An approach for identifying JavaScript-loaded advertisements through static program analysis

Authors:
Caitlin R. Orr;Arun Chauhan;Minaxi Gupta;Christopher J. Frisz;Christopher W. Dunn
Affiliations:
Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA
Venue:
Proceedings of the 2012 ACM workshop on Privacy in the electronic society
Year:
2012

Citing 12
Cited 0

JavaScript instrumentation for browser security

Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Introduction to Information Retrieval

Introduction to Information Retrieval
Characterizing insecure javascript practices on the web

Proceedings of the 18th international conference on World wide web
Detection and analysis of drive-by-download attacks and malicious JavaScript code

Proceedings of the 19th international conference on World wide web
An analysis of the dynamic behavior of JavaScript programs

PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
An empirical study of privacy-violating information flows in JavaScript web applications

Proceedings of the 17th ACM conference on Computer and communications security
Cujo: efficient detection and prevention of drive-by-download attacks

Proceedings of the 26th Annual Computer Security Applications Conference
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Prophiler: a fast filter for the large-scale detection of malicious web pages

Proceedings of the 20th international conference on World wide web
ZOZZLE: fast and precise in-browser JavaScript malware detection

SEC'11 Proceedings of the 20th USENIX conference on Security
The eval that men do: A large-scale study of the use of eval in javascript applications

Proceedings of the 25th European conference on Object-oriented programming
AdSentry: comprehensive and flexible confinement of JavaScript-based advertisements

Proceedings of the 27th Annual Computer Security Applications Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Motivated by reasons related to privacy, obtrusiveness, and security, there is great interest in the prospect of blocking advertisements. Current approaches to this goal involve keeping sets of URL-based regular expressions, which are matched against every URL fetched on a web page. While generally effective, this approach is not scalable and requires constant manual maintenance of the filtering lists. To counter these shortcomings, we present a fundamentally different approach with which we demonstrate that static program analysis on JavaScript source code can be used to identify JavaScript that loads and displays ads. Our use of static analysis lets us flag and block ad-related scripts before runtime, offering security in addition to blocking ads. Preliminary results from a classifier trained on the features we develop achieve 98% accuracy in identifying ad-related scripts.