Advanced persistent threat organization identification based on software gene of malware
Weixiang Chen, Xiaohan Helu, Chengjie Jin, Man Zhang, Hui Dong Lu, Yanbin Sun, Zhihong Tian · Transactions on Emerging Telecommunications Technologies · 2020
Abstract Since the concept of IoT (Internet of Things) was proposed, it has digitized the real world and has a wide range of applications. However, with tremendous evolution in data acquisition and transfer, a new type of attack represented by advanced persistent threat (APT) has attracted wide attention. APT organization identification for malware is a method to detect APT attacks. However, most of malware is tailored to the goal, it is complex and changeable, or can be updated very quickly. The traditional analysis method is difficult to obtain the source information of APT organization from the malware in the IoT. To this end, we propose a software genes method to solve this problem. Software gene is binary fragment of specific function or information in the software body. In this paper, different from traditional data flow and instruction flow, a new gene model is proposed which combine with knowledge graph of malware behavior. We fill the processed malware information into the gene model to obtain the APT organization gene pool. Of course, the gene pool should be optimized to include the genetic characteristics of APT. In theory, there genetic characteristics can help us identify malware and APT accurately in the IoT. However, biological genetic similarity algorithms cannot be used directly. A genetic similarity algorithm for APT organization identification of malware will be designed instead. Simulations on real‐world dataset corroborate theoretical analysis and reveal the possibility of using genes for malware traceability.