LIÊN KẾT WEBSITE
HA-IDS: A heterogeneous anomaly-based intrusion detection system
2017 4th NAFOSTED Conference on Information and Computer Science, NICS 2017 - Proceedings Số , năm 2017 (Tập 2017-January, trang 156-161)
DOI: 10.1109/NAFOSTED.2017.8108056
Tài liệu thuộc danh mục: Scopus
Conference Paper
English
Từ khóa: Backpropagation; Classification (of information); Computer crime; Computer graphics; Computer graphics equipment; Field programmable gate arrays (FPGA); Graphics processing unit; Mercury (metal); Network security; Neural networks; Program processors; Anomaly based IDS; Anomaly based intrusion detection systems; Anomaly-based NIDS; Back propagation neural networks; CUDA; Feature construction; Graphics Processing Unit (GPU); Large scale data; Intrusion detection
Tóm tắt tiếng anh
In this research, we propose a heterogeneous anomaly-based intrusion detection system (HA-IDS) which is built on both of Field-Programmable Gate Array (FPGA) and Graphics Processing Unit (GPU) platforms. An essential anomaly-based IDS comprises of the two main components: Feature Construction Module (FC) to extract and collect network header information, and Classification Module (CM) to categorize connections' behaviors. In our proposed system, FC is implemented on FPGA to handle large-scale data while CM is deployed on GPU using Back-propagation Neuron Network to utilize the parallel computing for improving the system performance. We employ our proposed system on GPU - GIGABYTE GeForce GTX 1080 and FPGA - Xilinx Virtex-5 XC5VTX240T chip. The experiment result shows that the training outcome on GPU is faster than that on CPU by up to 12x. The testing system throughput in real-time is 200Mbps with more than 80% in Accuracy Rate. This work proves that this is promising to take advantages of these two platforms to apply to a particular issue. � 2017 IEEE.
