TY - GEN
T1 - TSKE
T2 - 7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2023
AU - Zhao, Angxiao
AU - Wang, Haiyan
AU - Zhang, Junjian
AU - Liu, Yunhui
AU - Ma, Changchang
AU - Gu, Zhaoquan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Knowledge representation models have been extensively studied and adopted in many areas such as search, recommendation, etc. However, due to the highly spatio-temporal relevant characteristics of cyberspace security and the dynamic variability of the domain knowledge, the existing models and knowledge embedding methods cannot be adopted in this field directly. In this paper, we propose a two-stream knowledge embedding (TSKE) method for cyberspace security to jointly embed multi-dimensional characteristics. Specifically, we design a static stream neural network and a spatio-temporal stream neural network to extract the static knowledge and the spatio-temporal features of cyberspace security facts, which converts this domain knowledge into vector space. Considering the attack link prediction task in the field of cyberspace security, we conduct extensive experiments and TSKE outperforms other static and dynamic embedding methods.
AB - Knowledge representation models have been extensively studied and adopted in many areas such as search, recommendation, etc. However, due to the highly spatio-temporal relevant characteristics of cyberspace security and the dynamic variability of the domain knowledge, the existing models and knowledge embedding methods cannot be adopted in this field directly. In this paper, we propose a two-stream knowledge embedding (TSKE) method for cyberspace security to jointly embed multi-dimensional characteristics. Specifically, we design a static stream neural network and a spatio-temporal stream neural network to extract the static knowledge and the spatio-temporal features of cyberspace security facts, which converts this domain knowledge into vector space. Considering the attack link prediction task in the field of cyberspace security, we conduct extensive experiments and TSKE outperforms other static and dynamic embedding methods.
KW - Attack Link Prediction
KW - Cyberspace Security
KW - Knowledge Representation
KW - Spatio-temporal Characteristics
UR - https://www.scopus.com/pages/publications/85192739561
U2 - 10.1007/978-981-97-2390-4_10
DO - 10.1007/978-981-97-2390-4_10
M3 - 会议稿件
AN - SCOPUS:85192739561
SN - 9789819723898
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 132
EP - 146
BT - Web and Big Data - 7th International Joint Conference, APWeb-WAIM 2023, Proceedings
A2 - Song, Xiangyu
A2 - Feng, Ruyi
A2 - Chen, Yunliang
A2 - Li, Jianxin
A2 - Min, Geyong
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2023 through 8 October 2023
ER -