Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches

Online Recruitment Fraud (ORF) has become a significant cyber threat in recent years, targeting job seekers through fraudulent job ads, deceptive emails, and misleading recruitment websites.As more individuals turn to online platforms for job searches and hiring, cybercriminals have discovered new ways to exploit people by impersonating legitimate companies and offering fake job opportunities.Victims of ORF often face financial losses, identity theft, and emotional distress.Traditional fraud detection methods, such as rule-based systems and manual verification, have proven ineffective in combating these sophisticated scams due to their ever-changing nature.In response, deep learning techniques have gained significant attention for their ability to automatically learn complex patterns and anomalies from large datasets.Models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks offer promising solutions for detecting fraud in online recruitment platforms.These models can analyze various features, such as textual content, user behavior, communication patterns, and job posting characteristics, to accurately distinguish between legitimate and fraudulent recruitment activities.This project aims to develop a deep learning-based framework for detecting Online Recruitment Fraud, enhancing the security of online job portals, and protecting job seekers from potential scams.By leveraging advanced machine learning techniques, the proposed system seeks to automatically identify suspicious recruitment activities, reduce false positives, and improve the overall trustworthiness of online hiring platforms.Implementing intelligent fraud detection systems like this is essential for ensuring safe and reliable digital recruitment practices in the rapidly evolving cyber environment.