Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

eavesROP: Listening for ROP Payloads in Data Streams

Författare

Redaktör

  • Sherman S. M. Chow
  • Jan Camenisch
  • Lucas C. K. Hui
  • Siu Ming Yiu

Summary, in English

We consider the problem of detecting exploits based on

return-oriented programming. In contrast to previous works we investigate

to which extent we can detect ROP payloads by only analysing

streaming data, i.e., we do not assume any modifications to the target

machine, its kernel or its libraries. Neither do we attempt to execute any

potentially malicious code in order to determine if it is an attack. While

such a scenario has its limitations, we show that using a layered approach

with a filtering mechanism together with the Fast Fourier Transform, it

is possible to detect ROP payloads even in the presence of noise and

assuming that the target system employs ASLR. Our approach, denoted

eavesROP, thus provides a very lightweight and easily deployable mitigation

against certain ROP attacks. It also provides the added merit

of detecting the presence of a brute-force attack on ASLR since library

base addresses are not assumed to be known by eavesROP.

Publiceringsår

2014

Språk

Engelska

Sidor

413-424

Publikation/Tidskrift/Serie

Lecture Notes in Computer Science

Volym

8783

Dokumenttyp

Konferensbidrag

Förlag

Springer

Ämne

  • Electrical Engineering, Electronic Engineering, Information Engineering
  • Computer Science

Nyckelord

  • Return-Oriented Programming
  • ROP
  • Pattern Matching
  • ASLR

Conference name

ISC 2014

Conference date

2014-10-12 - 2014-10-14

Status

Published

Forskningsgrupp

  • Crypto and Security

ISBN/ISSN/Övrigt

  • ISSN: 0302-9743
  • ISBN: 978-3-319-13257-0
  • ISBN: 978-3-319-13256-3