Spectrum-based deep neural networks for fraud detection

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection

A Perspective on Complexity and Networks Science

Complexity and network science are nowadays used, or at least invoked, in a variety of scientific researchareas ranging from the analysis of financial systems to social structure and even to medicine. Here I explore some of the possible reasons for this success, the relationship between them and how they might be used in the future

Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM Conference on Web Science, June 30-July 3, 2019, Boston, U

The role of bot squads in the political propaganda on Twitter

Social Media are nowadays the privileged channel for information spreading and news checking. Unexpectedly for most of the users, automated accounts, also known as social bots, contribute more and more to this process of news spreading. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on a specific topic, namely the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retwitting activity of such automated accounts amplifies the presence on the platform of the hubs' messages.Comment: Under Submissio

One-Class Adversarial Nets for Fraud Detection

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.Comment: Update Fig 2, add Fig 7, and add reference

Consumer-facing technology fraud : economics, attack methods and potential solutions

The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks

Detecting Corruption in Public Procurement Through Open Data Analysis

Magistritöö eesmärgiks oli uurida, kas ainult andmeanalüüsile tuginedes on võimalik ennustada korruptsiooni võimalikkust Eesti riigihangetes ning tulenevalt eelnevast teha riigile soovitusi, kuidas parandada korruptsiooni tuvastamise võimalusi. Seatud eesmärgi saavutamiseks andis autor muuhulgas ülevaate korruptsioonist ja korruptsioonist riigihangetest ning nende vastu võitlemise strateegiast ja olulisematest viimastel aastatel toimunud arengutest maailmas. Kõrgele korruptsiooniriskile on viidanud nii üleeuroopalised uuringud kui ka meedia. Olemasolevatele andmetele tuginedes tegi autor masinõppe algoritmi, mis hindab korruptsiooni võimalikust riigihangetes Eestis.Rakenduse automatiseeritud lähenemise ja andmete analüüsi tulemusena jõudis autor tulemusteni, mis näitavad, et antud andmetele tuginedes on võimalik hinnata korruptsiooni tõenäosust Eesti riigihangetes. Eelneva põhjal saab seega öelda, et andmeanalüüsi kasutades on võimalik muuta korruptsiooni tuvastamine konkreetsemaks, lihtsamaks ja efektiivsemaks. Lähtudes teooriast ja tehtud praktilisest tööst, esitas autor enda poolsed soovitused riigile, milliste andmete kasutamisel ja analüüsil oleks võimalik korruptsiooniriski täpsemini ennustada ja seeläbi korruptsiooniriski maandada.Corruption is present in all aspects of the society and it hinders the progress of various sectors of the economy. In this context, corruption is defined as the act of dishonesty for personal gain by those in power. One of the biggest sectors it influences is public procurement. Previous research has shown that corruption is present in public procurement and it reduces the transparency of the process. Taking into account the monetary value of the public procurement sector, it is clear that this is a problem that must be addressed. Various studies have used qualitative analysis to root out the core of the issue, but as it still thrives, it essential that more accurate and acute measures are used. In order to tackle this problem, there have also been studies that try to quantify the likelihood of it, rather than only looking at qualitative research and this is where data analytics comes into play – the core of this study. This thesis aims to determine whether using open data resources and data analytics it is possible to classify corruption in the public procurement processes and therefore suggest a suitable set of data to make the detection of corruption easier and quicker. Building on existing work on corruption, it asks: what data could be analysed in classifying corruption and what methods could be used? Based on a review of the literature on corruption and theories of machine learning, data analytics was used to assess possible corruption in public procurement in Estonia. In the data analytical process the author used machine learning approaches that predict the classification of procurement as corrupt or non-corrupt. The analysis of the results demonstrated that based on available data it is possible to predict corruption in public procurement in Estonia. Furthermore, the results also indicate that some features have a bigger impact on corruption in public procurement. Taking into account the background, related work and the current results, the author suggests that data analytics is vital in the fight against corruption and using machine learning can yield in good results in predicting corruption. Further research is needed to identify other factors that could strengthen the effectiveness of these approaches

Fraud detection using machine leaning: a comparative analysis of neural networks & support vector machines

Submitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science in Finance at Strathmore UniversityFraud detection and prevention tools have been evolving over the past decade with the ever growing combination of resources, tools, and applications in big data analytics. The rapid adoption of a new breed of models is offering much deeper insights into data. There are numerous machine learning techniques in use today but irrespective of the method employed the objective remains to demonstrate comparable or better recognition performance in terms of the precision and recall metrics. This study evaluates two advanced Machine Learning approaches: Support Vector Machines and Neural Networks while taking a look at Deep Learning. The aim is to identify the approach that best identifies fraud cases and discuss challenges in their implementation. The approaches were evaluated on real-life credit card transaction data. Support Vector Machines demonstrated overall better performance across the various evaluation measures although Deep Neural Networks showed impressive results with better computational efficiency