Abstract
Data detailed on Artificial Intelligence have been presented. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “Smart cities have attracted a lot of attention from interdisciplinary research, and plenty of artificial intelligence based solutions have been proposed. However, cyber security has always been a serious problem, and it is becoming more and more severe in smart cities.” Funders for this research include Major Key Project of PCL, Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies. The news correspondents obtained a quote from the research from the School of Computer Science and Technology, “The existing attack defense methods are not suitable for detecting multi-step attacks since the detection rules are limited and the efficiency is limited by a large number of false security alarms. Hence, an advanced solution is urgently needed to improve cyber security defense capability. In this paper, we propose a novel attack detection framework called ACAM. To better represent the cyber security knowledge, the framework is based on the MDATA model, which can represent dynamic and temporalspatial knowledge better than the knowledge graph. The framework consists of the knowledge extraction module, the subgraph generation module, the alarm correlation module, and the attack detection module. These modules can remove false alarms and improve the detection capabilities of multi-step attacks. We implement the framework and conduct experiments on the cyber range platform, the experimental results validate the good performance of attack detection accuracy and efficiency.”
NETL
NSTL
Elsevier