基于分块神经网络和现场可编程门阵列的主机入侵检测系统

Quang-Anh Tran, F. Jiang, Quang Minh Ha
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引用次数: 15

摘要

在本文中,我们设计了一个基于不断发展的基于块的神经网络(BBNN)的混合软件检测引擎原型,并将其与现场可编程门阵列(FPGA)板集成,以实现基于主机的实时入侵检测系统(IDS)。建立的原型可以将从服务器获得的系统调用序列直接馈送到基于BBNN的IDS中。BBNN的结构和权值采用遗传算法进化。通过进行留一交叉验证,对四种主要的支持向量机(svm)进行了实验性能比较。结果表明,改进后的BBNN在分类和检测性能方面优于其他算法。成功将虚警率降至2.22%,检测率保持100%。本文还讨论了所提出的基于硬件的IDS与其他基于软件的系统的运行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving Block-Based Neural Network and Field Programmable Gate Arrays for Host-Based Intrusion Detection System
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.
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