Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset

Vidros, Sokratis; Kolias, Constantinos; Kambourakis, Georgios; Akoglu, Leman

doi:10.3390/fi9010006

Cited by 66 publications

(38 citation statements)

References 28 publications

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“…Clustering defines similar classes of items based on the similarity among objects or Figure 1. The conventional KDD model [2].…”

Section: Knowledge Discovery From Data (Kdd)mentioning

confidence: 99%

“…Recently, the cloud was integrated to the procedure of recruiting new members, where the managed cloud services or solutions are used by human resource managers. Nevertheless, there are many violating risk threats increased by scams and frauds along with the wide interest and adopting such embedded software [2].…”

Section: Introductionmentioning

confidence: 99%

“…The researchers recommended the research community to find a reliable detection model of ORF. Thus, we need to obtain a new reliable model to enhance the performance of classification based on pre-processing and feature selection phase [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Model for Online Recruitment Fraud Detection

Alghamdi¹,

Alharby²

2019

JIS

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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.

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“…Clustering defines similar classes of items based on the similarity among objects or Figure 1. The conventional KDD model [2].…”

Section: Knowledge Discovery From Data (Kdd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intelligent Model for Online Recruitment Fraud Detection

Alghamdi¹,

Alharby²

2019

JIS

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“…The concept of news hoax is often associated with, fake news, lies, disinformation, misinformation, deception, misleading, rumors, fraud, some related work can be found at, for disinformation [16] [17], for misinformation [12], for fake news [18] [19],for deception [20] ,for lies [5], for rumors [21], for fraud [22] [23], for misleading [24], etc. Problems related to this topic have been seen concerning classification, detection, filtering.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Hoax News Detection and Analyzer used Rule-based Methods

Yuliani¹,

Abdollah²,

Sahib³

et al. 2019

IJACSA

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Currently, the era where social media can present various facilities can answer the needs of the community for information and utilization for socioeconomic interests. But the other impact of the presence of social media opens an ample space for the existence of information or hoax news about an event that is troubling the public. The hoax also provides cynical provocation, which is inciting hatred, anger, incitement to many people, directly influencing behavior so that it responds as desired by the hoax makers. Fake news is playing an increasingly dominant role in spreading Misinformation by influencing people's Perceptions or knowledge to distort their awareness and decision-making. A framework is develope dataset collection of hoax gathered using web crawlers from several websites, using classification techniques. This hoax news will be categorized into several detection parameters including, page URL, title hoax news, publish date, author, and content. Matching each word hoax using the similarity algorithm to produce the accuracy of the hoax news uses the rule-based detection method. Experiments were carried out on eleven thousand-hoax news used as training datasets and testing data sets; this data set for validation using similarity algorithms, to produce the highest accuracy of hoax text similarity. In this study, each hoax news will label into four categories, namely, Fact, Hoax, Information, Unknown. Contributions propose Automatic detection of hoax news, Automatic Multilanguage Detection, and a collection of datasets that we gather ourselves and validation that results in four categories of hoax news that have measured in terms of text similarity using similarity techniques. Further research can be continued by adding objects hate speech, black campaign, blockchain technique to ward off hoaxes, or can produce algorithms that produce better text accuracy.

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“…Online fraud is a problem that takes many different forms as expressed in [4]. In our work, we focus on the specific area of advertising fraud which has been continuously expanding over the past years.…”

Section: Related Workmentioning

confidence: 99%

Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems

Mamais

Theodorakopoulos

2017

Future Internet

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Service commissions, which are claimed by Ad-Networks and Publishers, are susceptible to forgery as non-human operators are able to artificially create fictitious traffic on digital platforms for the purpose of committing financial fraud. This places a significant strain on Advertisers who have no effective means of differentiating fabricated Ad-Reports from those which correspond to real consumer activity. To address this problem, we contribute an advert reporting system which utilizes opportunistic networking and a blockchain-inspired construction in order to identify authentic Ad-Reports by determining whether they were composed by honest or dishonest users. What constitutes a user's honesty for our system is the manner in which they access adverts on their mobile device. Dishonest users submit multiple reports over a short period of time while honest users behave as consumers who view adverts at a balanced pace while engaging in typical social activities such as purchasing goods online, moving through space and interacting with other users. We argue that it is hard for dishonest users to fake honest behaviour and we exploit the behavioural patterns of users in order to classify Ad-Reports as real or fabricated. By determining the honesty of the user who submitted a particular report, our system offers a more secure reward-claiming model which protects against fraud while still preserving the user's anonymity.

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Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset

Cited by 66 publications

References 28 publications

An Intelligent Model for Online Recruitment Fraud Detection

An Intelligent Model for Online Recruitment Fraud Detection

A Framework for Hoax News Detection and Analyzer used Rule-based Methods

Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems

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