congratulations to Calvin Ardi for his new PhD

I would like to congratulate Dr. Calvin Ardi for defending his PhD in April 2020 and completing his doctoral dissertation “Improving Network Security through Collaborative Sharing” in June 2020.

From the abstract:

Calvin Ardi and John Heidemann (inset), after Calvin filed his PhD dissertation.

As our world continues to become more interconnected through the
Internet, cybersecurity incidents are correspondingly increasing in
number, severity, and complexity. The consequences of these attacks
include data loss, financial damages, and are steadily moving from the
digital to the physical world, impacting everything from public
infrastructure to our own homes. The existing mechanisms in
responding to cybersecurity incidents have three problems: they
promote a security monoculture, are too centralized, and are too slow.

In this thesis, we show that improving one’s network security strongly
benefits from a combination of personalized, local detection, coupled
with the controlled exchange of previously-private network information
with collaborators. We address the problem of a security monoculture
with personalized detection, introducing diversity by tailoring to the
individual’s browsing behavior, for example. We approach the problem
of too much centralization by localizing detection, emphasizing
detection techniques that can be used on the client device or local
network without reliance on external services. We counter slow
mechanisms by coupling controlled sharing of information with
collaborators to reactive techniques, enabling a more efficient
response to security events.

We prove that we can improve network security by demonstrating our
thesis with four studies and their respective research contributions
in malicious activity detection and cybersecurity data sharing. In
our first study, we develop Content Reuse Detection, an approach to
locally discover and detect duplication in large corpora and apply our
approach to improve network security by detecting “bad
neighborhoods” of suspicious activity on the web. Our second study
is AuntieTuna, an anti-phishing browser tool that implements personalized,
local detection of phish with user-personalization and improves
network security by reducing successful web phishing attacks. In our
third study, we develop Retro-Future, a framework for controlled information
exchange that enables organizations to control the risk-benefit
trade-off when sharing their previously-private data. Organizations
use Retro-Future to share data within and across collaborating organizations,
and improve their network security by using the shared data to
increase detection’s effectiveness in finding malicious activity.
Finally, we present AuntieTuna2.0 in our fourth study, extending the proactive
detection of phishing sites in AuntieTuna with data sharing between friends.
Users exchange previously-private information with collaborators to
collectively build a defense, improving their network security and
group’s collective immunity against phishing attacks.

Calvin defended his PhD when USC was on work-from-home due to COVID-19; he is the second ANT student with a fully on-line PhD defense.

Papers Publications

new paper “Precise Detection of Content Reuse in the Web” to appear in ACM SIGCOMM Computer Communication Review

We have published a new paper “Precise Detection of Content Reuse in the Web” by Calvin Ardi and John Heidemann, in the ACM SIGCOMM Computer Communication Review (Volume 49 Issue 2, April 2019) newsletter.

From the abstract:

With vast amount of content online, it is not surprising that unscrupulous entities “borrow” from the web to provide content for advertisements, link farms, and spam. Our insight is that cryptographic hashing and fingerprinting can efficiently identify content reuse for web-size corpora. We develop two related algorithms, one to automatically discover previously unknown duplicate content in the web, and the second to precisely detect copies of discovered or manually identified content. We show that bad neighborhoods, clusters of pages where copied content is frequent, help identify copying in the web. We verify our algorithm and its choices with controlled experiments over three web datasets: Common Crawl (2009/10), GeoCities (1990s–2000s), and a phishing corpus (2014). We show that our use of cryptographic hashing is much more precise than alternatives such as locality-sensitive hashing, avoiding the thousands of false-positives that would otherwise occur. We apply our approach in three systems: discovering and detecting duplicated content in the web, searching explicitly for copies of Wikipedia in the web, and detecting phishing sites in a web browser. We show that general copying in the web is often benign (for example, templates), but 6–11% are commercial or possibly commercial. Most copies of Wikipedia (86%) are commercialized (link farming or advertisements). For phishing, we focus on PayPal, detecting 59% of PayPal-phish even without taking on intentional cloaking.

Papers Publications

new workshop paper “AuntieTuna: Personalized Content-based Phishing Detection” in USEC 2016

The paper “AuntieTuna: Personalized Content-based Phishing Detection” will appear at the NDSS Usable Security Workshop on February 21, 2016 in San Diego, CA, USA (available at

From the abstract:

Implementation diagram of the AuntieTuna anti-phishing plugin.Phishing sites masquerade as copies of legitimate sites (“targets”) to fool people into sharing sensitive information that can then be used for fraud. Current phishing defenses can be ineffective, with training ignored, blacklists of discovered, bad sites too slow to pick up new threats, and whitelists of known-good sites too limiting. We have developed a new technique that automatically builds personalized lists of target sites (candidates that may be copied by phish) and then tests sites as a user browses them. Our approach uses cryptographic hashing of each page’s rendered Document Object Model (DOM), providing a zero false positive rate and identifying more than half of detectable phish in a controlled study. Since each user develops a customized list of target sites, our approach presents a diverse defense against phishers. We have prototyped our approach as a Chrome browser plugin called AuntieTuna, emphasizing usability through automated and simple manual addition of target sites and clean reports of potential phish that include context about the targeted site. AuntieTuna does not slow web browsing time and presents alerts on phishing pages before users can divulge information. Our plugin is open-source and has been in use by a few users for months.

The work in this paper is by Calvin Ardi (USC/ISI) and John Heidemann (USC/ISI).

Publications Technical Report

new technical report “Poster: Lightweight Content-based Phishing Detection”

We released a new technical report “Poster: Lightweight Content-based Phishing Detection”, ISI-TR-698, available at

The poster abstract and poster (included as part of the technical report) appeared at the poster session at the 36th IEEE Symposium on Security and Privacy in May 2015 in San Jose, CA, USA.

We have released an alpha version of our extension and source code here:
We would greatly appreciate any help and feedback in testing our plugin!

From the abstract:

Our browser extension hashes the content of a visited page and compares the hashes with a set of known good hashes. If the number of matches exceeds a threshold, the website is suspected as phish and an alert is displayed to the user.

Increasing use of Internet banking and shopping by a broad spectrum of users results in greater potential profits from phishing attacks via websites that masquerade as legitimate sites to trick users into sharing passwords or financial information. Most browsers today detect potential phishing with URL blacklists; while effective at stopping previously known threats, blacklists must react to new threats as they are discovered, leaving users vulnerable for a period of time. Alternatively, whitelists can be used to identify “known-good” websites so that off-list sites (to include possible phish) can never be accessed, but are too limited for many users. Our goal is proactive detection of phishing websites with neither the delay of blacklist identification nor the strict constraints of whitelists. Our approach is to list known phishing targets, index the content at their correct sites, and then look for this content to appear at incorrect sites. Our insight is that cryptographic hashing of page contents allows for efficient bulk identification of content reuse at phishing sites. Our contribution is a system to detect phish by comparing hashes of visited websites to the hashes of the original, known good, legitimate website. We implement our approach as a browser extension in Google Chrome and show that our algorithms detect a majority of phish, even with minimal countermeasures to page obfuscation. A small number of alpha users have been using the extension without issues for several weeks, and we will be releasing our extension and source code upon publication.