activity
20172021
most citedDetecting and Characterizing Lateral Phishing at Scale

43 citations · 51 across the 4 of their papers we have counts for

collaborators

7 papers

cs.OS20214 cited

BPF for storage: an exokernel-inspired approach

Yu Jian Wu, Hongyi Wang, Yuhong Zhong +4

The overhead of the kernel storage path accounts for half of the access latency for new NVMe storage devices. We explore using BPF to reduce this overhead, by injecting user-define…

cs.LG2020

Characterizing and Taming Model Instability Across Edge Devices

Eyal Cidon, Evgenya Pergament, Zain Asgar +2

The same machine learning model running on different edge devices may produce highly-divergent outputs on a nearly-identical input. Possible reasons for the divergence include diff…

cs.CR2020

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…

cs.CR2019

Cost-Aware Robust Tree Ensembles for Security Applications

Yizheng Chen, Shiqi Wang, Weifan Jiang +2

There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be…

cs.CR201943 cited

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…

cs.LG2018

Bandana: Using Non-volatile Memory for Storing Deep Learning Models

Assaf Eisenman, Maxim Naumov, Darryl Gardner +5

Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the require…