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20162022
most citedTiresias: Predicting Security Events Through Deep Learning

143 citations · 263 across the 25 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CY20222 cited

Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit

Alexandros Efstratiou, Jeremy Blackburn, Tristan Caulfield +3

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenom…

cs.CY20227 cited

Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots

Wai Man Si, Michael Backes, Jeremy Blackburn +4

Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not b…

cs.CR20223 cited

Cerberus: Exploring Federated Prediction of Security Events

Mohammad Naseri, Yufei Han, Enrico Mariconti +3

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction mod…

cs.LG2022

Finding MNEMON: Reviving Memories of Node Embeddings

Yun Shen, Yufei Han, Zhikun Zhang +5

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 g…

cs.CY20226 cited

Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge

Catherine Jennifer, Fatemeh Tahmasbi, Jeremy Blackburn +3

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.…