NDLI: Detecting Malicious Javascript in PDF through Document Instrumentation

Content Provider	IEEE Xplore Digital Library
Author	Daiping Liu Haining Wang Stavrou, A.
Copyright Year	2014
Abstract	An emerging threat vector, embedded malware inside popular document formats, has become rampant since 2008. Owed to its wide-spread use and Javascript support, PDF has been the primary vehicle for delivering embedded exploits. Unfortunately, existing defenses are limited in effectiveness, vulnerable to evasion, or computationally expensive to be employed as an on-line protection system. In this paper, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF. Our approach statically extracts a set of static features and inserts context monitoring code into a document. When an instrumented document is opened, the context monitoring code inside will cooperate with our runtime monitor to detect potential infection attempts in the context of Javascript execution. Thus, our detector can identify malicious documents by using both static and runtime features. To validate the effectiveness of our approach in a real world setting, we first conduct a security analysis, showing that our system is able to remain effective in detection and be robust against evasion attempts even in the presence of sophisticated adversaries. We implement a prototype of the proposed system, and perform extensive experiments using 18623 benign PDF samples and 7370 malicious samples. Our evaluation results demonstrate that our approach can accurately detect and confine malicious Javascript in PDF with minor performance overhead.
Starting Page	100
Ending Page	111
File Size	695733
Page Count	12
File Format	PDF
ISBN	9781479922338
DOI	10.1109/DSN.2014.92
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Portable document format Monitoring Instruments Runtime Context Feature extraction Malware document instrumentation Malcode bearing PDF malicious Javascript malware detection and confinement
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Detecting malicious javascript in pdf through document instrumentation.

PDF Scrutinizer: Detecting JavaScript-based attacks in PDF documents

Static detection of malicious javascript-bearing pdf documents abstract.

Static detection of malicious JavaScript-bearing PDF documents

Static detection of malicious JavaScript-bearing PDF documents (2011)

ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files

Multi-view Malicious Document Detection

A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis

De-obfuscation and Detection of Malicious PDF Files with High Accuracy

Detecting Malicious Javascript in PDF through Document Instrumentation

Similar Documents

Detecting malicious javascript in pdf through document instrumentation.

PDF Scrutinizer: Detecting JavaScript-based attacks in PDF documents

Static detection of malicious javascript-bearing pdf documents abstract.

Static detection of malicious JavaScript-bearing PDF documents

Static detection of malicious JavaScript-bearing PDF documents (2011)

ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files

Multi-view Malicious Document Detection

A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis

De-obfuscation and Detection of Malicious PDF Files with High Accuracy

Detecting Malicious Javascript in PDF through Document Instrumentation