Online Recruitment Fraud Detection Using Machine Learning and Deep Learning Techniques
S. Mano Venkat1, P. Madhuri2, P. Chandrika3, M. Prasanth4, K. Harika5
1Assisstant Professor, Computer Science And Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam, Andhra Pradesh, India
2,3,4,5th B.Tech Final Semester, Bachelor of Technology, Computer Science And Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam, Andhra Pradesh, India
Abstract - The rapid growth of online recruitment platforms has transformed the hiring process for both employers and job seekers. However, it has also increased the number of fraudulent job postings and fake recruiters, leading to financial loss and misuse of personal information. Therefore, detecting recruitment fraud has become essential to ensure the reliability and security of online job portals. Existing systems rely on manual verification and traditional machine learning algorithms such as Support Vector Machines (SVM) to identify fraudulent job postings by analyzing features like job descriptions, company profiles, salary details, and job requirements. Although these methods assist in fraud detection, their performance is limited when handling complex data patterns, achieving an accuracy of around 70%.
To address these limitations, the proposed system utilizes advanced machine learning techniques including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Random Forest algorithms. These models effectively learn complex patterns from large datasets and improve classification performance. By analyzing recruitment-related features, the proposed system achieves an accuracy of approximately 90%, thereby enhancing fraud detection efficiency and ensuring a safer and more reliable online recruitment environment.
Key Words: Online Recruitment Fraud (ORF), Deep Learning, Fraud Detection, Neural Networks, Python, Flask, Web Application, Machine Learning, Data Classification, Recruitment Systems
