TY - GEN
T1 - A Novel Fake Job Posting Detection
T2 - Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022
AU - Srikanth, Cheekati
AU - Rashmi, M.
AU - Ramu, S.
AU - Guddeti, Ram Mohana Reddy
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Recently, everything can be accomplished online, including education, shopping, banking, etc. This technological advancement makes it easy for fraudsters to scam people online and acquire easy money. Numerous cyber crimes worldwide exist, including identity theft and fake job postings. Nowadays, many companies post job openings online, making recruitment simple. Consequently, fraudsters also post job openings online to obtain money and personal information from job seekers. In the proposed work, we aimed to decrease the frequency of such scams by using ensemble techniques such as AdaBoost, Gradient Boost, Stacking classifier, XgBoost, Bagging, and Random Forest to identify fake job postings from genuine ones. This paper proposes various featurization techniques such as Response coding with Laplace smoothing, Average Word2vec, and term frequency-inverse document frequency weighted Word2vec. We compared the performance of ensemble techniques with machine learning (ML) algorithms on publicly available EMSCAD dataset using accuracy and F1-score. Bagging classifier outperformed all the models with an accuracy of 98.85% and an F1-score of 0.88 on imbalanced dataset. On balanced dataset, XgBoost achieved 97.89% accuracy and 0.98 F1-score. From the experimental results, it is observed that a combination of ensemble and featurization techniques using Laplace smoothed Response coding and BoW stood superior to most of the state-of-the-art works on fake job posting detection.
AB - Recently, everything can be accomplished online, including education, shopping, banking, etc. This technological advancement makes it easy for fraudsters to scam people online and acquire easy money. Numerous cyber crimes worldwide exist, including identity theft and fake job postings. Nowadays, many companies post job openings online, making recruitment simple. Consequently, fraudsters also post job openings online to obtain money and personal information from job seekers. In the proposed work, we aimed to decrease the frequency of such scams by using ensemble techniques such as AdaBoost, Gradient Boost, Stacking classifier, XgBoost, Bagging, and Random Forest to identify fake job postings from genuine ones. This paper proposes various featurization techniques such as Response coding with Laplace smoothing, Average Word2vec, and term frequency-inverse document frequency weighted Word2vec. We compared the performance of ensemble techniques with machine learning (ML) algorithms on publicly available EMSCAD dataset using accuracy and F1-score. Bagging classifier outperformed all the models with an accuracy of 98.85% and an F1-score of 0.88 on imbalanced dataset. On balanced dataset, XgBoost achieved 97.89% accuracy and 0.98 F1-score. From the experimental results, it is observed that a combination of ensemble and featurization techniques using Laplace smoothed Response coding and BoW stood superior to most of the state-of-the-art works on fake job posting detection.
UR - https://www.scopus.com/pages/publications/85171993503
UR - https://www.scopus.com/pages/publications/85171993503#tab=citedBy
U2 - 10.1007/978-981-99-3569-7_16
DO - 10.1007/978-981-99-3569-7_16
M3 - Conference contribution
AN - SCOPUS:85171993503
SN - 9789819935680
T3 - Lecture Notes in Electrical Engineering
SP - 219
EP - 234
BT - Security, Privacy and Data Analytics - Select Proceedings of the 2nd International Conference, ISPDA 2022
A2 - Rao, Udai Pratap
A2 - Alazab, Mamoun
A2 - Gohil, Bhavesh N.
A2 - Chelliah, Pethuru Raj
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 December 2022 through 15 December 2022
ER -
