An Intelligent Arabic Model for Recruitment Fraud Detection Using Machine Learning (original) (raw)

An Intelligent Model for Online Recruitment Fraud Detection

Journal of Information Security, 2019

This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

MACHINE LEARNING-BASED FAKE JOB RECRUITMENT DETECTION SYSTEM

IJTRET, 2022

In order to avoid fraudulent online job postings, we use an automated tool that uses natural language processing (NLP) and classification techniques based on machine learning are suggested on paper. Using the NLP library SpaCy in python we have performed various analyzes such as semantic, syntactic, tokenization of the task profile extracting features and using a machine learning algorithm called Random Forest we have predicted its accuracy to classify a job profile as Real or Fake.

Fake E Job Posting Prediction Based on Advance Machine Learning Approachs

International Journal of Research Publication and Reviews, 2022

There are many jobs adverts on the internet, even on reputable job posting sites, that never appear to be false. However, following the selection , the so-called recruiters begin to seek money and bank information. Many candidates fall into their traps and lose a lot of money as well as their existing job. As a result, it is preferable to determine whether a job posting submitted on the site is genuine or fraudulent. Manually identifying it is extremely difficult, if not impossible! An automated online tool (website) based on machine learning-based categorization and algorithms are presented to eliminate fraudulent job postings on the internet. It aids in the detection of bogus job postings among the vast number of postings on the internet.

Identification of Online Recruitment Fraud (ORF) through Predictive Models

Emirati Journal of Business, Economics and Social Studies

Job postings online have become popular these days due to connecting to job seekers around the world. There are also instances where the fraudulent employer posts a job online and expects people to apply to these postings. These fraudulent employers impend job seekers' privacy, spawns fake job offers, and wanes. We perceived that most of the Online Recruitment Fraud (ORF) has matching features. Though the user cannot categorize them, we propose using various predictive models like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, Naïve Bayes, or Logistics Regression to detect them effortlessly. Dataset with 17780 job postings was downloaded from Kaggle to identify which proposed model best predicts the fraudulent job posting. The dataset includes 14 features to determine whether online job posting is fraudulent or non-fraudulent. 70% of these job postings train the model, and the remaining 30% test the model's efficiency. The outcomes of each mode...

Detection of Online Employment Scam through Fake Jobs Using Random Forest Classifier

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model. It helps in detecting fake job posts form an enormous number of posts. Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers.

Fake Job Recruitment Detection Using Machine Learning

2021

It is proposed in this research that a computerised apparatus that makes use of artificial intelligence-based organising strategies in order to avoid deceptive job postings on the internet be developed. Various classifiers are used to check for misleading information on the internet, and the findings of those classifiers are analysed in order to develop the most effective business trick detection model that can be used in the field of information security. When searching for fake job advertisements amid a large number of legitimate job ads, this tool may be really helpful. Solitary classifiers and troupe classifiers, to name a few examples, are two important types of classifiers that are used in the process of spotting bogus job postings on the internet. In any event, the results of the trials demonstrate that aggregating classifiers outperform solo classifiers when it comes to detecting tricks in general.

A machine learning approach to detecting fraudulent job types

AI & SOCIETY

Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of f...

Fake Job Detection Using Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

The research proposes an automated solution based on machine learning-based classification approaches to prevent fraudulent job postings on the internet. Many organizations these days like to list their job openings online so that job seekers may access them quickly and simply. However, this could be a form of scam perpetrated by con artists who offer job seekers work in exchange for money. Many people are duped by this fraud and lose a lot of money as a result. We can determine which job postings are fraudulent and which are not by conducting an exploratory data analysis on the data and using the insights gained. In order to detect bogus posts, a machine learning approach is used, which employs numerous categorization algorithms. The system would train the model to classify jobs as authentic or false based on previous data of bogus and legitimate job postings. To start, supervised learning algorithms as classification techniques can be considered to handle the challenge of recognizing scammers on job postings. It will employ two or more machine learning algorithms, selecting the one that yields the highest accuracy score in the prediction of whether a job advertising headline is genuine or not.

ONLINE FAKE JOB ADVERTISEMENT RECOGNITION AND CLASSIFICATION USING MACHINE LEARNING

3C TIC, 2022

Machine learning algorithms handle numerous forms of data in real-world intelligent systems. With the advancement in technology and rigorous social media platforms, many job seekers and recruiters are actively working online. However, due to data and privacy breaches, one can become the target of perilous activities. The agencies and fraudsters entice the job seekers by using numerous methods, and sources coming from virtual job-supplying websites. We aim to reduce the quantity of such fake and fraudulent attempts by providing predictions using Machine Learning. In our proposed approach, multiple classification models are used for better detection. This paper also presents different classifiers' performances and compares results to enhance the results through various techniques for realistic results.

Application of machine learning in the process of classification of advertised jobs

International journal of electrical engineering and computing, 2020

Institutions that provide official statistics tend to use external data sources such as administrative data sources besides regular statistical surveys. In addition to the mentioned data sources, Big Data became recognized as a new data source for the provider of official statistics. Classification of textual data is one of the elementary tasks for the provider of official statistics, regardless of data sources. In this paper, application of traditional machine learning algorithms, Multinomial Naive Bayes and Support Vector Machine, for the classification of advertised jobs according to ISCO-08, has been presented. The paper presents the methods of collecting data on advertised jobs from four websites and procedures for creating a multilingual dataset. There are different types of text preprocessing, such as converting uppercase letters into lowercase letters, stopword removal, punctuation mark removal, lemmatization, correction of commonly misspelled words, and reduction of replicated characters. We hypothesized that the application of different combinations of preprocessing methods influenced the text classification results. Two experiments had conducted to test the hypothesis. Both experiments results showed that using the Support Vector Machine algorithm on a created dataset gives better results than Multinomial Naive Bayes. Performed experiments showed that the proposed algorithms gave a good performance with an overall accuracy of up to 90% but with different accuracy for individual classes due to an imbalanced dataset.