Pub Date : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043233
Ilyas Adeleke Jimoh, I. Ismaila, M. Olalere
Botnet is one of the major security threats in the field of information technology today (IT). The increase in the rate of attack on industrial IT infrastructures, theft of personal data and attacks on financial information is becoming critical. Majority of available dataset for botnet detection are very old and may not be able to stand the present reality in this research area. One of the latest dataset from Canadian Institute of Cyber Security labeled “CICIDS2017” was noted as an imbalance data distribution ratio of 99% to 1%. This distribution represents majority to minority class ratio. This may pose a challenge of over-fitting in majority class to the research and create a bias in the analysis of results. This research work has adopted J48 decision tree machine learning algorithm with application of SMOTE technique in solving the problem of imbalance dataset, thereby leading to an improved detection of botnets. The accuracy of the highest scenario was 99.95%. This is a significant improvement in detection rate compare to the previous research work.
{"title":"Enhanced Decision Tree-J48 With SMOTE Machine Learning Algorithm for Effective Botnet Detection in Imbalance Dataset","authors":"Ilyas Adeleke Jimoh, I. Ismaila, M. Olalere","doi":"10.1109/ICECCO48375.2019.9043233","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043233","url":null,"abstract":"Botnet is one of the major security threats in the field of information technology today (IT). The increase in the rate of attack on industrial IT infrastructures, theft of personal data and attacks on financial information is becoming critical. Majority of available dataset for botnet detection are very old and may not be able to stand the present reality in this research area. One of the latest dataset from Canadian Institute of Cyber Security labeled “CICIDS2017” was noted as an imbalance data distribution ratio of 99% to 1%. This distribution represents majority to minority class ratio. This may pose a challenge of over-fitting in majority class to the research and create a bias in the analysis of results. This research work has adopted J48 decision tree machine learning algorithm with application of SMOTE technique in solving the problem of imbalance dataset, thereby leading to an improved detection of botnets. The accuracy of the highest scenario was 99.95%. This is a significant improvement in detection rate compare to the previous research work.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132702679","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043268
E. A. Aja, Sadiq Thomas, Oluwatomisin E. Aina, I. D. Inuwa, B. Saka, Y. S. Mohammed
The fuel cell technology has wide range of applications, such as seen in the driving of electric cars and powering of electrical appliances. This paper describes how water an end product from air-conditioner is passed through different chambers which houses the machines that will harness the hydrogen gas into its tank and subsequently used to generate electricity for household appliances. The process involves three phases which are the air-conditioner phase, the chambers phase and the house phase. With the right connection as described by this paper, electricity would be generated for the household electrical appliances thereby reducing dependency on the nominal power from the grid. This technology is an alternative way of generating power for our everyday use, free from atmospheric toxics and ozone friendly.
{"title":"A Fuel Cell technology approach to generate electricity using air-conditioner water","authors":"E. A. Aja, Sadiq Thomas, Oluwatomisin E. Aina, I. D. Inuwa, B. Saka, Y. S. Mohammed","doi":"10.1109/ICECCO48375.2019.9043268","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043268","url":null,"abstract":"The fuel cell technology has wide range of applications, such as seen in the driving of electric cars and powering of electrical appliances. This paper describes how water an end product from air-conditioner is passed through different chambers which houses the machines that will harness the hydrogen gas into its tank and subsequently used to generate electricity for household appliances. The process involves three phases which are the air-conditioner phase, the chambers phase and the house phase. With the right connection as described by this paper, electricity would be generated for the household electrical appliances thereby reducing dependency on the nominal power from the grid. This technology is an alternative way of generating power for our everyday use, free from atmospheric toxics and ozone friendly.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123516692","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043220
Oluwatomisin E. Aina, Steve A. Adeshina, A. Aibinu
Cervical cancer is the fourth most common type of cancer found in females with a record of 570,000 incidences and 311,000 deaths in the year 2018 worldwide. It is caused by a virus known as Human Papilloma Virus (HPV). Screening if done early can reduce this prevalence. However, manual screening methods are not efficient in the detection of cervical cancer as a result of some factors. This, however, results in misdiagnosis and over-treatment. Therefore, researchers proposed screening cervical automatically by using traditional and deep learning techniques. This paper aims to review past work that has been done particularly in the deep learning domain and discusses future directions in the automated detection of cervical cancer. It is believed that this will ensure proper diagnosis and could potentially reduce the prevalence of cervical cancer.
{"title":"Deep Learning for Image-based Cervical Cancer Detection and Diagnosis — A Survey","authors":"Oluwatomisin E. Aina, Steve A. Adeshina, A. Aibinu","doi":"10.1109/ICECCO48375.2019.9043220","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043220","url":null,"abstract":"Cervical cancer is the fourth most common type of cancer found in females with a record of 570,000 incidences and 311,000 deaths in the year 2018 worldwide. It is caused by a virus known as Human Papilloma Virus (HPV). Screening if done early can reduce this prevalence. However, manual screening methods are not efficient in the detection of cervical cancer as a result of some factors. This, however, results in misdiagnosis and over-treatment. Therefore, researchers proposed screening cervical automatically by using traditional and deep learning techniques. This paper aims to review past work that has been done particularly in the deep learning domain and discusses future directions in the automated detection of cervical cancer. It is believed that this will ensure proper diagnosis and could potentially reduce the prevalence of cervical cancer.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115793582","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043278
Oluwasegun Adelaiye, A. Ajibola
Advanced Persistent Threat is a targeted attack method used to maintain undetected unauthorized access over an extended period to exfiltrate valuable data. The inability of traditional methods in mitigating this attack is a major problem, which poses huge threats to organizations. This paper proposes the combined use of pattern recognition and machine learning based techniques in militating the attack. Using basic statistical test approach, a dataset containing 1,047,908 PCAP instances is analyzed and results show patterns exist in identifying between malicious data traffic and normal data traffic. The machine learning on the other hand, is evaluated using three algorithms successfully: KNN, Decision Tree and Random Forest. All algorithms showed very high accuracies in correctly classifying the data traffic. Using the algorithm with the highest accuracy, Random Forest is optimized for better effectiveness.
{"title":"Mitigating Advanced Persistent Threats Using A Combined Static-Rule And Machine Learning-Based Technique","authors":"Oluwasegun Adelaiye, A. Ajibola","doi":"10.1109/ICECCO48375.2019.9043278","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043278","url":null,"abstract":"Advanced Persistent Threat is a targeted attack method used to maintain undetected unauthorized access over an extended period to exfiltrate valuable data. The inability of traditional methods in mitigating this attack is a major problem, which poses huge threats to organizations. This paper proposes the combined use of pattern recognition and machine learning based techniques in militating the attack. Using basic statistical test approach, a dataset containing 1,047,908 PCAP instances is analyzed and results show patterns exist in identifying between malicious data traffic and normal data traffic. The machine learning on the other hand, is evaluated using three algorithms successfully: KNN, Decision Tree and Random Forest. All algorithms showed very high accuracies in correctly classifying the data traffic. Using the algorithm with the highest accuracy, Random Forest is optimized for better effectiveness.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122119063","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043266
Nurseitov Daniyar, B. Kairat, Kanatov Maksat, Alimova Anel
The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.
{"title":"Classification of handwritten names of cities using various deep learning models","authors":"Nurseitov Daniyar, B. Kairat, Kanatov Maksat, Alimova Anel","doi":"10.1109/ICECCO48375.2019.9043266","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043266","url":null,"abstract":"The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123893400","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043285
N. Ibragimov, Alina Amangeldiyeva
There is an increasing trend in the application of integrated Quick Response(QR) and RFID technologies to large-scale events. QR codes and RFID readers containing logs data were introduced into an international computer project competition organized for students in the university. This paper aims to discover the perspectives of Integrated Technologies, which has been created and show how this integration affects to optimization and efficiency of the processes. The presented study is based on the application of the Human Resource Management System for the education sector(e-HRMS), a centralized platform that powers and manages the structure of international competitions. The study findings reveal that the integration of ICT technologies positively affects optimization and process management in the education sector. Moreover, the most important parameter that affects both technologies has been found and checked clearly.
{"title":"Perspectives of Integration QR Codes and RFID readers in large-scale events controlled by HRM systems","authors":"N. Ibragimov, Alina Amangeldiyeva","doi":"10.1109/ICECCO48375.2019.9043285","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043285","url":null,"abstract":"There is an increasing trend in the application of integrated Quick Response(QR) and RFID technologies to large-scale events. QR codes and RFID readers containing logs data were introduced into an international computer project competition organized for students in the university. This paper aims to discover the perspectives of Integrated Technologies, which has been created and show how this integration affects to optimization and efficiency of the processes. The presented study is based on the application of the Human Resource Management System for the education sector(e-HRMS), a centralized platform that powers and manages the structure of international competitions. The study findings reveal that the integration of ICT technologies positively affects optimization and process management in the education sector. Moreover, the most important parameter that affects both technologies has been found and checked clearly.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125127306","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043284
Oluwaseyi Olorunshola, Ayanfeoluwa Oluyomi
The usage of the Android Operating System (OS) has surpassed all other operating systems and as a result, it has become the primary target of attackers. Many attacks can be geared towards Android phones mainly using application installation. These third-party applications first seek permission from the user before installation. Some of the permissions can be elusive evading the users’ attention. With the type of harm that can be done which include illegal extraction and transfer of the users’ data, spying on the users and so on there is a need to have a heuristic approach in the detection of malware. In this research work, some classification algorithms were tested to determine the best performing algorithm when it comes to the detection of android malware detection. An android application dataset was obtained from figshare and used in the Waikato Environment for Knowledge Analysis (WEKA) for training and testing, it was measured under accuracy, false-positive rate, precision, recall, f-measure, Receiver Operating Curve (ROC) and Root Mean Square Error (RMSE). It was discovered that multi-layer perceptron performs best with an accuracy of 99.4%.
安卓操作系统(Android Operating System, OS)的使用率已经超过了其他所有操作系统，因此成为了攻击者的首要目标。许多针对Android手机的攻击主要是利用应用程序安装。这些第三方应用程序在安装前首先需要获得用户的许可。有些权限可能是难以捉摸的，逃避了用户的注意。由于可能造成的危害类型包括非法提取和传输用户数据，监视用户等，因此需要采用启发式方法来检测恶意软件。在本研究工作中，对一些分类算法进行了测试，以确定在检测android恶意软件时表现最好的算法。从figshare获取android应用程序数据集，在Waikato Environment for Knowledge Analysis (WEKA)中进行训练和测试，测量准确率、假阳性率、准确率、召回率、f-measure、受试者工作曲线(ROC)和均方根误差(RMSE)。结果表明，多层感知器的准确率最高，达到99.4%。
{"title":"ANDROID APPLICATIONS MALWARE DETECTION: A Comparative Analysis of some Classification Algorithms","authors":"Oluwaseyi Olorunshola, Ayanfeoluwa Oluyomi","doi":"10.1109/ICECCO48375.2019.9043284","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043284","url":null,"abstract":"The usage of the Android Operating System (OS) has surpassed all other operating systems and as a result, it has become the primary target of attackers. Many attacks can be geared towards Android phones mainly using application installation. These third-party applications first seek permission from the user before installation. Some of the permissions can be elusive evading the users’ attention. With the type of harm that can be done which include illegal extraction and transfer of the users’ data, spying on the users and so on there is a need to have a heuristic approach in the detection of malware. In this research work, some classification algorithms were tested to determine the best performing algorithm when it comes to the detection of android malware detection. An android application dataset was obtained from figshare and used in the Waikato Environment for Knowledge Analysis (WEKA) for training and testing, it was measured under accuracy, false-positive rate, precision, recall, f-measure, Receiver Operating Curve (ROC) and Root Mean Square Error (RMSE). It was discovered that multi-layer perceptron performs best with an accuracy of 99.4%.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121343085","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043217
Sadiq M. Bammami, E. Okafor, S. Hussein, M. Bammami, Sadiq Thomas, Omotayo Oshiga
this research is aimed at modeling and performing structural analysis on Gravity light system using bond graph method in order to improve its performance. To achieve the bond graph model of the gravity light system, the various subsystems, storage elements, junction structures, transformer elements with appropriate causality assignments and energy exchange that make up the gravity light system were identified and modeled. In the developed model, the effect of friction was considered. 20-Sim software was used to validate the developed bond graph model. The structural design and analysis of the system carried out shows that, it is not just another renewable energy generating source but one that improves greatly, the exiting gravity light system by extended operational time of 40 minutes and higher efficiency of over 80%. Hence, it meets all requirements for the application of renewable energy sources, and technically viable for construction and further developments.
{"title":"Improvement Of Gravity Light Generation Modeling Using Bond Graph Method","authors":"Sadiq M. Bammami, E. Okafor, S. Hussein, M. Bammami, Sadiq Thomas, Omotayo Oshiga","doi":"10.1109/ICECCO48375.2019.9043217","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043217","url":null,"abstract":"this research is aimed at modeling and performing structural analysis on Gravity light system using bond graph method in order to improve its performance. To achieve the bond graph model of the gravity light system, the various subsystems, storage elements, junction structures, transformer elements with appropriate causality assignments and energy exchange that make up the gravity light system were identified and modeled. In the developed model, the effect of friction was considered. 20-Sim software was used to validate the developed bond graph model. The structural design and analysis of the system carried out shows that, it is not just another renewable energy generating source but one that improves greatly, the exiting gravity light system by extended operational time of 40 minutes and higher efficiency of over 80%. Hence, it meets all requirements for the application of renewable energy sources, and technically viable for construction and further developments.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129391514","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043240
Isa Muslu, Moussa Mahamat Boukar, O. Gurbuz
Integral equations arises in many physical applications like potential theory and Dirichlet problems, electrostatics, mathematical problems of radiation equilibrium, the particle transport problems of astrophysics and radiation heat problems etc. Integral equations course is one of the main courses in engineering faculties. The aim of this work is to develop an interactive digital learning material, a question bank using Wildcards, on an automation management system Moodle. To create the digital material Wildcard technology of the Moodle is used.
{"title":"Developing a Digital Interactive Course Material on Automated Management System Moodle for Integral Equations Course","authors":"Isa Muslu, Moussa Mahamat Boukar, O. Gurbuz","doi":"10.1109/ICECCO48375.2019.9043240","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043240","url":null,"abstract":"Integral equations arises in many physical applications like potential theory and Dirichlet problems, electrostatics, mathematical problems of radiation equilibrium, the particle transport problems of astrophysics and radiation heat problems etc. Integral equations course is one of the main courses in engineering faculties. The aim of this work is to develop an interactive digital learning material, a question bank using Wildcards, on an automation management system Moodle. To create the digital material Wildcard technology of the Moodle is used.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115170615","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043236
A. Musa, A. Abubakar, Usman Abdul Gimba, Rasheed Abubakar Rasheed
Peer-to-peer networks’ ability to be used as the primary vector of delivery in a highly sensitive environment poses many dangers. The impacts of the hazards on security can be measured when using peer-to-peer networks in a standard computing environment. The transfer of data across multiple network data-centers exposes the system to a potential insider threat, remote control, and the possible leak of private and confidential information. In this paper, we present an analysis of peer-to-peer network security. This follows a description of existing techniques to prevent potential threats to peer-to-peer networks and finally the paper presents a proposed technique to enhance the security of peer-to-peer networks using simulation based experimental study.
{"title":"An Investigation into Peer-to-Peer Network Security Using Wireshark","authors":"A. Musa, A. Abubakar, Usman Abdul Gimba, Rasheed Abubakar Rasheed","doi":"10.1109/ICECCO48375.2019.9043236","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043236","url":null,"abstract":"Peer-to-peer networks’ ability to be used as the primary vector of delivery in a highly sensitive environment poses many dangers. The impacts of the hazards on security can be measured when using peer-to-peer networks in a standard computing environment. The transfer of data across multiple network data-centers exposes the system to a potential insider threat, remote control, and the possible leak of private and confidential information. In this paper, we present an analysis of peer-to-peer network security. This follows a description of existing techniques to prevent potential threats to peer-to-peer networks and finally the paper presents a proposed technique to enhance the security of peer-to-peer networks using simulation based experimental study.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114084045","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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