Security Analysis of Kolmogorov Complexity based Online Spam Filtering under Adversarial Impact
As a variety of machine learning algorithms have been applied to the field of network security, they become a very attractive target for attackers who seek to evade and disrupt them. This situation happens especially in spam filtering. Since spam evolves continuously in adversarial environment, it calls for fast, incremental, robust and secure learning algorithms. Currently security analysts for machine learning based system under adversarial impact becomes a very hot and important topic in researchers from the fields of information security and machine learning. More considering of the security of learning when people start to design machine learning based system in adversarial environment, more robustness and security will be achieved. Kolmogorov complexity is relatively new effective method proposed to measure individual randomness. It is used in spam filtering recently and gets very good performance. In this paper, we make a primarily security analysis of a classic model of kolmogorov complexity based spam filtering. We quantitatively analyze its vulnerability against malicious attacks by adversaries. Also the implications for security in complexity based filtering systems are discussed.
Spam filtering Adversarial learning Security learning Kolmogorov complexity
Wei Deng Zhiguang Qin
School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu, Sichuan 611731, China
国际会议
成都
英文
311-314
2010-12-17(万方平台首次上网日期,不代表论文的发表时间)