Journal of Information Security

Volume 9, Issue 4 (October 2018)

ISSN Print: 2153-1234   ISSN Online: 2153-1242

Google-based Impact Factor: 1.90  Citations  h5-index & Ranking

Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation

HTML  XML Download Download as PDF (Size: 1752KB)  PP. 225-241  
DOI: 10.4236/jis.2018.94016    2,176 Downloads   4,018 Views   Citations


Distributed Denial of Service (DDoS) attacks are performed from multiple agents towards a single victim. Essentially, all attacking agents generate multiple packets towards the victim to overwhelm it with requests, thereby overloading the resources of the victim. Since it is very complex and expensive to conduct a real DDoS attack, most organizations and researchers result in using simulations to mimic an actual attack. The researchers come up with diverse algorithms and mechanisms for attack detection and prevention. Further, simulation is good practice for determining the efficacy of an intrusive detective measure against DDoS attacks. However, some mechanisms are ineffective and thus not applied in real life attacks. Nowadays, DDoS attack has become more complex and modern for most IDS to detect. Adjustable and configurable traffic generator is becoming more and more important. This paper first details the available datasets that scholars use for DDoS attack detection. The paper further depicts the a few tools that exist freely and commercially for use in the simulation programs of DDoS attacks. In addition, a traffic generator for normal and different types of DDoS attack has been developed. The aim of the paper is to simulate a cloud environment by OMNET++ simulation tool, with different DDoS attack types. Generation normal and attack traffic can be useful to evaluate developing IDS for DDoS attacks detection. Moreover, the result traffic can be useful to test an effective algorithm, techniques and procedures of DDoS attacks.

Cite this paper

Alzahrani, S. and Hong, L. (2018) Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation. Journal of Information Security, 9, 225-241. doi: 10.4236/jis.2018.94016.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.