Detecting Online Recruitment Fraud with AI

Online Recruitment Fraud ORF Detection Using Deep Learning Approaches

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Summary

This video delves into the increasingly critical issue of Online Recruitment Fraud (ORF) and demonstrates how deep learning can be leveraged to tackle this threat. The video explains the evolution of recruitment processes from offline to online platforms, highlighting the convenience and new issues like ORF, where scammers exploit job portals. Traditional detection methods struggle due to data imbalance and advanced scam tactics. The video showcases a system employing advanced deep learning models, including Transformers like BERT and RoBERTa, combined with datasets from different regions to enhance fraud detection accuracy. The addition of CNN 2D models further refines detection accuracy, aiding in classifying job postings more effectively, thus safeguarding job seekers and maintaining the credibility of online recruitment platforms.

Highlights

The digital shift in recruitment has unfortunately opened the door to fraudsters. 💻
Fraudsters post fake job ads, collect user data fraudulently, and sometimes even extort money. 🚨
The traditional detection methods fall short due to data imbalance and advanced fraudulent tactics. 📊
Using deep learning models like BERT and RoBERTa can significantly enhance detection by understanding text semantics. 📚
The introduction of CNN 2D models provides even greater accuracy and effectiveness in fraud detection systems. 🧠

Key Takeaways

Online recruitment has shifted job hunting into the digital realm, offering convenience but also scams. 🕵️‍♂️
Scammers pose as employers to trick job seekers, making robust fraud detection systems crucial. 💼
Traditional detection methods are not enough anymore; they don’t cope well with imbalanced data and new scam tactics. 🔍
Advanced deep learning models like BERT and RoBERTa can vastly improve fraud detection by understanding job description contexts. 🤖
Enhancing models with CNN 2D helps capture more refined features, boosting accuracy and reliability. 📈

Overview

In this informative video, TRU Projects explores the new-age challenges of Online Recruitment Fraud (ORF) and the innovative deep learning approaches that counter it. With the shift from offline to online recruitment, scammers have found a fertile ground to deceive job seekers, prompting a need for advanced detection methods. Traditional approaches don’t cut it anymore, thanks to data imbalances and ever-evolving scam tactics. The presenter highlights how deep learning models, particularly Transformers like BERT and RoBERTa, can revolutionize fraud detection by understanding the context in job postings.

The video explains the use of a combination of different datasets to train these models, targeting the imbalance that disrupts accurate fraud detection. The integration of CNN 2D models is a game-changer; by optimizing feature extraction, these models help in analyzing job data more intricately. Thus, it ensures superior accuracy in distinguishing between fake and genuine job postings, effectively safeguarding online recruitment platforms. This project not only demonstrates technological advancement but also its significant impact in protecting job seekers.

Conclusively, TRU Projects' approach involves a robust system that incorporates user-friendly interfaces while maintaining high security and reliability in predictions. By using a Flask framework, users interact seamlessly with the application, further enhanced by a secure authentication system. This comprehensive project encapsulates deep learning's potential to thwart online recruitment fraud, providing a safer digital space for job seekers and maintaining trust in online recruitment platforms.

Chapters

00:00 - 00:30: Introduction The introduction chapter introduces the topic of online recruitment fraud (ORF) detection using deep learning approaches. It highlights the transformation of recruitment processes from traditional offline methods to more convenient online methods, setting the stage for a discussion on the importance and execution of ORF detection.
00:30 - 01:30: Problem Statement: Online Recruitment Fraud The chapter discusses the problem of online recruitment fraud, which has emerged alongside the shift to online job platforms. These platforms allow job seekers to browse job listings that match their skills and interests, and employers to post job openings with details like roles, responsibilities, and benefits. While convenient and efficient, the use of online platforms has led to an increase in recruitment fraud.
01:30 - 03:00: Challenges in Detecting ORF The chapter titled 'Challenges in Detecting ORF' discusses the issue of fraudulent activities on job portals. It explains how fraudsters exploit these platforms by posting fake job advertisements, deceiving job seekers into providing personal information, and in some cases, extracting money from them. This fraudulent behavior not only harms the individuals who are victimized but also tarnishes the reputation of legitimate organizations inadvertently associated with these fraudulent listings. The problem exacerbated during the COVID-19 pandemic as more people became vulnerable to such scams.
03:00 - 03:30: Proposed Solution: Using Deep Learning Models The chapter "Proposed Solution: Using Deep Learning Models" addresses the problem of online recruitment fraud (ORF), which has been exacerbated by the increase in online job postings. It emphasizes the growing need for improved detection and prevention strategies. Traditional methods leveraging outdated data have proved inadequate as they fail to adapt to the evolving sophistication of scam tactics. The chapter suggests that deep learning models could offer a more effective solution by potentially providing more accurate detection mechanisms that are capable of adapting to new fraud patterns.
03:30 - 04:30: Data Sets Used for Training This chapter discusses the challenges faced in detecting fraudulent job postings, primarily focusing on the evolving nature of fraud tactics and the data imbalance issue in training models. It highlights that there are significantly more legitimate job postings than fraudulent ones, which affects the accuracy of detection models in predicting fake job postings.
04:30 - 05:30: Data Balancing Technique: SMOTE The chapter discusses the limitations of existing fraud detection systems in online recruitment platforms, which leave job seekers vulnerable and damage trust. It highlights the need for more advanced methods, and proposes using advanced deep learning models like Transformers to overcome these issues and enhance the trustworthiness of the platforms.
05:30 - 07:00: Extending the Model with CNN 2D The chapter discusses the extension of a model using 2D Convolutional Neural Networks (CNN). It specifically focuses on using Roberta, a robust model building on BERT (Bidirectional Encoder Representations from Transformers), to detect fake job postings. These models are highlighted for their ability to understand the context of job descriptions more effectively than traditional methods. The training process for these models involves using a combination of three datasets: Real or Fake job posting prediction dataset, India job posting dataset, and the Pakistan job market dataset. This multi-source approach aims to yield a more diverse data collection.
07:00 - 10:00: Steps in Project Implementation In this chapter, the focus is on overcoming challenges in project implementation, specifically in the context of job boosting for fraud detection. The chapter highlights the limitations of older datasets, which are narrower and affect the accuracy and effectiveness of existing systems. During the project, a class imbalance in the dataset was identified, with more legitimate job postings than fake ones. This imbalance poses a challenge to the model's ability to learn and identify fake advertisements effectively. The chapter discusses methodologies to address and fix this imbalance, enhancing the model's performance.
10:00 - 11:30: Executing the Project In the chapter titled 'Executing the Project', the focus is on using data balancing techniques to improve the accuracy of fraud detection systems. Specifically, the Synthetic Minority Oversampling Technique (SMOTE) is utilized to create synthetic samples of underrepresented classes, such as fake job postings. By applying SMOTE, the data set becomes balanced, which enhances the effectiveness of deep learning models used for training. This method is crucial for accurately identifying fake job postings, playing a pivotal role in addressing online recruitment fraud.
11:30 - 13:00: Understanding the Code Structure The chapter titled 'Understanding the Code Structure' discusses enhancements in a project's accuracy through the implementation of a Convolutional Neural Network (CNN) 2D model. The CNN 2D model introduces two-dimensional layers, which allow for the optimization of dataset features. This enhancement enables the model to analyze data more precisely and extract more useful patterns, thereby aiding in better classification of job postings.
13:00 - 15:00: Running the Flask Application In this chapter, the focus is on running the Flask application. The front end of the application is developed using the Flask framework, which integrates a user authentication mechanism. This allows users to interact with the system, test its functionality, and verify that the detection works effectively. The chapter also discusses the importance of user authentication to ensure that only authorized users can access the application, thereby maintaining security while enabling smoother user interaction. The chapter concludes with a brief mention of the project's implementation.
15:00 - 16:00: Uploading Test Data and Viewing Results The chapter 'Uploading Test Data and Viewing Results' begins with an overview of the workflow steps necessary for the project implementation. The initial step involves importing essential Python libraries and tools, such as pandas, NumPy, and PyIC learner, in addition to deep learning frameworks, crucial for data manipulation, machine learning, and model building. Following this, the chapter describes the loading of three datasets: a dataset for fake job posting prediction, the Indeed job posting dataset, and data from Pakistan's job market. These datasets are then analyzed to understand their structure and identify key features.
16:00 - 18:00: Understanding the Test Data Structure This chapter focuses on understanding the structure of test data, starting with examining data quality, which is crucial for deciding on pre-processing and cleaning strategies. It covers the data processing stage, which includes removing unnecessary elements such as punctuations, mentions, and null values. Furthermore, it emphasizes the removal of stop words to enhance the focus on significant keywords within job postings, thereby improving the learning capacity of the model. The chapter also delves into feature extraction, utilizing BERT and RoBERTa models—transformer-based deep learning models renowned for effectively capturing features from job postings.
18:00 - 20:00: Job Type Prediction Results The chapter discusses the process involved in predicting job types with an emphasis on identifying fake job postings. Initially, it talks about the importance of semantic meaning in text for efficiently spotting these fake postings. The chapter then outlines the data preparation steps, highlighting the importance of random shuffling and splitting the data into training (80%) and testing (20%) sets. The purpose of the training set is to build the model, while the testing set evaluates the model's performance on new, unseen data. Additionally, it covers the technique of balancing the dataset using the SMOTE (Synthetic Minority Over-sampling Technique) to address the issue of data imbalance, which is evident with having more real job postings than fake ones.
20:00 - 22:00: Analyzing Prediction Graphs and Metrics The chapter "Analyzing Prediction Graphs and Metrics" discusses the use of Smo BD mode to balance classes by creating synthetic samples for the minority class, specifically fake job postings. The models are then trained on this balanced dataset to learn patterns differentiating between real and fake job postings. The effectiveness of these models is evaluated using metrics like accuracy, precision, recall, and F1 score.
22:00 - 25:00: Conclusion and Resources In the conclusion, the system design is recapped, highlighting the use of Flask for the front end to create an interactive user interface. This includes features for user registration, login, and inputting job postings for classification, using SQLI for secure data storage. The focus is on enabling users to predict the authenticity of job postings.

Online Recruitment Fraud ORF Detection Using Deep Learning Approaches Transcription

00:00 - 00:30 [Music] welcome to True projects in this video we are going to explain about the project that is online recruitment fraud that is ORF detection using deep learning approaches before getting into the execution first let us understand what is the project about internet internet has really changed the way recruitment Works what used to be traditional offline process is now happening online making it easier for
00:30 - 01:00 both job Seekers and employers too companies what do they do is they can post job openings they can share all the details like the roles responsibilities and benefits and job Seekers they can browse through these listings to find opportunities that match their skills and interest it is convenient and efficient for both the sides however with the shift towards this online platforms a new problem has emerged that is online recruitment fraud that is o r f essentially fraudsters
01:00 - 01:30 like Advantage frauders they take advantage of these job portals by posting fake job ads they they tricking the job Seekers into giving away personal information and they are even taking money this is not only this not only harms the individuals who fall victim to these camps but also damages the reputation of organizations that get associated with these frent listing the problem became even worse during covid-19 pandemic when many more
01:30 - 02:00 companies began posting jobs online giving scammers more opportunities to exploit the situation it's a growing issue that highlights the need for better ways to detect and prevent these scams from spreading so tackling this ORF that is online recruitment fraud is tough because this methods have been using so far just aren cutting it traditional detections that we rely on older data sets they don't reflect to the new or more sophisticated ways that scammers
02:00 - 02:30 are opening today these frauders are constantly evolving and the detection models just can't keep up and the other major issue is with the data imbalance there are way more legitimate job postings that means in the data we will be having more genuine postings than the fraudulent job postings this messes with how accurate the models can learn the model learn to predict the whether it is a fake job posting or
02:30 - 03:00 a genuine job posting moving on because of this it is hard for existing systems to effectively spot fraud which leaves job Seekers vulnerable and damages the trust people have in online recruitment Platforms in short the old methods just aren't enough anymore we really need something more advanced to protect users and keep these platforms trustworthy so to overcome all these issues the idea behind this proposed system is to use Advanced deep learning models specifically Transformers like BT
03:00 - 03:30 and robota Bert is bidirectional encoder representations from Transformers and robust optimize BD pre-rain approaches Roberta to detect fake job postings these models are powerful because they can understand the context of the job description much better than the traditional methods to train these models we use combination of three data sets that is real or fake fake job posting prediction data set India job posting data set and Pakistan job market data set by using data from different sources we get more diverse collection
03:30 - 04:00 of job boosting this approach helps us to overcome the limitations of the older more narrower data sets than many existing systems were relying on making the fraud detection process more accurate and effective so during the data analysis we notice that there was a class imbalance there is an imbalance in the data set meaning there was there were more legitimate job postings than the fake ones so this imbalance could make it harder the model to effectively learn how to spot the fake ads so to fix this
04:00 - 04:30 we have used a technique called SM B that is nothing but synthetic minority oversampling technique balanced distribution so what smob does is it generates synthetic samples of the minority class in this case fake job postings helping to balance the data set once the data set is balanced we train the Deep learning models on it this approach ensures that the system can more accurately detect fake job postings which is key in tackling online recruitment fraud
04:30 - 05:00 this is the propose system moving on to the extension part to further improve the accuracy in this project we are incorporating CNN 2D model so here we are introducing convolutional neural network 2D model to further improve the system CN and 2D it uses two dimensional layers that helps to optimize the features in the data set which essentially means it looks at the data in more refined way to extract better more useful patterns this helps the model to classify job postings with even
05:00 - 05:30 better accuracy on top of that we have developed front end using flask framework which is integrated with the user authentication mechanism so this allows users to interact with the system directly test it out and check while the detection Works coming to the part of user authentication we have added user authentication to make sure that only authorized users can access it keeping it secure while allowing for smoother interaction so this is about the extension moving on to the implementation of the project we have
05:30 - 06:00 followed the steps of the flow of work to implement this project so coming to the first step in the first step we will be importing all the necessary python libraries and tools required for data manipulation machine learning and model building like pandas numai pyic Lear and deep learning Frameworks and coming to the next step in the next step we will be loading all the three data sets the data sets are real or fake that is fake job posting prediction Indeed job posting data set and Pakistan's job market that are explored to understand their structure identify features and
06:00 - 06:30 examine data quality this step helps in deciding how to pre-process and clean the data then comes the data processing this involves cleaning the data by removing unnecessary punctuations mentions and null values additionally stop wordss are removed to focus on the most important words in job posting improving the model's ability to learn then comes the feature extraction part features are extracted from job postings using bird and robota models which are Transformer based deep learning models the these models helps to capture the
06:30 - 07:00 semantic meaning of the text which is crucial for identifying fake job postings then comes the part of splitting the data so the data set is randomly shuffled and then split into train and test sets where we assign 80% of the main data to training and 20% for testing here training set is used to train the model and testing set is used to evaluate the train model on unseen data and then we balance the data using SM o BD technique so since the data set is imbalanced that is more real job
07:00 - 07:30 postings are there than the fake ones we have used Smo BD mode is applied to balance the classes by generating synthetic samples for the minority class that is the fake job postings class and the next step we will be initiating the models that will be trained on the train data set training set during training the model learns patterns to differentiate between real and fake job postings based on features extracted earlier and that will be evaluated using accuracy procession recall fence code
07:30 - 08:00 and they will be compared in the comparison bar graphs coming to the part of front end in the front end a front end interface is built using flask a web framework this allows users to interact with the system by inputting job postings for classification user registration and login functionality is incorporated with the sqli to securely store the user data and coming to the part of user gives input here users they will be G able to give input text for predicting whether the job posting is a genuine one or a fake one and that will
08:00 - 08:30 be pre-processed at the back end it will be feeded with the train model finally it will be displayed in the outcome will be displayed in the front end whether it is real or fake moving on to the algorithms used these are the algorithms that are built in this project to classify the job description whether it is fake or Genuine so first every algorithm is trained on the actual data so here B is trained on the actual data and robota model is also trained on the actual data and then we have train this
08:30 - 09:00 bird model on the data which is balanced using this SM o BD SM data similarly the robota model is also trained like that and this is the extension model that is train dot BT with Smo BD and that to CNN 2D coming to the part of requirements to implement this project we need basic requirements which are hardware and software coming to Hardware we need operating system of Windows processor of i5 and above Ram of 8 GB and above and hard disk of 25 GB and above coming to software we need application of python front and framework of flask back framework of jupyter notebook database
09:00 - 09:30 of SQL I 3 and front end Technologies are HTML CSS JavaScript and bootst 4 this is the overview of the project now let us execute to execute we are supposed to open the code folder which contains source code files so let us open it so let's open the code folder so these are the source code files in the code folder of the project let us understand each of them how will they be used in this project here in this the first folder we have data sets used in this project so here the first three
09:30 - 10:00 files represents the training data that we have used in this project let us open the fake job postings one it is being loaded so this is the data set that we have used it for training the models for classifying whether the job is real or freak these are all the features of that data set and this is the target class close this simp similarly we will be
10:00 - 10:30 having in this file and also this file and in this case that is the test data case we have the test cases that we will be using in the front end to detect the whether job posting is fake or Genuine so this is the test data that we are going to upload so these are different different features that we will be having so let's close this get back this is the model folder in which we have the algorithm information that is stored in np. hedan pickle files that
10:30 - 11:00 will be loaded into the project code during execution and here we have static folder which consists of files related to CSS JavaScript and bootstrap files which are important for the visual appeal coming to templates folder this folder contains all the HTML Pages used in this project it typically includes files like index.html about. HTML Etc which represent different pages of the website and this is app.py file this py file contains the information related to front-end logic it includes code written in Python that handles server side
11:00 - 11:30 requests user request interacting with the database and generating Dynamic content to be rendered in HTML templates this is The Notebook file notebook. iynb file this is a Jupiter notebook file which contains the combination of code graphs and outputs all in one place it allows users to write and execute code in individual cells making it a popular choice for the data signs the last file we have is the sign up. DB file this file is a database file which is used to store the user information now we have understood all the files of this code
11:30 - 12:00 folder now let's execute so copy the path of the code folder from the address bar of the file explorer so I'm copying it now open the command prompt it has been opened give the command CD give the space paste the copied path now click on enter Here If You observe current directory is changed to the code folders path now we are supposed to compile the app.py file which is the frontend file now click on enter this will execute python script and perform an untime check for any syntax errors or logical issues and
12:00 - 12:30 after running the app.py file FL framework will host the application locally at the default address which is the local host and the port andless configured differently so this is the local host and this is the port now copy this local link provided by the flas framework and paste it in any web browser I'm copying the link I prefer Google Chrome so I will paste it in that pay the link here click on enter this is the web page of the project
12:30 - 13:00 which is displayed in the browser this is developed using flask framework now we need to sign up first click on sign up and enter all the registration details if you are registering newly I have already registered so I will directly sign in click on sign in and give your username and the password click on login we have logged in successfully and we are redirected to prediction page of the project so here you can see a choose file button right we need to click on that for uploading the test
13:00 - 13:30 cases before uploading the file let's see what we have in the file so let's open the data set folder open the test data so here the First Column which we have is the job ID this is the job ID and here we have title of the job that is the job role and here we have location of particular job and here we have Department that belongs to which kind of department and here we have salary range here we have we have company profile so this is the
13:30 - 14:00 company profile and here we have description of the job rule so this is the description of the job role and here we have requirements this these are the specific requirements that are needed for that job role and here we have benefits that a candidate that a employee will be getting if he gets the job and here we have telecommunication and here we have whether the company has logo or not
14:00 - 14:30 whether the company has telecommunication whether the company has logo or not and here we have questions or not and here this is the employer type whe it is the employment type whether it is full-time or part-time and here we have requirement experience that is required for a candidate to apply the job and here we have required education for getting the job and here we have industry that job belongs to which kind of industry and the last one we have is the functioning whether it is administr to whether it is engineering or customer service so this
14:30 - 15:00 is these are the test cases that we're going to upload let's close this get back to the application click on choose file so this is the data set I mean this is the test data that we're going to upload click on open here it has been loaded now upload it the given input will be pre-processed at the back end and then it will be feeded with the train model and we be getting us the predictions so here here we got the prediction so this these are
15:00 - 15:30 the job related details they are completely clued together and here it has predicted as the job type is real job it is the real job that has been predicted similarly for the second job details for the second test case it has predicted as real job and coming to the third job description that is the job detail here it has predicted as fraudulent job it is the fraudin job accordingly we have for all other test cases prediction also so Here If You observe we have fraud job predicted here we have real job predicted similarly for all the jobs it has been
15:30 - 16:00 predicted now let's check with the graphs click on graph so here the first graph which we have is the sample outcome graph here on x-axis we have class label I mean Target that we going to predict and on Y axis we have count of the target so Here If You observe real jobs are more than the fraudulent jobs it shows a class imbalance right so that is why we have used that smooth technique moving on here we have the confusion Matrix so
16:00 - 16:30 this is the confusion Matrix of the extension model this is the extension models B SM o BD SM with CN and 2D confusion Matrix and here this is the accuracy comparison graph here on xaxis we have accuracy score and on Y axis we have all the algorithms that are trained on the data set to predict the job whether it is real or fake similarly we have pression score recall score and F1 score so these are the metrics used to evaluate all the models that are trained now click on log out in this way just by
16:30 - 17:00 giving the give input related to job it will it will predict I mean the train model will be predicting whether the given job is fake or real this will help employers and also employees to get safe so this is all about the project thank you for watching video for more projects please visit our website www.tr projects. in for updates on latest project videos
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