Detecting online recruitment fraud by using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
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Brac University
2021
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/15174 |
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10361-151742022-01-26T10:20:02Z Detecting online recruitment fraud by using machine learning Ghosh, Gitanjali Tabassum, Hridita Atika, Afra Kutubuddi, Zainab Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Machine Learning Fraud Detection Prediction Decision Tree Classifier Logistic Regression algorithm Adaptive Boosting Random Forest Classifier Decision trees Gradient Boost LightGBM Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 41-43). Online Recruitment fraud (ORF) is becoming an important issue in the cyber-crime region. Companies find it easier to hire people with the help of the internet rather than the old traditional way. But it has greatly attracted the scammers to deceive people and exploit their information. There have been lots of incidents where innocent people have fallen for this malicious fraud and lost millions of money. Even it causes harm to business and the economy. Unlike other cyber-security problems, like email spam, phishing, opinion fraud, detecting Online Recruitment Fraud(ORF) did not get that much of recognition. So, this matter needed to be highlighted more. In this paper, we have proposed a solution on how to detect ORF. We have presented our results based on the previous model and also presented the methodologies which we are going to use to create the ORF detection model where we are using our own dataset. We are going to use a publicly accessible dataset from fake job postings.csv, license-CC0: Public Domain, as a reference for the dataset that we have created. Furthermore, we have collected 4000 data from different job sites in Bangladesh, among which 301 of them are fraudulent. We have used many common and latest classification models to detect which algorithm works best for our model. Logistic Regression, AdaBoost, Decision Tree Classifier, Random Forest, Voting Classifier, LightGBM, Gradient Boosting are the algorithms that have been used. From our observations we have found that the accuracy of different prediction models are: Logistic Regression(94.67%), AdaBoost(95%), Decision Tree Classifier(95%), Random Forest(95%), Voting Classifier(95.34%), LightGBM(95.17%), Gradient Boosting(95.17%). Through this report, we tried to create a precise way for detecting the fraudulent hiring posts. Gitanjali Ghosh Hridita Tabassum Afra Atika Zainab Kutubuddin B. Computer Science 2021-10-07T06:44:36Z 2021-10-07T06:44:36Z 2021 2021-01 Thesis ID 17101228 ID 17101446 ID 17101206 ID 17101198 http://hdl.handle.net/10361/15174 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine Learning Fraud Detection Prediction Decision Tree Classifier Logistic Regression algorithm Adaptive Boosting Random Forest Classifier Decision trees Gradient Boost LightGBM Machine learning |
| spellingShingle |
Machine Learning Fraud Detection Prediction Decision Tree Classifier Logistic Regression algorithm Adaptive Boosting Random Forest Classifier Decision trees Gradient Boost LightGBM Machine learning Ghosh, Gitanjali Tabassum, Hridita Atika, Afra Kutubuddi, Zainab Detecting online recruitment fraud by using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Ghosh, Gitanjali Tabassum, Hridita Atika, Afra Kutubuddi, Zainab |
| format |
Thesis |
| author |
Ghosh, Gitanjali Tabassum, Hridita Atika, Afra Kutubuddi, Zainab |
| author_sort |
Ghosh, Gitanjali |
| title |
Detecting online recruitment fraud by using machine learning |
| title_short |
Detecting online recruitment fraud by using machine learning |
| title_full |
Detecting online recruitment fraud by using machine learning |
| title_fullStr |
Detecting online recruitment fraud by using machine learning |
| title_full_unstemmed |
Detecting online recruitment fraud by using machine learning |
| title_sort |
detecting online recruitment fraud by using machine learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15174 |
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AT ghoshgitanjali detectingonlinerecruitmentfraudbyusingmachinelearning AT tabassumhridita detectingonlinerecruitmentfraudbyusingmachinelearning AT atikaafra detectingonlinerecruitmentfraudbyusingmachinelearning AT kutubuddizainab detectingonlinerecruitmentfraudbyusingmachinelearning |
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