An Anomaly-based Intrusion Detection Architecture Integrated on OpenFlow Switch
Title | An Anomaly-based Intrusion Detection Architecture Integrated on OpenFlow Switch |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Van, Nguyen Thanh, Bao, Ho, Thinh, Tran Ngoc |
Conference Name | Proceedings of the 6th International Conference on Communication and Network Security |
Date Published | November 2016 |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4783-9 |
Keywords | anomaly-based IDS, compositionality, FPGA, Network security, Network Security Architecture, OpenFlow Network, pubcrawl, Resiliency, software defined networking |
Abstract | 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. |
URL | https://dl.acm.org/doi/10.1145/3017971.3017982 |
DOI | 10.1145/3017971.3017982 |
Citation Key | van_anomaly-based_2016 |