Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Electrical and Computer Engineering

First Advisor

Yong Guan

Second Advisor

Ying Cai

Third Advisor

Daji Qiao

Abstract

Mal-ware such as viruses and worms are increasingly proliferating through out all networks. Existing schemes that address these issues either assume that the mal-ware is available in its plain-text format which can be detected directly with its signature or that its exploit-code execution is directly recognizable. Hence much of the development in this area has been focussed on generating more efficient signatures or in coming up with improved anomaly-based detection and pattern matching rules. However with "secure data" being the watch-word and several efficient encryption schemes being developed to obfuscate data and protect its privacy, encrypted mal-ware is very much a clear and present threat. While securing resources from encrypted threats is the need of the hour, equally critical is the privacy of content that needs to be protected. In this paper we discuss encrypted mal-ware detection and propose an efficient IP-packet level scheme for encrypted mal-ware detection that does not compromise the privacy of the data but at the same time helps detect the presence of hidden mal-ware in it. We also propose a new grammar for a generalized representation of all kinds of malicious-signatures. This signature grammar is inclusive of even polymorphic and metamorphic signatures which do not have a straight-forward one-to-one mapping between the signature string and worm-recognition. In a typical system model consisting of several co-operating hosts which are un-intentional senders of mal-ware traffic, where a centralized network monitor functions as the mal-ware detection entity, we show that for a very small memory and processing overhead and almost negligible time-requirements, we achieve a very high detection rate for even the most advanced multi-keyword polymorphic signatures.

DOI

https://doi.org/10.31274/rtd-180813-16658

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Bhuvaneswari Ramkumar

Language

en

Proquest ID

AAI1454600

OCLC Number

268956879

ISBN

9780549684749

File Format

application/pdf

File Size

58 pages

Share

COinS