activity
20162022
most citedTiresias: Predicting Security Events Through Deep Learning

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

collaborators
Showing cs.CRShow all

13 papers · 1 filter

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.CR20211 cited

A Large-scale Temporal Measurement of Android Malicious Apps: Persistence, Migration, and Lessons Learned

Yun Shen, Pierre-Antoine Vervier, Gianluca Stringhini

We study the temporal dynamics of potentially harmful apps (PHAs) on Android by leveraging 8.8M daily on-device detections collected among 11.7M customers of a popular mobile secur…

cs.CR2021

Marked for Disruption: Tracing the Evolution of Malware Delivery Operations Targeted for Takedown

Colin C. Ife, Yun Shen, Steven J. Murdoch +1

The malware and botnet phenomenon is among the most significant threats to cybersecurity today. Consequently, law enforcement agencies, security companies, and researchers are cons…

cs.CR20211 cited

ANDRUSPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices

Yun Shen, Gianluca Stringhini

Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sa…

cs.CR20211 cited

Understanding Worldwide Private Information Collection on Android

Yun Shen, Pierre-Antoine Vervier, Gianluca Stringhini

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. This informat…

cs.CR2019

Automatically Dismantling Online Dating Fraud

Guillermo Suarez-Tangil, Matthew Edwards, Claudia Peersman +3

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have addressed the technical or data-driven responses to this problem. In this ty…