140 citations · 247 across the 8 of their papers we have counts for
6 papers · 1 filter
Hopper: Modeling and Detecting Lateral Movement (Extended Report)
Grant Ho, Mayank Dhiman, Devdatta Akhawe +4
In successful enterprise attacks, adversaries often need to gain access to additional machines beyond their initial point of compromise, a set of internal movements known as latera…
A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts
Neil Shah, Grant Ho, Marco Schweighauser +3
We present a large-scale characterization of attacker activity across 111 real-world enterprise organizations. We develop a novel forensic technique for distinguishing between atta…
Detecting and Characterizing Lateral Phishing at Scale
Grant Ho, Asaf Cidon, Lior Gavish +5
We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral…
Stateful Detection of Black-Box Adversarial Attacks
Steven Chen, Nicholas Carlini, David Wagner
The problem of adversarial examples, evasion attacks on machine learning classifiers, has proven extremely difficult to solve. This is true even when, as is the case in many practi…
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
Primal Wijesekera, Arjun Baokar, Lynn Tsai +4
Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective beca…
Android Permissions Remystified: A Field Study on Contextual Integrity
Primal Wijesekera, Arjun Baokar, Ashkan Hosseini +3
Due to the amount of data that smartphone applications can potentially access, platforms enforce permission systems that allow users to regulate how applications access protected r…