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Evolving block-based neural network and field programmable gate arrays for host-based intrusion detection system
Proceedings - 4th International Conference on Knowledge and Systems Engineering, KSE 2012 Số , năm 2012 (Tập , trang 86-92)
DOI: 10.1109/KSE.2012.31
Tài liệu thuộc danh mục: Scopus
Conference Paper
English
Từ khóa: Block-based neural networks; Detection performance; Detection rates; False alarm rate; Host-based intrusion detection system; Intrusion Detection Systems; Leave-one-out cross validations; Performance comparison; Running time; Software-based; System calls; Computer crime; Field programmable gate arrays (FPGA); Software prototyping; Support vector machines; Systems engineering; Websites; Intrusion detection
Tóm tắt tiếng anh
In this paper, we design a prototype with hybrid software-enabled detection engine on the basis of an evolving block-based neural network (BBNN), and integrate it with a Field Programmable Gate Arrays (FPGA) board to enable a real-time host-based intrusion detection system (IDS). The established prototype can feed sequence of system calls obtained from a server directly into the BBNN based IDS. The structure and weights of BBNN are evolved by Genetic Algorithms. Experimental performance comparisons have been conducted against four major Support Vector Machines (SVMs) by carrying out leave-one-out cross validation. The results show that the improved BBNN outperforms other algorithms with respect to the classification and detection performances. The false alarm rate is successfully reduced as low as 2.22% while the detection rate 100% is still maintained. The running times of the proposed hardware based IDS versus other software based systems are also discussed. � 2012 IEEE.
