Attack Models for Big Data Platform Hadoop
Ningwei Li, Hang Gao, Liang Liu, Fan Zhang, Wenxuan Wang · 2019
Hadoop is a very popular big data processing framework, however, due to its distributed and large-scale characteristics, its security problems have not been solved very well. Existing research does not systematically analyze attacks in big data platforms. This paper proposes four innovative hadoop attack models. Through adjusting heartbeat time, tampering intermediate data, blocking network, attackers prolong the execution time of jobs, and damage the correctness of job result. We implemented these attacks in hadoop and evaluate the effects of them through experiments. The experimental results show that our attacks are effective and harmful.