Multi-Stage Attack Detection Using Contextual Information.pdf (1.04 MB)
0/0

Multi-stage attack detection using contextual information

Download (1.04 MB)
conference contribution
posted on 31.07.2018 by Kostas Kyriakopoulos, Francisco J. Aparicio-Navarro, Ibrahim Ghafir, Sangarapillai Lambotharan, Jonathon Chambers
The appearance of new forms of cyber-threats, such as Multi-Stage Attacks (MSAs), creates new challenges to which Intrusion Detection Systems (IDSs) need to adapt. An MSA is launched in multiple sequential stages, which may not be malicious when implemented individually, making the detection of MSAs extremely challenging for most current IDSs. In this paper, we present a novel IDS that exploits contextual information in the form of Pattern-of-Life (PoL), and information related to expert judgment on the network behaviour. This IDS focuses on detecting an MSA, in real-time, without previous training process. The main goal of the MSA is to create a Point of Entry (PoE) to a target machine, which could be used as part of an APT like attack. Our results verify that the use of contextual information improves the efficiency of our IDS by enhancing the detection rate of MSAs in real-time by 58%.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/2 and the MOD University Defence Research Collaboration in Signal Processing, and by the British Council UK-Gulf Institutional Link Grant and the EPSRC Grant numbers EP/R006385/1 and EP/R006377/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE MILCOM

Citation

KYRIAKOPOULOS, K.G. ...et al., 2018. Multi-stage attack detection using contextual information. Presented at IEEE Military Communications Conference (MILCOM 2018), Los Angeles, October 29 - 31st, pp. 920-925.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Acceptance date

19/07/2018

Publication date

2018

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781538671856

ISSN

2155-7586

Language

en

Location

Los Angeles

Exports