Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80% accuracy). The FairJudge algorithm is already being deployed at Flipkart.
From big players to small and midsize businesses, every organization has faced the impact of cyber threats at some point. But, the new generation of automated cyber attacks will affect multiple businesses to an unimaginable extent. With the onset of the digital age, going online became a necessity for every business. Most business processes, data storage, and data exchange are now handled digitally. Data has become such a significant asset that companies have started monetizing their data.
Machine learning (ML) is taking cybersecurity by storm nowadays as well as other tech fields. In the past year, there has been ample information on the use of machine learning in both defense and attacks. While the defense was covered in most articles (I recommend reading "The Truth about Machine Learning in Cybersecurity"), Machine Learning for Cybercriminals seems to be overshadowed and not unanimous.
We exploit the prevalence of malicious review writers on crowdsourcing platforms like RapidWorkers to identify actual fraud reviews on Amazon. Complementary to previous efforts which often rely on proxies for fraud reviews, we present a long-term study of actual fraudulent behaviors in online review manipulation. We find that these malicious reviewers — though often providing seemingly legitimate opinions — do exhibit significant differences from normal reviewers in terms of ratings distribution, length of the reviews, and the burstiness of the reviews themselves. We additionally study the evolution of these reviews, and find striking temporal changes that could support future discovery of these reviewers who may be “hiding in plain sight.”