Detecting Online Recruitment Fraud Using Machine Learning

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 scammers. In this paper, we have proposed a solution on how to detect ORF. We have presented our results based on the previous model and the methodologies, to create the ORF detection model where we have used our own dataset. We have created our dataset based on the Bangladesh job field and by using a publicly accessible dataset as a reference. Furthermore, Logistic Regression, AdaBoost, Decision Tree Classifier, Random Forest Classifier, Voting Classifier, LightGBM, Gradient Boosting are the algorithms that have been used. We have found the accuracy of different prediction models, where LightGBM (95.17%) and Gradient Boosting (95.17%) give the highest accuracy. Through this paper, we tried to create a precise way for detecting fraudulent hiring posts.