SEINAA Stealthy and Effective Internal Attack in Hadoop Systems
Document Type
Conference Proceeding
Publication Date
3-10-2017
Abstract
Big data processing frameworks such as Hadoop [1] are now widely adopted, however the security issues in large scale systems have not been well studied yet. Unlike prior work on data privacy and protection, this paper investigates a potential attack from a compromised internal node against the overall system performance. We develop an effective attack launched from the compromised node that can significantly degrade the data processing performance of the cluster without being detected and blacklisted for job execution, also present a mitigation scheme that protects a Hadoop system from such attack. The results of experiments show that this attack greatly slows down the job executions in the native Hadoop system even with some basic defense mechanisms, however, our mitigation schem can keep the whole cluster running efficiently under such attack.
DOI
10.1109/ICCNC.2017.7876183
Montclair State University Digital Commons Citation
Wang, Jiayin; Wang, Teng; Yang, Zhengyu; Mao, Ying; Mi, Ningfang; and Sheng, Bo, "SEINAA Stealthy and Effective Internal Attack in Hadoop Systems" (2017). Department of Computer Science Faculty Scholarship and Creative Works. 534.
https://digitalcommons.montclair.edu/compusci-facpubs/534