Search Results for author:
Found 28 papers, 4 papers with code
Cerberus: Exploring Federated Prediction of Security Events
Modern defenses against cyberattacks increasingly rely on proactive approaches, e. g., to predict the adversary's next actions based on past events.
Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots
We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries.
Finding MNEMON: Reviving Memories of Node Embeddings
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks.
Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge
Internet memes have become a dominant method of communication; at the same time, however, they are also increasingly being used to advocate extremism and foster derogatory beliefs.
Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos
We do so vis-\`a-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contribute to virality.
ANDR USPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices
Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited by Android's permission system.
Graph Representation Learning
Cryptography and Security
Understanding Worldwide Private Information Collection on Android
Mobile phones enable the collection of a wealth of private information, from unique identifiers (e. g., email addresses), to a user's location, to their text messages.
Mobile Security
Cryptography and Security
The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information
By interpreting cryptic "drops" of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal.
Computers and Society Social and Information Networks
An Early Look at the Parler Online Social Network
Parler is as an "alternative" social network promoting itself as a service that allows to "speak freely and express yourself openly, without fear of being deplatformed for your views."
Social and Information Networks Computers and Society Physics and Society
"Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19
Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs.
Word Embeddings
Social and Information Networks
Computers and Society
"How over is it?" Understanding the Incel Community on YouTube
YouTube is by far the largest host of user-generated video content worldwide.
Computers and Society
The Evolution of the Manosphere Across the Web
In this paper, we present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements roughly focused on "men's issues," which has seen significant growth over the past years.
Computers and Society
Detecting Cyberbullying and Cyberaggression in Social Media
We also explore specific manifestations of abusive behavior, i. e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate).
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them.
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack.
On the Origins of Memes by Means of Fringe Web Communities
In this paper, we 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.
Social and Information Networks Computers and Society
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world.
A Family of Droids -- Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis
Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed.
Cryptography and Security
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms.
Social and Information Networks 68T06 K.4.2
Understanding Web Archiving Services and Their (Mis)Use on Social Media
Web archiving services play an increasingly important role in today's information ecosystem, by ensuring the continuing availability of information, or by deliberately caching content that might get deleted or removed.
Computers and Society Digital Libraries Social and Information Networks
Eight Years of Rider Measurement in the Android Malware Ecosystem: Evolution and Lessons Learned
In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1. 2 million malware samples that belong to 1. 2K families over a period of eight years (from 2010 to 2017).
Cryptography and Security
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)
Aiming to assess whether MaMaDroid's effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls.
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words
Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings.
Email Babel: Does Language Affect Criminal Activity in Compromised Webmail Accounts?
In summary, we show that language plays a significant role in the ability of cybercriminals to access sensitive information hidden in compromised webmail accounts.
Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying
Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms.
Mean Birds: Detecting Aggression and Bullying on Twitter
In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics.
Computers and Society Social and Information Networks
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models
Finally, we compare against DroidAPIMiner, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MaMaDroid significantly outperforms it.
Cryptography and Security
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world.
