Malicious Sellers Research Articles

Detecting fake reviewers in heterogeneous networks of buyers and sellers: a collaborative training-based spammer group algorithm

It is not uncommon for malicious sellers to collude with fake reviewers (also called spammers) to write fake reviews for multiple products to either demote competitors or promote their products’ reputations, forming a gray industry chain. To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers, researchers have conducted extensive research using Frequent Item Mining-based and graph-based methods. However, these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features, and structure-attribute correlation, resulting in poorer detection performance. Therefore, we propose a collaborative training-based spammer group detection algorithm by constructing a heterogeneous induced sub-network based on the target product set to detect cross-product attack spammer groups. To jointly consider all available features, we use the collaborative training method to learn the feature representations of nodes. In addition, we use the DBSCAN clustering method to generate candidate groups, exclude innocent ones, and rank them to obtain spammer groups. The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.

An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products

In recent years, the social network has become popular and people have started trading transactions on the Internet. Many counterfeit websites have begun to appear which create websites with counterfeit products or use the digital advertiser’s services to promote their websites on social media. Malicious sellers disguise high-quality products to attract consumers since buyers cannot receive transparent information. If there is asymmetry information, a secondary market will be formed. To solve the above problems, this research explored the machine-learning-based method to classify counterfeit and legitimate websites with symmetry information. The data set is 1612 websites used in this paper and a total of 15 feature values and takes 804 counterfeit websites and 808 legitimate websites. The Random Forest and Deep Neural Network algorithms were used to classify fake websites. This study also used statistical tests, such as Chi-square and ANOVA detection, to compare the importance of features in feature selection. The experiment results show that the RF accuracy is 99.2% and the DNN accuracy is 93.2%. The RF Precision and Recall are 100% and 98.5%, respectively. The DNN Precision and Recall are less than RF. Then, the RF F1-score is 99.2% which is higher than DNN.

일본의 ｢거래 디지털 플랫폼을 이용하는 소비자의 이익 보호에 관한 법률｣에 관한 고찰

Digital platforms - where businesses supply goods or services, and consumers purchase them - have become essential to our daily lives and to the world economy. Problems such as the distribution of unsafe products, etc., and difficulty in identifying sellers in resolving disputes exist on "digital platforms" such as online marketplaces. In response to this, a new Act was prepared to protect consumer interests. In order to contribute to the optimization of mail-order transactions (Business to Customer transactions) conducted using digital platforms and the promotion of dispute resolution, digital platform providers are obligated to make efforts to implement and disclose an outline of the following measures listed in Act (and the Prime Minister draws up guidelines for specific content). The Prime Minister requests that digital platform providers remove listings, etc. of unsafe products when it is difficult to enforce relevant Individual Acts due to reasons such as that the seller cannot be specified. Establishes the right to request the disclosure of seller information as necessary when consumers make a claim for damages, etc. A public-private council consisting of national government agencies, groups consisting of digital platform providers, consumer groups, etc. will be organized to discuss matters that each entity is responsible for handling, such as dealing with malicious sellers. Establishing of a reporting system in which consumers, etc. can report the risk of damage to consumers and request that the Prime Minister (Consumer Affairs Agency) takes appropriate measures in response to this. The Act has modell characteristics in that it emphasizes autonomous response without hindering the innovation of the platform business model from an outsider's point of view. However, not all contents of the Act are modell characteristics.

Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

Online reviews have increasingly become a very important resource before making a purchasing decisions. Unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.Existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. However, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. In this paper, an effective method of identifying opinion spammers is proposed. Firstly, suspected spammers are detected by means of unsupervised learning based on reviewer’s reputation. We believe that the reviewer’s reputation has a direct relation with the quality of reviews. Generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. Therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. On basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. Experimental results on Amazon’s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved.

CReam: A Smart Contract Enabled Collusion-Resistant e-Auction

Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol. Moreover, the open and anonymous online environment may stimulate auction participants to form collusion coalitions to rig the auction and reap unfair profit. Many auction designs have been proposed to address these concerns, but they fall short of simultaneously achieving decentralization (i.e., held without a trusted third utility), strong consensus (i.e., the establishment of trust), collusion resistance, and practical implementation. We present CReam , the first decentralized collusion-resistant e-auction system that is implemented with smart contract on the blockchain. With the carefully designed smart auction contract, mutually distrustful and rational sellers and buyers are stimulated to operate properly, hence transact safely without trusted third parties. The auction mechanism in the smart contract can effectively prevent bidder collusion and realize economic robustness, i.e., truthfulness. We implement a fully functional CReam on the Ethereum network. Extensive experimental results confirm that CReam can greatly reduce the probability of collusion and achieve an approximate optimal revenue at a low cost of contract execution.

An unsupervised strategy for defending against multifarious reputation attacks

In electronic markets, malicious sellers often employ reviewers to carry out different types of attacks to improve their own reputations or destroy their opponents’ reputations. As such attacks may involve deception, collusion, and complex strategies, maintaining the robustness of reputation evaluation systems remains a challenging problem. From a platform manager’s view, no trader can be taken as a trustable benchmark for reference, therefore, accurate filtration of dishonest sellers and fraud reviewers and precise presentation of users’ reputations remains a challenging problem. Based on impression theory, this paper presents an unsupervised strategy, which first design a nearest neighbor search algorithm to select some typical lenient reviewers and strict reviewers. Then, based on these selected reviewers and the behavior expectation theory in impression theory, this paper adopts a classification algorithm that pre-classify sellers into honest and dishonest ones. Thirdly, another classification algorithm is designed to classify reviewers (i.e., buyers) into honest, dishonest, and uncertain ones according to their trading experiences with the pre-classified sellers. Finally, based on the ratings of various reviewers, this paper proposes a formula to estimate seller reputations. We further designed two general sets of experiments over simulated data and real data to evaluate our scheme, which demonstrate that our unsupervised scheme outperforms benchmark strategies in accurately estimating seller reputations. In particular, this strategy can robustly defend against various common attacks and unknown attacks.

An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews

In electronic commerce, online reviews play very important roles in customers’ purchasing decisions. Unfortunately, malicious sellers often hire buyers to fabricate fake reviews to improve their reputation. In order to detect deceptive reviews and mine the topics and sentiments from the reviews, in this paper, we propose an unsupervised topic-sentiment joint probabilistic model (UTSJ) based on Latent Dirichlet Allocation (LDA) model. This model first employs Gibbs sampling algorithm to approximate parameters of maximum likelihood function offline and obtain topic-sentiment joint probabilistic distribution vector for each review. Secondly, a Random Forest classifier and a SVM (Support Vector Machine) classifier are trained offline, respectively. Experimental results on real-life datasets show that our proposed model is better than baseline models such as n-grams, character n-grams in token, POS (part-of-speech), LDA, and JST (Joint Sentiment/Topic). Moreover, our UTSJ model outperforms or performs similarly to benchmark models in detecting deceptive reviews over balanced dataset and unbalanced dataset in different domains. Particularly, our UTSJ model is good at dealing with real-life unbalanced big data, which makes it very suitable for being applied in e-commerce environment.

Flow My FE the Vendor Said: Exploring Violent and Fraudulent Resource Exchanges on Cryptomarkets for Illicit Drugs

A growing share of illicit drug distribution takes place using cryptomarkets that use encryption and anonymization technologies. The risks of law enforcement intervention and violence are lower here than in off-line traditional drug markets, but with the technological innovations follow new opportunities for stealing and fraud. The sites themselves fall prey to theft and hacking attempts, administrators abscond with users’ funds, and malicious sellers regularly cheat buyers. In this study, we explore the types of theft and fraud that occur on cryptomarkets using multiple data sources: formalized community resources (e.g., guides, tutorials), ethnographic observations of user forums, thematic identification of forum posts using unsupervised text classification, and an expert interview. We find system-based violent predatory resource exchange similar to robberies and process-based fraudulent resource exchange similar to rip-offs. We discuss these offenses conceptually as extensions of common drug-related crimes in the digital world. This contributes to the research on how cryptomarkets work and can improve crime-prevention efforts.

TRIVIA: visualizing reputation profiles to detect malicious sellers in electronic marketplaces

Reputation systems are an essential part of electronic marketplaces that provide a valuable method to identify honest sellers and punish malicious actors. Due to the continuous improvement of the computation models applied, advanced reputation systems have become non-transparent and incomprehensible to the end-user. As a consequence, users become skeptical and lose their trust toward the reputation system. In this work, we are taking a step to increase the transparency of reputation systems by means of providing interactive visual representations of seller reputation profiles. We thereto propose TRIVIA - a visual analytics tool to evaluate seller reputation. Besides enhancing transparency, our results show that through incorporating the visual-cognitive capabilities of a human analyst and the computing power of a machine in TRIVIA, malicious sellers can be reliably identified. In this way we provide a new perspective on how the problem of robustness could be addressed.

ReputationPro

In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness of a seller without taking any transaction context information into account. With such a result as the indication of reputation, a buyer may be easily deceived by a malicious seller in a transaction where the notorious value imbalance problem is involved—in other words, a malicious seller accumulates a high-level reputation by selling cheap products and then deceives buyers by inducing them to purchase more expensive products. In this article, we first present a trust vector consisting of three values for contextual transaction trust (CTT). In the computation of CTT values, three identified important context dimensions , including Product Category, Transaction Amount, and Transaction Time, are taken into account. In the meantime, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters specified by a buyer regarding context dimensions, different sets of CTT values can be calculated. As a result, all of these trust values can outline the reputation profile of a seller that indicates the dynamic trustworthiness of a seller in different products, product categories, price ranges, time periods, and any necessary combination of them. We name this new model ReputationPro . Nevertheless, in ReputationPro , the computation of reputation profile requires new data structures for appropriately indexing the precomputation of aggregates over large-scale ratings and transaction data in three context dimensions, as well as novel algorithms for promptly answering buyers’ CTT queries. In addition, storing precomputed aggregation results consumes a large volume of space, particularly for a system with millions of sellers. Therefore, reducing storage space for aggregation results is also a great demand. To solve these challenging problems, we first propose a new index scheme CMK-tree by extending the two-dimensional K-D-B-tree that indexes spatial data to support efficient computation of CTT values. Then, we further extend the CMK-tree and propose a CMK-tree RS approach to reducing the storage space allocated to each seller. The two approaches are not only applicable to three context dimensions that are either linear or hierarchical but also take into account the characteristics of the transaction-time model—that is, transaction data is inserted in chronological order. Moreover, the proposed data structures can index each specific product traded in a time period to compute the trustworthiness of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency of computing CTT values to all three existing approaches in the literature. In particular, while answering a buyer’s CTT queries for each brand-based product category, the CMK-tree has almost linear query performance. In addition, with significantly reduced storage space, the CMK-tree RS approach can further improve the efficiency in computing CTT values. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce Web sites in terms of efficiency and storage space consumption.

The approaches to contextual transaction trust computation in e‐Commerce environments

ABSTRACTIn e‐commerce environments, the reputation of sellers is a crucial issue to potential buyers in making decisions. Most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context into account. With such a simple trust evaluation model, a buyer may be easily deceived by a malicious seller in a transaction where the notorious value imbalance problem is involved, namely, the malicious seller can accumulate a high level of trust by selling cheap products and then start to deceive buyers by inducing them to purchase expensive products. In this article, we first present a trust vector consisting of four trust values (termed as CTT values), the computation of each of which is based on both past transactions and the forthcoming transaction. In addition, in the computation of CTT values, the parameters, such as product category, price range, and time range, can be specified and adjusted by the buyer, yielding different sets of trust values that can outline the seller's reputation profile. As a result, the value imbalance problem potentially existing in a forthcoming transaction can be identified. The computation of CTT values requires the pre‐computation of aggregates over large‐scale ratings and transaction data with necessary combinations of three dimensions (i.e., product category, price, and time), so as to deliver prompt responses to a buyer's query on different CTT values. In solving this challenging problem, we propose three new data structures and algorithms that mainly focus on efficient computation of CTT values. Our experiments conducted on both eBay data sets and large‐scale synthetic data sets illustrate the advantages and disadvantages of our proposed approaches when responding to a buyer's CTT queries. Copyright © 2014 John Wiley & Sons, Ltd.

A robust e-commerce service: Light-weight secure mail-order mechanism

Mail order systems offer a convenient purchase service, in which buyers need not visit the store physically and instead choose what they want via a table of contents. Without a third party to play the roles of verifier and recorder, however, buyers face a potential problem of being cheated by a malicious seller. Thus, we aim to develop a mail order system over the Internet that can guarantee user anonymity and secrecy during the transaction process. The low computation of the mutual authentication between the parties involved contributes to the practicality of this new system, while the correctness of this process can be confirmed by the BAN logic model.