Limiting Self-Propagating Malware Based on Connection Failure Behavior through Hyper-Compact Estimators
This work offers incremental improvements in network security for detecting and limiting malware propagation, specifically targeting worm-infected hosts.
The paper tackles the problem of defending against self-propagating malware by addressing inefficiencies in measuring connection failure rates for rate-limit algorithms, proposing two hyper-compact estimator solutions that improve memory efficiency and estimation range.
Self-propagating malware (e.g., an Internet worm) exploits security loopholes in software to infect servers and then use them to scan the Internet for more vulnerable servers. While the mechanisms of worm infection and their propagation models are well understood, defense against worms remains an open problem. One branch of defense research investigates the behavioral difference between worm-infected hosts and normal hosts to set them apart. One particular observation is that a worm-infected host, which scans the Internet with randomly selected addresses, has a much higher connection-failure rate than a normal host. Rate-limit algorithms have been proposed to control the spread of worms by traffic shaping based on connection failure rate. However, these rate-limit algorithms can work properly only if it is possible to measure failure rates of individual hosts efficiently and accurately. This paper points out a serious problem in the prior method. To address this problem, we first propose a solution based on a highly efficient double-bitmap data structure, which places only a small memory footprint on the routers, while providing good measurement of connection failure rates whose accuracy can be tuned by system parameters. Furthermore, we propose another solution based on shared register array data structure, achieving better memory efficiency and much larger estimation range than our double-bitmap solution.