed model implements the concept of roles between a user and the user's attributes as well as between the object and object attributes, in the ABAC system. The proposed model decreases the load of the administrator, provides least of privileges concept in ABAC due to the addition of roles. Authors also implemented the proposed model and discussed with respect to a case study.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693301","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}
The Philippines is largely an agricultural country, and the importance of coffee in the Philippines cannot be undervalued. However, the coffee plantations are generally confronted with various insect pests and diseases. This is the reason why authorities continue to look for solutions through technological applications for coffee farming. Smartphones are becoming a functional tool in agriculture because its mobility served as an advantage to agriculture. However, challenges regarding the level of ICT, particularly of smartphones technology usage among the rural community is low due to limited knowledge and skills. Thus, this study has the primary objective to apply data mining to the smartphone manipulation skills of possible users' dataset in the province of Quirino, Philippines. Specifically, it sought to determine the optimal number of the types of potential users and to identify the different types of possible users and their skills that emerged from the clustering. The result shows that k=4 is the best choice for the dataset. The four clusters formed to represent the four groups of possible users are the good users with 25 instances, skilled users with 35 instances, the users with limited skills with 83 instances and finally, the expert users with 32 instances. Each of the groups possesses their distinct skills which emerged from the clustering technique implemented.
{"title":"Data Mining the Smartphone Manipulation Skills in a Coffee Farming Community: A Step for Risk Analysis","authors":"M. V. Pagudpud, T. Palaoag","doi":"10.1145/3316615.3316693","DOIUrl":"https://doi.org/10.1145/3316615.3316693","url":null,"abstract":"The Philippines is largely an agricultural country, and the importance of coffee in the Philippines cannot be undervalued. However, the coffee plantations are generally confronted with various insect pests and diseases. This is the reason why authorities continue to look for solutions through technological applications for coffee farming. Smartphones are becoming a functional tool in agriculture because its mobility served as an advantage to agriculture. However, challenges regarding the level of ICT, particularly of smartphones technology usage among the rural community is low due to limited knowledge and skills. Thus, this study has the primary objective to apply data mining to the smartphone manipulation skills of possible users' dataset in the province of Quirino, Philippines. Specifically, it sought to determine the optimal number of the types of potential users and to identify the different types of possible users and their skills that emerged from the clustering. The result shows that k=4 is the best choice for the dataset. The four clusters formed to represent the four groups of possible users are the good users with 25 instances, skilled users with 35 instances, the users with limited skills with 83 instances and finally, the expert users with 32 instances. Each of the groups possesses their distinct skills which emerged from the clustering technique implemented.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123187801","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}
Muhammad Ibnu Choldun Rachmatullah, J. Santoso, K. Surendro
One of the challenges in the successful implementation of deep neural network (DNN) is setting the value for various hyper-parameters, one of which is the network topology, which is closely related to the number of hidden layers and the number of hidden neurons. Determining the number of hidden layers and the number of neurons is very important and has a large influence on DNN performance. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process, while the automatic approach is divided into two, they are a model-based approach and a non-model based approach. The non-model-based approach, for example, is grid search and random search, whereas model-based approaches, for example, are using particle swarm optimization (PSO) algorithms. In some researches, how to determine the number of hidden layers or number of neurons, often the guidelines are unclear, even the roles and functions of both are explained minimally. Although it is still a difficult area of research, research to determine the number of hidden layers and the number of neurons must continue to be carried out, because these two numbers will greatly determine the performance of DNN.
{"title":"Determining the Neural Network Topology: A Review","authors":"Muhammad Ibnu Choldun Rachmatullah, J. Santoso, K. Surendro","doi":"10.1145/3316615.3316697","DOIUrl":"https://doi.org/10.1145/3316615.3316697","url":null,"abstract":"One of the challenges in the successful implementation of deep neural network (DNN) is setting the value for various hyper-parameters, one of which is the network topology, which is closely related to the number of hidden layers and the number of hidden neurons. Determining the number of hidden layers and the number of neurons is very important and has a large influence on DNN performance. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process, while the automatic approach is divided into two, they are a model-based approach and a non-model based approach. The non-model-based approach, for example, is grid search and random search, whereas model-based approaches, for example, are using particle swarm optimization (PSO) algorithms. In some researches, how to determine the number of hidden layers or number of neurons, often the guidelines are unclear, even the roles and functions of both are explained minimally. Although it is still a difficult area of research, research to determine the number of hidden layers and the number of neurons must continue to be carried out, because these two numbers will greatly determine the performance of DNN.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124402252","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}
Mei-Li Shen, Hsiou-Hsiang Liu, Yi-Hsiang Lien, Cheng-Feng Lee, Cheng-Hong Yang
For the tourism industry, accurate forecasts of travel needs are essential to meeting relevant needs, providing pertinent information to the government, and enabling stakeholders to adjust plans and policies. This study devised an approach that combines feature selection and support vector regression with particle swarm optimization (FS-PSOSVR) to forecast tourists to Singapore. The monthly tourist arrivals to Singapore from January 1978 to December 2017 were utilized as a test dataset. The results showed that the error obtained through FS-PSOSVR was smaller than that through other methods, revealing that FS-PSOSVR is an effective method for predicting tourism demand.
{"title":"Hybrid Approach for Forecasting Tourist Arrivals","authors":"Mei-Li Shen, Hsiou-Hsiang Liu, Yi-Hsiang Lien, Cheng-Feng Lee, Cheng-Hong Yang","doi":"10.1145/3316615.3316628","DOIUrl":"https://doi.org/10.1145/3316615.3316628","url":null,"abstract":"For the tourism industry, accurate forecasts of travel needs are essential to meeting relevant needs, providing pertinent information to the government, and enabling stakeholders to adjust plans and policies. This study devised an approach that combines feature selection and support vector regression with particle swarm optimization (FS-PSOSVR) to forecast tourists to Singapore. The monthly tourist arrivals to Singapore from January 1978 to December 2017 were utilized as a test dataset. The results showed that the error obtained through FS-PSOSVR was smaller than that through other methods, revealing that FS-PSOSVR is an effective method for predicting tourism demand.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120830617","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}
Fanf Ming, Riyaz Ahamed Ariyaluran Habeeb, F. Nasaruddin, A. Gani
In recent years, environment monitoring are of greater importance towards the area of climate monitoring, analysis, agricultural productivity management, quality assurance of water, air, alongside with other potential factors that are closely connected to industrial development and convenience of living. This research is motivated by creating awareness of smart home residents on indoor air quality, as well as providing insight of carbon dioxide emissions for industries and environmental organizations. This paper proposes an efficient solution towards environment monitoring of carbon dioxide integrated with Internet of Things capability and cloud computing technology. Aforementioned techniques will deliver highly accessible and real-time data visualization which would be greatly beneficial for Smart Homes efficiency of analysis actualization and counter-measures deployment. A monitoring architecture was developed to generate, accumulate, store and visualize carbon dioxide concentration using MQ135 carbon dioxide sensor, ESP8266 Wi-Fi module, Firebase Cloud Storage Service and Android mobile application Carbon Insight for data visualization. 2880 data points in the time frame of 10 days with a 30-second interval was collected, stored and visualized with the application of this system.
{"title":"Real-Time Carbon Dioxide Monitoring Based on IoT & Cloud Technologies","authors":"Fanf Ming, Riyaz Ahamed Ariyaluran Habeeb, F. Nasaruddin, A. Gani","doi":"10.1145/3316615.3316622","DOIUrl":"https://doi.org/10.1145/3316615.3316622","url":null,"abstract":"In recent years, environment monitoring are of greater importance towards the area of climate monitoring, analysis, agricultural productivity management, quality assurance of water, air, alongside with other potential factors that are closely connected to industrial development and convenience of living. This research is motivated by creating awareness of smart home residents on indoor air quality, as well as providing insight of carbon dioxide emissions for industries and environmental organizations. This paper proposes an efficient solution towards environment monitoring of carbon dioxide integrated with Internet of Things capability and cloud computing technology. Aforementioned techniques will deliver highly accessible and real-time data visualization which would be greatly beneficial for Smart Homes efficiency of analysis actualization and counter-measures deployment. A monitoring architecture was developed to generate, accumulate, store and visualize carbon dioxide concentration using MQ135 carbon dioxide sensor, ESP8266 Wi-Fi module, Firebase Cloud Storage Service and Android mobile application Carbon Insight for data visualization. 2880 data points in the time frame of 10 days with a 30-second interval was collected, stored and visualized with the application of this system.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050345","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}
In the past decade, object-oriented software engineering (OOSE) has gained popularity from many software developers, especially OOSE with a unified modeling language (UML). Use case views are often used in most systems during an analysis phase. These views show the system functionality related to the system stakeholders. Hence, use case views seem to be a corner stone for a software system. The defects occurring in use case views will affect the later designs. If these defects can be found early, it would save time and cost in software development. Therefore, this research proposes a technique for detecting defects in use case views during an analysis phase or requirements engineering process. Correct users' requirements were created as requirements specifications in a traditional form-based style for testing comparison. The algorithms for generating decision table from form-based requirements and UML use case specification as well as the algorithm for use case view validation were invented. Two simple case studies were investigated and used as the preliminary evaluation. The nineteen fourth-year students were selected as the subjects for the preliminary investigation in order to compare between manual fault detection and our automated proposed system. They were asked to perform four tasks for each case study. The results show that the efficiency of manual fault detection is less than the proposed technique at 47% and 44% or overall average as 45.5% for the two case studies. Currently, we apply this proposed technique to more complex industrial setting and familiar software systems to software engineers.
{"title":"Defects Detection Technique of Use Case Views during Requirements Engineering","authors":"Poranat Tianual, A. Pohthong","doi":"10.1145/3316615.3316631","DOIUrl":"https://doi.org/10.1145/3316615.3316631","url":null,"abstract":"In the past decade, object-oriented software engineering (OOSE) has gained popularity from many software developers, especially OOSE with a unified modeling language (UML). Use case views are often used in most systems during an analysis phase. These views show the system functionality related to the system stakeholders. Hence, use case views seem to be a corner stone for a software system. The defects occurring in use case views will affect the later designs. If these defects can be found early, it would save time and cost in software development. Therefore, this research proposes a technique for detecting defects in use case views during an analysis phase or requirements engineering process. Correct users' requirements were created as requirements specifications in a traditional form-based style for testing comparison. The algorithms for generating decision table from form-based requirements and UML use case specification as well as the algorithm for use case view validation were invented. Two simple case studies were investigated and used as the preliminary evaluation. The nineteen fourth-year students were selected as the subjects for the preliminary investigation in order to compare between manual fault detection and our automated proposed system. They were asked to perform four tasks for each case study. The results show that the efficiency of manual fault detection is less than the proposed technique at 47% and 44% or overall average as 45.5% for the two case studies. Currently, we apply this proposed technique to more complex industrial setting and familiar software systems to software engineers.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124311581","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}
Imbalance learning is one of the most challenging problems in supervised learning, so many different strategies are designed to tackle balanced sample distribution. The over-sampling techniques which achieve a relatively balanced class distribution through synthesizing samples receive more and more attention. In this paper, we present an over-sampling approach based on isolation Forest (iForest) and SMOTE, called iForest-SMOTE. Firstly, for minority class samples, iForest-score is employed to assess the importance of each sample based on iForest model. Then, in each SMOTE process, roulette wheel selection based on iForest-score is utilized to select the neighbor sample. Finally, M-dimensional-sphere interpolation approach is employed to generate a new sample. The experiments illustrate that our approach takes into account the spatial distribution of minority class samples and sample synthetic simultaneously. Therefore, iForest-SMOTE can effectively improve the performance of the classification model.
{"title":"An Improved Over-sampling Algorithm based on iForest and SMOTE","authors":"Yifeng Zheng, Guohe Li, Teng Zhang","doi":"10.1145/3316615.3316641","DOIUrl":"https://doi.org/10.1145/3316615.3316641","url":null,"abstract":"Imbalance learning is one of the most challenging problems in supervised learning, so many different strategies are designed to tackle balanced sample distribution. The over-sampling techniques which achieve a relatively balanced class distribution through synthesizing samples receive more and more attention. In this paper, we present an over-sampling approach based on isolation Forest (iForest) and SMOTE, called iForest-SMOTE. Firstly, for minority class samples, iForest-score is employed to assess the importance of each sample based on iForest model. Then, in each SMOTE process, roulette wheel selection based on iForest-score is utilized to select the neighbor sample. Finally, M-dimensional-sphere interpolation approach is employed to generate a new sample. The experiments illustrate that our approach takes into account the spatial distribution of minority class samples and sample synthetic simultaneously. Therefore, iForest-SMOTE can effectively improve the performance of the classification model.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123426396","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}
R. Efendi, Susnaningsih Mu’at, Nelsy Arisandi, N. Samsudin
In economics research survey, the causal relationship between independent and dependent attributes has been frequently investigated by using regression linear models. However, not easy to achieve the high R-square value between both attributes if there are too many unclassified elements in data sets. This paper presents removing unclassified elements in conventional regression model using rough sets approximation. The proposed model is address to handle the unclassified academic staffs in data set which less contribution for supporting financial wellbeing decision. The result showed that number of unclassified staff has a positive effect to increase coefficient determination (R-square) value in the regression model. In this case study, the financial wellbeing of academic staff is significantly influenced by two different attributes, namely, financial behavior and financial stress. It also may help decision makers or universities management in improving their staff in financial wellness and wellbeing.
{"title":"Removing Unclassified Elements in Investigating of Financial Wellbeing Attributes Using Rough-Regression Model","authors":"R. Efendi, Susnaningsih Mu’at, Nelsy Arisandi, N. Samsudin","doi":"10.1145/3316615.3316651","DOIUrl":"https://doi.org/10.1145/3316615.3316651","url":null,"abstract":"In economics research survey, the causal relationship between independent and dependent attributes has been frequently investigated by using regression linear models. However, not easy to achieve the high R-square value between both attributes if there are too many unclassified elements in data sets. This paper presents removing unclassified elements in conventional regression model using rough sets approximation. The proposed model is address to handle the unclassified academic staffs in data set which less contribution for supporting financial wellbeing decision. The result showed that number of unclassified staff has a positive effect to increase coefficient determination (R-square) value in the regression model. In this case study, the financial wellbeing of academic staff is significantly influenced by two different attributes, namely, financial behavior and financial stress. It also may help decision makers or universities management in improving their staff in financial wellness and wellbeing.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124862858","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}
A. Jamal, Deperkdharrshan Kumar, Rabab Alayham Abbas Helmi, Sim Liew Fong
Nowadays, internet has become an important part of everyone's lives. The internet has turned out to be a reliable tool for communication, data transfer and many more applications for people from all over the world. An individual's internet activities are being tracked and traced without their knowledge by various means. Thus, online security is a genuine worry for web clients who intend to visit interpersonal interaction locales, make an online transaction, or take an interest in internet diversions, as they need their protection to be ensured. Internet browsing is not anonymized as many Internet Service Providers (ISP's) could legally sell users information without informing users. People's internet activities are being tracked and traced by ISPs. Moreover, network spies and snooping attempts are very hard to be defeated as an ample network snooping tools are available in the market. These issues can be curbed by using a router that has anonymous browsing infrastructure. However, most of the routers that gives anonymous browsing are not portable. From this project, people will be able to browse anonymously without being traced. Besides that, network snooping and spying attempts can be defeated. Finally, a portable The Onion Routing (TOR) router can be created with a Raspberry Pi..
{"title":"Portable TOR Router with Raspberry Pi","authors":"A. Jamal, Deperkdharrshan Kumar, Rabab Alayham Abbas Helmi, Sim Liew Fong","doi":"10.1145/3316615.3316694","DOIUrl":"https://doi.org/10.1145/3316615.3316694","url":null,"abstract":"Nowadays, internet has become an important part of everyone's lives. The internet has turned out to be a reliable tool for communication, data transfer and many more applications for people from all over the world. An individual's internet activities are being tracked and traced without their knowledge by various means. Thus, online security is a genuine worry for web clients who intend to visit interpersonal interaction locales, make an online transaction, or take an interest in internet diversions, as they need their protection to be ensured. Internet browsing is not anonymized as many Internet Service Providers (ISP's) could legally sell users information without informing users. People's internet activities are being tracked and traced by ISPs. Moreover, network spies and snooping attempts are very hard to be defeated as an ample network snooping tools are available in the market. These issues can be curbed by using a router that has anonymous browsing infrastructure. However, most of the routers that gives anonymous browsing are not portable. From this project, people will be able to browse anonymously without being traced. Besides that, network snooping and spying attempts can be defeated. Finally, a portable The Onion Routing (TOR) router can be created with a Raspberry Pi..","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125512137","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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