TY - GEN
T1 - DDoS Attacks Detection with AutoEncoder
AU - Yang, Kun
AU - Zhang, Junjie
AU - Xu, Yang
AU - Chao, Jonathan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, DDoS attacks are still a major problem for network security. The latent and inherent problems of these detection algorithms are 1) Requirement of both normal and attack data for building detection models, and 2) Almost inability to detect novel and unknown DDoS attacks. To conquer the problems, this paper proposes an AutoEncoder based DDoS attacks Detection Framework (AE-D3F), which only uses normal traffic to build the detection model and is able to update itself automatically as time goes. Experimental results on synthetic and public traffic show that our AE-D3F can not only achieve 82.00% detection rate (DR) with 0 false positive rate (FPR), better than classical anomaly detection approaches, but also detect novel and unknown attacks.
AB - Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, DDoS attacks are still a major problem for network security. The latent and inherent problems of these detection algorithms are 1) Requirement of both normal and attack data for building detection models, and 2) Almost inability to detect novel and unknown DDoS attacks. To conquer the problems, this paper proposes an AutoEncoder based DDoS attacks Detection Framework (AE-D3F), which only uses normal traffic to build the detection model and is able to update itself automatically as time goes. Experimental results on synthetic and public traffic show that our AE-D3F can not only achieve 82.00% detection rate (DR) with 0 false positive rate (FPR), better than classical anomaly detection approaches, but also detect novel and unknown attacks.
KW - Anomaly Detection
KW - DDoS
KW - Deep Learning
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85086767533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086767533&partnerID=8YFLogxK
U2 - 10.1109/NOMS47738.2020.9110372
DO - 10.1109/NOMS47738.2020.9110372
M3 - Conference contribution
AN - SCOPUS:85086767533
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Y2 - 20 April 2020 through 24 April 2020
ER -