Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data

Tecnologías de la Información y Comunicaciones

Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting

How much useful information can we extract from security forums? Many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Our goal here is to extract information from hacker forums, whose information is provided in ad hoc and unstructured ways. Here, we focus on the problem of identifying malicious IPs addresses, when these are being reported in the forums. We develop a method to automate the identification of malicious IPs with the design goal of being independent of external sources. A key novelty is that we use a matrix decomposition method to extract latent features of the behavioral information of the users, which we combine with textual information from the related posts. As key design feature, our technique can be applied to different language forums since it relies on a simple NLP solution in combination with behavioral features. In particular, our solution only needs a small number of keywords in the new language plus the user's behavior captured by specific features. We also develop a tool to automate the data collection from security forums. We collect approximately 600K posts from 3 different forums. Our method exhibits high classification accuracy, while the precision of identifying malicious IP in post is greater than 88% in all three sites. Furthermore, by applying our method, we find up to 3 times more potentially malicious IPs than compared to the reference blacklist VirusTotal. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.

InferIP: Extracting actionable information from security discussion forums

This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling. The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.

From Security to Community Detection in Social Networking Platforms

Girasol, a sky imaging and global solar irradiance dataset

The world's digital transformation has influenced not only the way we do business, but also the way we perform daily activities. Social media has a clear influence on people, especially during events such as elections where recent international events have shown that social media played an important role. In fact, there are three main aspects in this digital landscape: political movements and political candidates looking for popular support (number of followers), citizens' messages discussing social issues (trending topics on social media), and political propaganda appearing in the net in favor of or against politicians or political movements (advertisement). One of the issues with social media is the presence of automatic accounts (bots) that artificially fill accounts with fake followers, create false trending topics, and share fake news or simply flood the net with propaganda. All this artificially generated information may influence people and sometimes may even censor people's real opinions undermining their freedom of speech and affecting true democracy. In this paper, we present a set of observations based on two main objectives: collect evidence of digital campaigning during the Presidential Elections in Ecuador 2017 and describe the mechanisms used for this campaign. Finally, we show how candidates prepared online campaigns in Twitter and how normal patterns of new followers' subscriptions were altered. And, we collected information about more than 30,000 bots filtered only by political content used for propaganda purposes.

Presidential Elections in Ecuador: Bot Presence in Twitter

Malware authors have been using websites to distribute their products as a way to evade spam filters and classic anti-virus engines. Yet there has been relatively little work in modeling the behaviors and temporal properties of websites, as most research focuses on detecting whether a website distributes malware. In this paper we ask: How does web-based malware spread? We conduct an extensive study and follow a website-centric and user-centric point of view. We collect data from four online databases, including Symantec's WINE Project, for a total of more than 600K malicious URLs and over 500K users. First, we find that legitimate but compromised websites constitute 33.1% of the malicious websites in our dataset. In order to conduct this study, we develop a classifier to distinguish between compromised vs. malicious websites with an accuracy of 95.3%, which could be of interest to studies on website profiling. Second, we find that malicious URLs can be surprisingly long-lived, with 10% of malicious sites staying active for three months or more. Third, we observe that a significant number of URLs exhibit the same temporal pattern that suggests a flush-crowd behavior, inflicting most of their damage during the first few days of appearance. Finally, the distribution of the visits to malicious sites per user is skewed, with 1.4% of users visiting more than 10 malicious sites in 8 months. Our study is a first step towards modeling web-based malware propagation as a network-wide phenomenon and enabling researchers to develop realistic assumptions and models.

“Infect-me-not”: A user-centric and site-centric study of web-based malware

The world's digital transformation has influenced not only the way we do business, but also the way we perform daily activities. In fact, the past Presidential elections in the United States as well as those in Great Britain (Brexit) and in Colombia (peace agreement referendum) are proof that social media play an important part in modern politics. In fact, this digital political field is filled by political movements and political candidates looking for popular support (number of followers), regular citizens' messages discussing social issues (trending topics flooding social media), or even political propaganda in favor or against politicians or political movements (advertisement). One of the issues with social media in this era is the presence of automatic accounts (bots) that artificially fill accounts with fake followers, create false trending topics, and share fake news or simply flood the net with propaganda. All this artificial information may influence people and sometimes may even censor people's real opinions undermining their freedom of speech. In this paper, we propose a methodology to track elections and a set of tools used to collect and analyze election data. In particular, this paper discusses our experiences during the Presidential Elections in Ecuador held in 2017. In fact, we show how all candidates prepared an online campaign in social media (Twitter) and how the political campaign altered a common follower rate subscription. We discuss that the high presence of followers during the period between the first and second round of elections may be altered by automatic accounts. Finally, we use bot detection systems and gathered more than 30,000 political motivated bots. In our data analysis, we show that these bots were mainly used for propaganda purposes in favor or against a particular candidate.

Adnan Bashir

Papers

Girasol, a sky imaging and global solar irradiance dataset

Presidential Elections in Ecuador: Bot Presence in Twitter

“Infect-me-not”: A user-centric and site-centric study of web-based malware

Tracking Elections: our experience during the presidential elections in Ecuador