Cormac Herley (Microsoft), Stuart Schechter (Unaffiliated)

Online guessing attacks against password servers can be hard to address. Approaches that throttle or block repeated guesses on an account (e.g., three strikes type lockout rules)
can be effective against depth-first attacks, but are of little help against breadth-first attacks that spread guesses very widely. At large providers with tens or hundreds of millions
of accounts breadth-first attacks offer a way to send millions or even billions of guesses without ever triggering the depth-first defenses.
The absence of labels and non-stationarity of attack traffic make it challenging to apply machine learning techniques.

We show how to accurately estimate the odds that an observation $x$ associated with a request is malicious. Our main assumptions are that successful malicious logins are a small
fraction of the total, and that the distribution of $x$ in the legitimate traffic is stationary, or very-slowly varying.
From these we show how we can estimate the ratio of bad-to-good traffic among any set of requests; how we can then identify subsets of the request data that contain least (or even no) attack traffic; how
these least-attacked subsets allow us to estimate the distribution of values of $x$ over the legitimate data, and hence calculate the odds ratio.
A sensitivity analysis shows that even when we fail to identify a subset with little attack traffic our odds ratio estimates are very robust.

View More Papers

How Bad Can It Git? Characterizing Secret Leakage in...

Michael Meli (North Carolina State University), Matthew R. McNiece (Cisco Systems and North Carolina State University), Bradley Reaves (North Carolina State University)

Read More

Cracking the Wall of Confinement: Understanding and Analyzing Malicious...

Eihal Alowaisheq (Indiana University, King Saud University), Peng Wang (Indiana University), Sumayah Alrwais (King Saud University), Xiaojing Liao (Indiana University), XiaoFeng Wang (Indiana University), Tasneem Alowaisheq (Indiana University, King Saud University), Xianghang Mi (Indiana University), Siyuan Tang (Indiana University), Baojun Liu (Tsinghua University)

Read More

A Systematic Framework to Generate Invariants for Anomaly Detection...

Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

Read More

Quantity vs. Quality: Evaluating User Interest Profiles Using Ad...

Muhammad Ahmad Bashir (Northeastern University), Umar Farooq (LUMS Pakistan), Maryam Shahid (LUMS Pakistan), Muhammad Fareed Zaffar (LUMS Pakistan), Christo Wilson (Northeastern University)

Read More