TY - GEN
T1 - Enhancing Online Job Posting Security
T2 - 56th International Carnahan Conference on Security Technology, ICCST 2023
AU - Sharma, Kanhaiya
AU - Parashar, Deepak
AU - Sangwan, Akshay
AU - Vengali, Abhinav
AU - Agrawal, Gouransh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the digital era, online employment platforms have revolutionized the hiring landscape, offering unprecedented convenience and accessibility to both employers and job seekers. Nevertheless, this newfound ease has attracted malicious entities seeking to exploit the system through the dissemination of deceptive job listings. These fraudulent activities pose significant threats to businesses and individuals, encompassing financial losses, damage to reputation, and potential legal ramifications. This study leverages Apache Spark and Big Data to develop an advanced fraud detection system for online job postings. Our primary objective is to enhance the precision and efficiency of detecting and preventing fraudulent job advertisements. This will be achieved through the comprehensive analysis and processing of vast datasets extracted from various employment websites. By harnessing the power of these cutting-edge technologies, our investigation aims to fortify the integrity of online employment platforms, safeguarding the interests of both employers and job seekers alike.
AB - In the digital era, online employment platforms have revolutionized the hiring landscape, offering unprecedented convenience and accessibility to both employers and job seekers. Nevertheless, this newfound ease has attracted malicious entities seeking to exploit the system through the dissemination of deceptive job listings. These fraudulent activities pose significant threats to businesses and individuals, encompassing financial losses, damage to reputation, and potential legal ramifications. This study leverages Apache Spark and Big Data to develop an advanced fraud detection system for online job postings. Our primary objective is to enhance the precision and efficiency of detecting and preventing fraudulent job advertisements. This will be achieved through the comprehensive analysis and processing of vast datasets extracted from various employment websites. By harnessing the power of these cutting-edge technologies, our investigation aims to fortify the integrity of online employment platforms, safeguarding the interests of both employers and job seekers alike.
UR - https://www.scopus.com/pages/publications/85189302523
UR - https://www.scopus.com/pages/publications/85189302523#tab=citedBy
U2 - 10.1109/ICCST59048.2023.10474243
DO - 10.1109/ICCST59048.2023.10474243
M3 - Conference contribution
AN - SCOPUS:85189302523
T3 - Proceedings - International Carnahan Conference on Security Technology
BT - 2023 International Carnahan Conference on Security Technology, ICCST 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 October 2023 through 15 October 2023
ER -