Matrix Factorization Techniques for Recommender Systems

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

A systematic literature review of blockchain-based applications: Current status, classification and open issues

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.

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Securify: Practical Security Analysis of Smart Contracts

An overview on smart contracts : Challenges, advances and platforms

Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact

Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating "trustworthy" frauds that still make users lose money, but at least are guaranteed to execute "correctly". We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints.

Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

More and more financial transactions through different E-commerce platforms have appeared now-days within the big data era bringing plenty of opportunities but also challenges and risks of stealing information for potential frauds that need to be faced. This is due to the massive use of tools such as credit cards for electronic payments which are targeted by attackers to steal sensitive information and perform fraudulent operations. Although intelligent fraud detection systems have been developed to face the problem, they still suffer from some well-known problems due to the imbalance of the used data. Therefore this paper proposes a novel data intelligence technique based on a Prudential Multiple Consensus model which combines the effectiveness of several state-of-the-art classification algorithms by adopting a twofold criterion, probabilistic and majority based. The goal is to maximize the effectiveness of the model in detecting fraudulent transactions regardless the presence of any data imbalance. Our model has been validated with a set of experiments on a large real-world dataset characterized by a high degree of data imbalance and results show how the proposed model outperforms several state-of-the-art solutions, both in terms of ensemble models and classification approaches.

Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model

E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

https://www.mdpi.com/1999-5903/11/1/5/pdf

Roberto Saia

Papers

Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact

Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

Semantics-aware content-based recommender systems: Design and architecture guidelines