TY - GEN
T1 - A privacy-preserving framework for collaborative intrusion detection networks through fog computing
AU - Wang, Yu
AU - Xie, Lin
AU - Li, Wenjuan
AU - Meng, Weizhi
AU - Li, Jin
N1 - Funding Information:
Acknowledgment. This work was partially supported by National Natural Science Foundation of China (No. 61472091), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (2014A030306020), Science and Technology Planning Project of Guangdong Province, China (2015B010129015) and the Innovation Team Project of Guangdong Universities (No. 2015KCXTD014).
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS is able to implement more complicated detection algorithms by offloading the expensive operations such as the process of signature matching to the cloud (i.e., utilizing computing resources from the cloud). However, during the detection process, no party wants to disclose their own data especially sensitive information to others for privacy concerns, even to the cloud side. For this sake, privacy-preserving technology has been applied to IDSs, while it still lacks of proper solutions for a collaborative intrusion detection network (CIDN) due to geographical distribution. A CIDN enables a set of dispersed IDS nodes to exchange required information. With the advent of fog computing, in this paper, we propose a privacy-preserving framework for collaborative networks based on fog devices. Our study shows that the proposed framework can help reduce the workload on cloud’s side.
AB - Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS is able to implement more complicated detection algorithms by offloading the expensive operations such as the process of signature matching to the cloud (i.e., utilizing computing resources from the cloud). However, during the detection process, no party wants to disclose their own data especially sensitive information to others for privacy concerns, even to the cloud side. For this sake, privacy-preserving technology has been applied to IDSs, while it still lacks of proper solutions for a collaborative intrusion detection network (CIDN) due to geographical distribution. A CIDN enables a set of dispersed IDS nodes to exchange required information. With the advent of fog computing, in this paper, we propose a privacy-preserving framework for collaborative networks based on fog devices. Our study shows that the proposed framework can help reduce the workload on cloud’s side.
KW - Cloud environment
KW - Collaborate network
KW - Fog computing
KW - Intrusion detection
KW - Privacy preserving
UR - http://www.scopus.com/inward/record.url?scp=85034225563&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69471-9_20
DO - 10.1007/978-3-319-69471-9_20
M3 - Conference article published in proceeding or book
AN - SCOPUS:85034225563
SN - 9783319694702
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 279
BT - Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
A2 - Wu, Wei
A2 - Castiglione, Aniello
A2 - Wen, Sheng
PB - Springer Verlag
T2 - 9th International Symposium on Cyberspace Safety and Security, CSS 2017
Y2 - 23 October 2017 through 25 October 2017
ER -