Detecting Online Recruitment Fraud Using Machine Learning

Tabassum, Hridita; Ghosh, Gitanjali; Atika, Afra; Chakrabarty, Amitabha

doi:10.1109/icoict52021.2021.9527477

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…Our experimental findings reveal an accuracy of 91.86%, a precision value of 0.898, and a recall value of 0.695, which exceeds the reported measures in terms of accuracy and precision. The work by Tabassum, et al [29], reported accuracies within the approximate range of 94-96%. However, the research did not report any other performance measure, such as, precision or recall, to demonstrate the robustness of their proposed approach.…”

Section: Experimental Findings and Discussionmentioning

confidence: 95%

“…As outlined in the related work section, apart from our proposed work, only two other research works [28], [29] considered focusing on localised factors. However, they were conducted under Indonesian and Bangladeshi contexts, respectively.…”

Section: Experimental Findings and Discussionmentioning

confidence: 99%

“…Another research work by Tabassum, et al [29] considered the localisation of data. The authors extracted data from one of the prominent job portals in Bangladesh, i.e., BD Jobs, and used the features presented in EMSCAD dataset as a reference for feature space design.…”

Section: B Related Work In the Orf Domainmentioning

confidence: 99%

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Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

2022

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The purpose of this study is to investigate the effects of contextual features on automatic detection accuracy of online recruitment frauds in Australian job market. In addition, the study aims to unearth the significance of localisation of such approaches. The study first generates a dataset based on a local and semi-structured advertising platform in Australia. The labelled dataset is then used to train a learning model on several content-based and contextual features. The existence of advertising body in relevant government and non-government registries in Australia, along with the internet presence of the advertiser, were considered as contextual features. The extraction process of such contextual features was automated as well. The study concludes that the inclusion of contextual features improves the performance measures of the automated online recruitment fraud detection model. The practical implication of the study is two-folds. Firstly, the contextual feature space generation engine can be used with any dataset, with minimal localisation efforts. Secondly, such learning models can be used in the back end of online job recruitment portals to detect and prevent online recruitment frauds. The study not only demonstrates the positive impact of using contextual features in fraud detection using a real-life dataset, but it also demonstrates how these contextual features can be extracted automatically from the web, based on localised company registries. The generated dataset has been made available for further research in the domain.

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Section: Experimental Findings and Discussionmentioning

confidence: 95%

Section: Experimental Findings and Discussionmentioning

confidence: 99%

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Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

2022

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“…AB is an ensemble ML method that aims to integrate several weak classifiers and transform them into strong ones [ 38 ]. In this method, DT is used as a default base estimator for training the model.…”

Section: Methodsmentioning

confidence: 99%

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

Ullah

Saleem

Jamjoom

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.

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“…The study of Anita et al (2021) used logistic regression (LR), k-nearest neighbor (k-NN), Random Forest (RF), and deep learning (DL) algorithms for detecting fraudulent jobs from a large pool of real data and found that DL performed best. Another study by Tabassum et al (2021) applied seven different ML algorithms and found the highest accuracy of two classifiers at 95.17%. The study of Alghamdi & Alharby (2019) created a prediction model using an RF algorithm for preventing fraudulent jobs and achieved 97.41% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

Ullah

Jamjoom

2023

PeerJ Computer Science

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With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants’ privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants’ privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods—AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)—are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.

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Detecting Online Recruitment Fraud Using Machine Learning

Cited by 11 publications

References 13 publications

Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

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