ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection (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.

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...

Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods

2007

Phishing attack is a kind of identity theft which tries to steal confidential data like on -line bank account information . In a phishing attack scenario, attacker deceives users by a fake email which is called scam. In this paper we employ three different learning methods to detect phishing scams. Then, we use ensemble methods on their results to improve our scam detection mechanism. Experimental results show that the proposed method can detect 94.4% of scam emails correctly, while only 0.08% of legitimate emails are classified as scams.

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.

An Intelligent Arabic Model for Recruitment Fraud Detection Using Machine Learning

Journal of Advances in Information Technology

Over the last years, with the tremendous growth of digital transformation and the constant need for companies to hire employees, huge amounts of fraudulent jobs have been posted on the internet. A cleverly planned sort of scam aimed at job searchers for a variety of unprofessional purposes is a false job posting. It can lead to a loss of money and effort. An Arabic intelligent model has been built to avoid fraudulent jobs on the Internet using machine learning, data mining, and classification techniques. The proposed model is applied to the Arabic version of the EMSCAD dataset. It is available on the Internet in the English version and it has been retrieved from the use of a real-life system and consists of several features such as company profile, company logo, interview questions, and more features depending on job offer ads, Firstly, EMSCAD is translated into the Arabic language. Then, a set of different classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve ...

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.

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.

Phishing Website Detection Using Ensemble Learning

International Journal of Emerging Trends in Engineering Research, 2023

Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%.

A Review of Ensemble Learning-Based Solutions for Phishing Website Detection

International Journal of Emerging Trends in Engineering Research , 2021

Phishing is the deception of a trustworthy person in an electronic connection in order to obtain confidential information from individuals or organisations usernames, passwords, and credit card numbers are just a few examples. Phishers imitate legitimate websites by creating websites that are visually and semantically identical. As technology advances, phishing techniques have become more sophisticated, necessitating the use of antiphishing measures to detect phishing attacks. To solve the phishing attacks problems. We got the data for the Phishing website from the Kaggle open source website, which is a Google Limited Liability Company-owned online community of data scientists and machine learning experts (LLC). We are using Ensemble learning to detecting website. We are also analize accurary. We compared the results of multiple machine learning methods for predicting phishing websites.

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.