Open Journal for Information Technology最新文献

Decision Making on Introducing of Blockchain Technology in Croatian Public Administration 关于在克罗地亚公共管理部门引入区块链技术的决策

Open Journal for Information Technology Pub Date : 2024-05-10 DOI: 10.32591/coas.ojit.0701.01001k

Alen Kišić, N. Vrček

引用次数: 0

A Classifier Model to Detect Phishing Emails Using Ensemble Technique 利用集合技术检测网络钓鱼邮件的分类器模型

Open Journal for Information Technology Pub Date : 2023-12-24 DOI: 10.32591/coas.ojit.0602.06157n

Fredrick Nthurima, Abraham Matheka

{"title":"A Classifier Model to Detect Phishing Emails Using Ensemble Technique","authors":"Fredrick Nthurima, Abraham Matheka","doi":"10.32591/coas.ojit.0602.06157n","DOIUrl":"https://doi.org/10.32591/coas.ojit.0602.06157n","url":null,"abstract":"Phishing attacks usually take advantage of weaknesses in the way users behave. An attacker sends an email to the recipient that mimics a genuine email with phishing links. When the recipient clicks on the embedded links, the attacker can harvest critical information like credit card numbers, usernames or passwords as a result of entering the compromised account. Online surveys have put phishing attacks as the leading attack for web content, mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attacks is about $1.5 billion annually. This is a global threat to information security, and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these losses and reputation injury. This research paper explores and reports the use of multiple machine learning models by using an algorithm called Random Forest and using more phishing email features to improve the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier to detect and prevent phishing emails and test the model with existing data. A dataset consisting of benign and phishing emails will be used to conduct supervised learning by the model. Expected accuracy is 99.9%, with a rate of less than 0.1% for False Negatives (FN) and False Positives (FP).","PeriodicalId":210545,"journal":{"name":"Open Journal for Information Technology","volume":"2007 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160304","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}

引用次数: 0

Detecting Phishing Emails Using Random Forest and AdaBoost Classifier Model 利用随机森林和AdaBoost分类器模型检测钓鱼邮件

Open Journal for Information Technology Pub Date : 2023-11-14 DOI: 10.32591/coas.ojit.0602.03123n

Fredrick Nthurima, Abraham Mutua, Waithaka Stephen Titus

{"title":"Detecting Phishing Emails Using Random Forest and AdaBoost Classifier Model","authors":"Fredrick Nthurima, Abraham Mutua, Waithaka Stephen Titus","doi":"10.32591/coas.ojit.0602.03123n","DOIUrl":"https://doi.org/10.32591/coas.ojit.0602.03123n","url":null,"abstract":"Phishing attack occurs when a phishing email which is a legitimate-looking email, designed to lure the recipient into believing that it is a genuine email to open and click malicious links embedded into the email. This leads to user reveal sensitive information such as credit card number, usernames or passwords to the attacker thereby gaining entry into the compromised account. Online surveys have put phishing attack as the leading attack for web content mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attack is about $1.5 billion per year. This is a global threat to information security and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these loses and reputation injury. This research paper explores and reports the use of a combination of machine learning algorithms; Random Forest and AdaBoost and use of more phishing email features in improving the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier which can detect phishing and prevent phishing emails and test the model with existing data. A dataset consisting of both benign and phishing emails will be used to conduct a supervised learning by the model. Expected accuracy is 99.9%, False Negative (FN) and False Positive (FP) rates of 0.1% and below.","PeriodicalId":210545,"journal":{"name":"Open Journal for Information Technology","volume":"26 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991766","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}

引用次数: 0

A Hybrid Model for Detecting Insurance Fraud Using K-Means and Support Vector Machine Algorithms 使用 K-Means 和支持向量机算法检测保险欺诈的混合模型

Open Journal for Information Technology Pub Date : 2023-11-14 DOI: 10.32591/coas.ojit.0602.05143m

Brian Ndirangu Muthura, Abraham Matheka

{"title":"A Hybrid Model for Detecting Insurance Fraud Using K-Means and Support Vector Machine Algorithms","authors":"Brian Ndirangu Muthura, Abraham Matheka","doi":"10.32591/coas.ojit.0602.05143m","DOIUrl":"https://doi.org/10.32591/coas.ojit.0602.05143m","url":null,"abstract":"Private stakeholders and governments across the globe are striving to improve the quality and access of healthcare services to citizens. The need to improve healthcare services, coupled with the increase in social awareness and improvement of people’s living standards, has seen an increase in medical policyholders in the insurance industry. Even so, the healthcare sector is grappled with increased costs every other year, leading to revision of premiums and increased costs for the policyholders. One of the main factors contributing to the increased costs is fraudulent claims raised by the service providers and the policyholders, leading to unprecedented risks and losses for insurance firms. The insurance industry has set up fraud detection and mitigation systems to mitigate losses brought about by fraudulent claims, which come in two flavors: rule-based systems and expert claims analysis. With rule-based systems, conditions such as missing details, location of the claim vis a vis the location of the policyholder, among other rules, are evaluated by systems to assess the validity of the claims. On the other hand, insurance firms rely on the human intervention of experts using statistical analyses and artificial rules to detect fraudulent claims. The rule-based and expert analysis methods fail to detect patterns or anomalies in claims, which is central to efficient fraud detection. Data mining and machine learning techniques are being leveraged to detect fraud. This automation presents enormous opportunities for identifying hidden patterns for further analysis by insurance firms. This research aims to analyze a hybrid approach to detect medical insurance fraud using both K-Means (unsupervised) and Support Vector Machines (supervised) machine learning algorithms.","PeriodicalId":210545,"journal":{"name":"Open Journal for Information Technology","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277388","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}

引用次数: 0

Managing the Implementation of Information Technology in Schools 学校信息技术实施管理

Open Journal for Information Technology Pub Date : 2023-11-14 DOI: 10.32591/coas.ojit.0602.04137s

Alaa Sarsour, Raed Sarsour

引用次数: 0

Automated Assessment System Using Machine Learning Libraries 使用机器学习库的自动评估系统

Open Journal for Information Technology Pub Date : 2023-10-25 DOI: 10.32591/coas.ojit.0602.02097o

Adebola Victor Omopariola, Chukwudi Nnanna Ogbonna, Felix Uloko, Monday Abdullahi

{"title":"Automated Assessment System Using Machine Learning Libraries","authors":"Adebola Victor Omopariola, Chukwudi Nnanna Ogbonna, Felix Uloko, Monday Abdullahi","doi":"10.32591/coas.ojit.0602.02097o","DOIUrl":"https://doi.org/10.32591/coas.ojit.0602.02097o","url":null,"abstract":"Assessment and the grading of students is a task that has been done for as long as school has existed. This was previously done by teachers in primary and secondary, lecturers for institutions like JAMB and lecturers in schools. Up until now students’ marks were influenced by other external factors such as bad handwriting, lengthy paragraphs, roundabout way of speaking rather than going straight to the point and the sheer number of assignments the lecturer has to mark. This has resulted in students getting lower or higher marks than they should be awarded. This project is to create an ML (Machine Learning) powered assessment system that will take the assignment questions and the marking scheme and award the student the marks similar to what the ideal lecturer would have given. This will also reduce the time the lecturers spend on marking and ensure the students get their results on time. This project will be made with Python and machine learning and will be tested with a number of potential answers to the questions and their grading’s. This will enable system to be able to grade assignments as soon as they are uploaded. This research will be limited by the fact that the system can only handle the marking of short sentences accurately and not long paragraphs. The system is also limited by the fact that it can only mark with the aid of the marking scheme and not without it so it is not a truly intelligent model in that regard. The research showed that the system is indeed capable of obtaining the similarity between two paragraphed answers provided but it needs extras to produce the most accurate results.","PeriodicalId":210545,"journal":{"name":"Open Journal for Information Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168187","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}

引用次数: 0

Design and Implementation of a Tailoring Management System (Virlor) 一个裁剪管理系统(Virlor)的设计与实现

Open Journal for Information Technology Pub Date : 2023-05-27 DOI: 10.32591/coas.ojit.0602.01067o

Adebola Victor Omopariola, Raphael Ozighor Enihe, Chukwudi Nnanna Ogbonna, Felix Uloko, Manasseh Chukwudubem Ezeocha, Monday J. Abdullahi

{"title":"Design and Implementation of a Tailoring Management System (Virlor)","authors":"Adebola Victor Omopariola, Raphael Ozighor Enihe, Chukwudi Nnanna Ogbonna, Felix Uloko, Manasseh Chukwudubem Ezeocha, Monday J. Abdullahi","doi":"10.32591/coas.ojit.0602.01067o","DOIUrl":"https://doi.org/10.32591/coas.ojit.0602.01067o","url":null,"abstract":"The tailoring method has a popular view of being a Manuel method, in which clients must attain out to the tailor physically, select the materials for their clothing, provide their measurements, and, in most circumstances, return to the tailor shop to pick it up, consuming more time and more resources. as time progresses the provision of services has modernized, and even preference of service provision of customers has also modernized as well, customers of recent would prefer to employ mobile services that can easily reach them rather than Manuel conventional service deliveries. in this recent society, the majority would prefer a service that is automated to the point they put little or no effort into acquiring these serviced thus the goal of this project, the project is aims to automate the tailoring management services which is manually maintained. After the automation this will mean better services, data security, quick search, and also paperless environment. The project’s major goal is to automate these services in such a way that tailors will have more work opportunities. Every user of the system will have to log into the system using username and password so that security and authentication will be ensured. After logging in, a consumer can place an order, monitor the status of their outfit, and even provide feedback. This system would aid both tailors and customers as it improves effectiveness and efficiency, this system also help in realizing the vision 2030 where the Nigerian government wants its people to be digitally informed and automate all government bodies and ministries, thereby embracing Electronic Governing.","PeriodicalId":210545,"journal":{"name":"Open Journal for Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116894437","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}

引用次数: 0

Cyberdeviance in the Western Balkans and ICT-Media-Based Protection 西巴尔干地区的网络越轨行为与基于信息通信技术媒体的保护

Open Journal for Information Technology Pub Date : 2023-05-16 DOI: 10.32591/coas.ojit.0601.05059k

Ilda Kashami, Arjan Curi

引用次数: 0

Educational Data Analytics and Fog Computing in Education 4.0 教育数据分析和雾计算4.0

Open Journal for Information Technology Pub Date : 2023-04-12 DOI: 10.32591/coas.ojit.0601.04047m

Jackson Machii, Julius Murumba, E. Micheni

引用次数: 0

Drivers and Barriers to Adopting ICT: Mexican EFL Primary School Teachers’ Perceptions 采用ICT的驱动因素与障碍:墨西哥英语小学教师的认知

Open Journal for Information Technology Pub Date : 2023-03-23 DOI: 10.32591/coas.ojit.0601.03033g

José Joaquín Enrique Erguera Guerreo

引用次数: 0