To Detect Fraud Ranking For Mobile Apps Using SVM Classification

User examination is a critical part of release mobile app encourage such as Google Play Store. These marketplace permit user to suggest statement for downloaded apps inside the form of – a) star ratings and – b) judgment in the form of text reviews Users understand these review in assemble to gain insight into the app before they buy or download it. The user view about the product also influences on the purchase decision of prospective user; certainly play a key function in the production of revenue used for the developers. Fraudulent behaviors in Google Play, the bulk popular Android app marketplace, fuel search rank mistreatment and malware production. To distinguish malware, earlier work has prepared on app executable as well as authorization study. In this thesis we establish Fair Play, a storyline format that conclude and leverage traces left following by fraudsters, to observe both malware and apps subject matter to search rank fraud. Fair Play gets over 95% exactness in classifying gold average datasets of malware, fraudulent and rightful apps. Fair participate also facilitate the detection of new than 1,000 review, statement for 193 apps so as to reveal a innovative kind of “coercive” review campaign: users are strained into writing affirmative reviews, and install and review innovative apps

Search Rank Fraud Prevention in Online Systems

The survival of products in online services such as Google Play, Yelp, Facebook and Amazon, is contingent on their search rank. This, along with the social impact of such services, has also turned them into a lucrative medium for fraudulently influencing public opinion. Motivated by the need to aggressively promote products, communities that specialize in social network fraud (e.g., fake opinions and reviews, likes, followers, app installs) have emerged, to create a black market for fraudulent search optimization. Fraudulent product developers exploit these communities to hire teams of workers willing and able to commit fraud collectively, emulating realistic, spontaneous activities from unrelated people. We call this behavior “search rank fraud”. In this dissertation, we argue that fraud needs to be proactively discouraged and prevented, instead of only reactively detected and filtered. We introduce two novel approaches to discourage search rank fraud in online systems. First, we detect fraud in real-time, when it is posted, and impose resource consuming penalties on the devices that post activities. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. In a second approach, we introduce the problem of fraud de-anonymization: reveal the crowdsourcing site accounts of the people who post large amounts of fraud, thus their bank accounts, and provide compelling evidence of fraud to the users of products that they promote. We investigate the ability of our solutions to ensure that fraud does not pay off

Stateless Puzzles for Real Time Online Fraud Preemption

The profitability of fraud in online systems such as app markets and social networks marks the failure of existing defense mechanisms. In this paper, we propose FraudSys, a real-time fraud preemption approach that imposes Bitcoin-inspired computational puzzles on the devices that post online system activities, such as reviews and likes. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles, that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph-based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. FraudSys does not alter the experience of users in online systems, but delays fraudulent actions and consumes significant computational resources of the fraudsters. Using real datasets from Google Play and Facebook, we demonstrate the feasibility of FraudSys by showing that the devices of honest users are minimally impacted, while fraudster controlled devices receive daily computational penalties of up to 3,079 hours. In addition, we show that with FraudSys, fraud does not pay off, as a user equipped with mining hardware (e.g., AntMiner S7) will earn less than half through fraud than from honest Bitcoin mining

Predicting Fraud Apps Using Hybrid Learning Approach: A Survey

Each individual in the planet are mobile phone users in fact smart-phone users with android applications. So, due to this attractiveness and well-known concept there will be a hasty growth in mobile technology. And in addition in information mining, mining the required information from a fastidious application is exceptionally troublesome. Consolidating these two ideas of ranking frauds in android market and taking out required information is gone exceptionally tough.The mobile phone Apps has developed at massive speed in some years; as for march 2017, there are nearby 2.8 million Apps at google play and 2.2 Apps at Google Apps store. In addition, there are over 400,000 self-governing app developers all fighting for the attention of the same potential clients. The Google App Store saw 128,000 new business apps alone in 2014 and the mobile gaming category alone has contest to the tune of almost 300,000 apps. Here the major need to make fraud search in Apps is by searching the high ranked applications up to 30-40 which may be ranked high in some time or the applications which are in those high ranked lists should be confirmed but this is not applied for thousands of applications added per day. So, go for wide examination by applying some procedure to every application to judge its ranking. Discovery of ranking fraud for mobile phone applications, require a flawless, fraud less and result that show correct application accordingly provide ranking; where really make it occur by searching fraud of applications. They create fraud of App by ranked high the App by methods using such human water armies and bot farms; where they create fraud by downloading application through different devices and provide fake ratings and reviews. So, extract critical data connecting particular application such as review which was called comments and lots of other information, to mine and place algorithm to identify fakeness in application rank

Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.Comment: The 29Th ACM Conference on Hypertext and Social Media, July 201

Hiding in Plain Sight: A Longitudinal Study of Combosquatting Abuse

Domain squatting is a common adversarial practice where attackers register domain names that are purposefully similar to popular domains. In this work, we study a specific type of domain squatting called "combosquatting," in which attackers register domains that combine a popular trademark with one or more phrases (e.g., betterfacebook[.]com, youtube-live[.]com). We perform the first large-scale, empirical study of combosquatting by analyzing more than 468 billion DNS records---collected from passive and active DNS data sources over almost six years. We find that almost 60% of abusive combosquatting domains live for more than 1,000 days, and even worse, we observe increased activity associated with combosquatting year over year. Moreover, we show that combosquatting is used to perform a spectrum of different types of abuse including phishing, social engineering, affiliate abuse, trademark abuse, and even advanced persistent threats. Our results suggest that combosquatting is a real problem that requires increased scrutiny by the security community.Comment: ACM CCS 1