On the Assessment of Information Quality in Spanish Wikipedia

Featured Articles (FA) are considered to be the best articles that Wikipedia has to offer and in the last years, researchers have found interesting to analyze whether and how they can be distinguished from “ordinary” articles. Likewise, identifying what issues have to be enhanced or fixed in ordinary articles in order to improve their quality is a recent key research trend. Most of the approaches developed in these research trends have been proposed for the English Wikipedia. However, few efforts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present a first breakdown of Spanish Wikipedia’s quality flaw structure. Besides, we carry out a study to automatically assess information quality in Spanish Wikipedia, where FA identification is evaluated as a binary classification task. The results obtained show that FA identification can be performed with an F1 score of 0.81, using a document model consisting of only twenty six features and AdaBoosted C4.5 decision trees as classification algorithm.XIII Workshop Bases de datos y Minería de Datos (WBDMD)Red de Universidades con Carreras en Informática (RedUNCI

Towards Information Quality Assurance in Spanish: Wikipedia

Featured Articles (FA) are considered to be the best articles that Wikipedia has to offer and in the last years, researchers have found interesting to analyze whether and how they can be distinguished from “ordinary” articles. Likewise, identifying what issues have to be enhanced or fixed in ordinary articles in order to improve their quality is a recent key research trend. Most of the approaches developed to face these information quality problems have been proposed for the English Wikipedia. However, few efforts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present a breakdown of Spanish Wikipedia’s quality flaw structure. Besides, we carry out studies with three different corpora to automatically assess information quality in Spanish Wikipedia, where FA identification is evaluated as a binary classification task. Our evaluation on a unified setting allows to compare with the English version, the performance achieved by our approach on the Spanish version. The best results obtained show that FA identification in Spanish, can be performed with an F1 score of 0.88 using a document model consisting of only twenty six features and Support Vector Machine as classification algorithm.Facultad de Informátic

The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence

REPLOT : REtrieving Profile Links on Twitter for malicious campaign discovery

Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns. © 2016 - IOS Press and the authors. All rights reserved

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software

Investigating and Validating Scam Triggers: A Case Study of a Craigslist Website

The internet and digital infrastructure play an important role in our day-to-day live, and it has also a huge impact on the organizations and how we do business transactions every day. Online business is booming in this 21st century, and there are many online platforms that enable sellers and buyers to do online transactions collectively. People can sell and purchase products that include vehicles, clothes, and shoes from anywhere and anytime. Thus, the purpose of this study is to identify and validate scam triggers using Craigslist as a case study. Craigslist is one of the websites where people can post advertising to sell and buy personal belongings online. However, with the growing number of people buying and selling, new threats and scams are created daily. Private cars are among the most significant items sold and purchased over the craigslist website. In this regard, several scammers have been drawn by the large number of vehicles being traded over craigslist. Scammers also use this forum to cheat others and exploit the vulnerable. The study identified online scam triggers including Bad key words, dealers’ posts as owners, personal email, multiple location, rogue picture and voice over IP to detect online scams that exists in craigslist. The study also found over 360 ads from craigslist based on our scam trigger. Finally, the study validated each and every one of the scam triggers and found 53.31% of our data is likelihood to be considered as a scam

Analyzing and Predicting Quality Flaws in User-generated Content: The Case of Wikipedia

Web applications that are based on user-generated content are often criticized for containing low-quality information; a popular example is the online encyclopedia Wikipedia. The major points of criticism pertain to the accuracy, neutrality, and reliability of information. The identification of low-quality information is an important task since for a huge number of people around the world it has become a habit to first visit Wikipedia in case of an information need. Existing research on quality assessment in Wikipedia either investigates only small samples of articles, or else deals with the classification of content into high-quality or low-quality. This thesis goes further, it targets the investigation of quality flaws, thus providing specific indications of the respects in which low-quality content needs improvement. The original contributions of this thesis, which relate to the fields of user-generated content analysis, data mining, and machine learning, can be summarized as follows: (1) We propose the investigation of quality flaws in Wikipedia based on user-defined cleanup tags. Cleanup tags are commonly used in the Wikipedia community to tag content that has some shortcomings. Our approach is based on the hypothesis that each cleanup tag defines a particular quality flaw. (2) We provide the first comprehensive breakdown of Wikipedia's quality flaw structure. We present a flaw organization schema, and we conduct an extensive exploratory data analysis which reveals (a) the flaws that actually exist, (b) the distribution of flaws in Wikipedia, and, (c) the extent of flawed content. (3) We present the first breakdown of Wikipedia's quality flaw evolution. We consider the entire history of the English Wikipedia from 2001 to 2012, which comprises more than 508 million page revisions, summing up to 7.9 TB. Our analysis reveals (a) how the incidence and the extent of flaws have evolved, and, (b) how the handling and the perception of flaws have changed over time. (4) We are the first who operationalize an algorithmic prediction of quality flaws in Wikipedia. We cast quality flaw prediction as a one-class classification problem, develop a tailored quality flaw model, and employ a dedicated one-class machine learning approach. A comprehensive evaluation based on human-labeled Wikipedia articles underlines the practical applicability of our approach

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks