G
Guillermo Suarez-Tangil
Researcher at King's College London
Publications - 58
Citations - 2188
Guillermo Suarez-Tangil is an academic researcher from King's College London. The author has contributed to research in topics: Malware & Computer science. The author has an hindex of 20, co-authored 50 publications receiving 1640 citations. Previous affiliations of Guillermo Suarez-Tangil include Royal Holloway, University of London & Carlos III Health Institute.
Papers
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Journal ArticleDOI
Evolution, Detection and Analysis of Malware for Smart Devices
TL;DR: This article presents a detailed analysis on how malware has evolved over the last years for the most popular platforms and surveys, classify and discusses efforts made on detecting both malware and other suspicious software (grayware) between 2010 and 2013.
Journal ArticleDOI
Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families
TL;DR: Dendroid, a system based on text mining and information retrieval techniques for malware analysis, is introduced, suggesting that the approach is remarkably accurate and deals efficiently with large databases of malware instances.
Journal ArticleDOI
AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection
TL;DR: It is shown that Intents are semantically rich features that are able to encode the intentions of malware when compared to other well-studied features such as permissions, and it is argued that this type of feature is not the ultimate solution.
Proceedings ArticleDOI
On the Origins of Memes by Means of Fringe Web Communities
Savvas Zannettou,Tristan Caulfield,Jeremy Blackburn,Emiliano De Cristofaro,Michael Sirivianos,Gianluca Stringhini,Guillermo Suarez-Tangil +6 more
TL;DR: In this article, the authors detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, over the course of 13 months.
Proceedings ArticleDOI
DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware
Guillermo Suarez-Tangil,Santanu Kumar Dash,Mansour Ahmadi,Johannes Kinder,Giorgio Giacinto,Lorenzo Cavallaro +5 more
TL;DR: DroidSieve is proposed, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation, and exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware.