| Title | A Visual Analysis Framework of Attack Paths Based on Network Traffic |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Xiaolong, Zhao, Tengteng, Zhang, Wei, Gan, Zhiqiang, Liu, Fugang |
| Conference Name | 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) |
| Date Published | jan |
| Keywords | Attack fingerprint, attack surface, Attack Visualization, Backtracking, Cyberspace, feature extraction, Fingerprint recognition, Lethality assessment, Metrics, pubcrawl, resilience, Resiliency, Scalability, security, telecommunication traffic, Time series analysis, visualization |
| Abstract | With the rapid development of the Internet, cyberspace security has become a potentially huge problem. At the same time, the disclosure of cyberspace vulnerabilities is getting faster and faster. Traditional protection methods based on known features cannot effectively defend against new network attacks. Network attack is no more a single vulnerability exploit, but an APT attack based on multiple complicated methods. Cyberspace attacks have become ``rationalized'' on the surface. Currently, there are a lot of researches about visualization of attack paths, but there is no an overall plan to reproduce the attack path. Most researches focus on the detection and characterization individual based on single behavior cyberspace attacks, which loose it's abilities to help security personnel understand the complete attack behavior of attackers. The key factors of this paper is to collect the attackers' aggressive behavior by reverse retrospective method based on the actual shooting range environment. By finding attack nodes and dividing offensive behavior into time series, we can characterize the attacker's behavior path vividly and comprehensively. |
| DOI | 10.1109/ICPECA51329.2021.9362725 |
| Citation Key | li_visual_2021 |
A Visual Analysis Framework of Attack Paths Based on Network Traffic