@inproceedings{8ef88cc8f8dd44989b1e9f83780f0986,
title = "Network Security Situation Awareness Based on Spatio-temporal Correlation of Alarms",
abstract = "Traditional intrusion detection systems often deal with massive alarms based on specific filtering rules, which is complex and inexplicable. In this demo, we developed a network security situation awareness (NSSA) system based on the spatiotemporal correlation of alarms. It can monitor the security situation from the temporal dimension and discover abnormal events based on the time series of alarms. Also, it can analyze alarms from the spatial dimension on the heterogeneous alarm graph and handle alarms in batches of events. With this system, system operators can filter most irrelevant alarms quickly and efficiently. The rich visualization of alarm data could also help find hidden high-risk attack behaviors.",
keywords = "Community Discovery, Pattern Matching, Situation Awareness, Spatio-temporal Correlation, Subgraph Mining",
author = "Zehua Ren and Yang Liu and Huixiang Liu and Baoxiang Jiang and Xiangzhen Yao and Lin Li and Haiwen Yang and Ting Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 ; Conference date: 02-05-2022 Through 05-05-2022",
year = "2022",
doi = "10.1109/INFOCOMWKSHPS54753.2022.9798168",
language = "英语",
series = "INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops",
}