Scanner-Hunter: An Effective ICS Scanning Group Identification System

As the precursor of cyber-attacks, the campaigns of scanning groups are able to reflect the attack target and attack trend to a great extent, which provide highly valuable threat intelligence for cyber defenders to understand the current cyber security situation. However, how to identify scanning groups in the context of limited information, especially in the absence of relevant threat intelligence, remains a challenging problem. In this paper, we utilize the honeynet as the unique data source to propose a scanning group identification system, Scanner-Hunter, which focuses on identifying scanning groups targeting ICS devices. To better characterize scanning patterns, a novel traffic representation scheme for scanning traffic is proposed, which is composed of a set of feature vectors to describe all the ICS request packets. On this basis, we propose a novel self-expanding multi-class classification (SEMCC) model and the IP prefix judgment, which are deliberately integrated to cope with sophisticated scanning groups. Take the Modbus protocol as an example, we implement a prototype of Scanner-Hunter, and use six years of real-world honeynet datasets to evaluate its performance. The experimental results illustrate its effectiveness and superior performance compared with some popular machine learning methods and existing SOTA scanning group identification methods. In addition, Scanner-Hunter is further leveraged to investigate the group distribution and maliciousness of 506 unknown scanners, and some suspicious attack groups with APT characteristics are analyzed. Furthermore, accurate scanning group information will contribute to revealing potential attack organizations and supporting decision making to prevent or interrupt cyber-attacks in time.