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An anomaly-based intrusion detection architecture integrated on openflow switch

Van Thanh N. Ho Chi Minh City University of Technology, HCM National University, Viet Nam|
Thinh T.N. | Bao H. HCMC University of Technology and Education, Viet Nam|

ACM International Conference Proceeding Series Số , năm 2016 (Tập , trang 96-100)

ISSN: 125834

ISSN: 125834

DOI: 10.1145/3017971.3017982

Tài liệu thuộc danh mục: Scopus

ACM Int. Conf. Proc. Ser.

English

Từ khóa: Field programmable gate arrays (FPGA); Intrusion detection; Mercury (metal); Network architecture; Security of data; Software defined networking; Anomaly based IDS; Anomaly-based intrusion detection; Innovative solutions; Internet based system; Openflow networks; Programmable network; Security challenges; Software defined networking (SDN); Network security
Tóm tắt tiếng anh
Recently, Internet-based systems need to be changed their configuration dynamically. Traditional networks have very limited ability to cope up with such frequent changes and hinder innovations management and configuration procedures. To address this issue, Software Defined Networking (SDN) has been emerging as a new network architecture that allows for more flexibility through software-enabled network control. However, the dynamism of programmable networks also faces new security challenges that demand innovative solutions. Among the widespread mechanisms of SDN security control applications, anomaly-based IDS is an extremely effective technique in detecting both known and unknown (new) attack types. In this paper, we propose an anomaly-based Intrusion Detection architecture integrated on OpenFlow Switch. The proposed system can detect and prevent a network from many attack types, especially new attack types using anomaly detection. We implement the proposed system on the FPGA technology using a Xilinx Virtex-5 xc5vtx240t device. In this FPGA-based prototype, we integrate an anomaly-based intrusion detection technique to be able to defend against many attack types and anomalous on the network traffic. The experimental results show that our system achieves a detection rate exceeding 91.81% with a 0.55% false alarms rate at maximum. � 2016 ACM.

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