Intrusion detection model for RFID system based on immune network

• Information security • Previous Articles Next Articles

Intrusion detection model for RFID system based on immune network

Received:2008-04-28 Revised:1900-01-01 Online:2008-10-01 Published:2008-10-01
Contact: Jian-Hua GUO

基于免疫网络的RFID入侵检测模型研究

郭建华杨海东邓飞其

华南理工大学自动化科学与工程学院华南理工大学自动化科学与工程学院华南理工大学自动化科学与工程学院

通讯作者: 郭建华

Abstract

Abstract: It is very hard to develop encryption technology used in cheap Radio Frequency Identification (RFID) tags. In this paper, intrusion detection, as a new methodology, was adopted to create security model for RFID system. By analyzing typical security attacks on RFID systems, and based on artificial immune network, a solution to extract intrusion characteristics and to identify intrusion was proposed. A self-adaptive intrusion detection model for RFID system was designed. The model can enhance the defense capabilities of RFID systems by cooperating with encryption technology, but has no need to amend the technical standards of RFID. Stimulation results prove that the mistake rate and miss rate of the model are fairly low.

Key words: Radio Frenquency IDentificaiton (RFID), intrusion detection, immune network

摘要： 针对无线射频识别技术（RFID）的加密认证等安全策略在廉价标签上的局限性,采用入侵检测作为RFID系统的新型安全策略，通过分析RFID系统的典型安全攻击，基于人工免疫网络，提出了入侵特征提取方法和入侵分析方法，建立了一个自适应的RFID入侵检测模型。该模型在不需要修改RFID已有技术标准的前提下，与加密认证等已有安全策略互补提升RFID系统的安全防护能力。试验证明该模型具有极低的误检率和漏检率。

关键词: 无线射频识别, 入侵检测, 免疫网络

Jian-Hua GUO Hai-Dong YANG Fei-Qi DENG. Intrusion detection model for RFID system based on immune network[J]. Journal of Computer Applications.

郭建华杨海东邓飞其. 基于免疫网络的RFID入侵检测模型研究[J]. 计算机应用.

[1]	ZHANG Shipeng, LI Yongzhong, DU Xiangtong. Intrusion detection model based on semi-supervised learning and three-way decision [J]. Journal of Computer Applications, 2021, 41(9): 2602-2608.
[2]	WANG Yue, JIANG Yiming, LAN Julong. Intrusion detection based on improved triplet network and K-nearest neighbor algorithm [J]. Journal of Computer Applications, 2021, 41(7): 1996-2002.
[3]	WANG Yao, SUN Guozi. Oversampling method for intrusion detection based on clustering and instance hardness [J]. Journal of Computer Applications, 2021, 41(6): 1709-1714.
[4]	ZHANG Quanlong, WANG Huaibin. Intrusion detection model based on combination of dilated convolution and gated recurrent unit [J]. Journal of Computer Applications, 2021, 41(5): 1372-1377.
[5]	REN Xiaokui, LIU Pengfei, TAO Zhiyong, LIU Ying, BAI Lichun. Indoor intrusion detection based on direction-of-arrival estimation algorithm for single snapshot [J]. Journal of Computer Applications, 2021, 41(4): 1153-1159.
[6]	CHENG Xiaohui, NIU Tong, WANG Yanjun. Wireless sensor network intrusion detection system based on sequence model [J]. Journal of Computer Applications, 2020, 40(6): 1680-1684.
[7]	OU Binli, ZHONG Xiaru, DAI Jianhua, YANG Tian. Intrusion detection method based on variable precision covering rough set [J]. Journal of Computer Applications, 2020, 40(12): 3465-3470.
[8]	LI Zhongwei, TAN Kai, GUAN Yadong, JIANG Wenqi, YE Lin. In-vehicle CAN bus-off attack and its intrusion detection algorithm [J]. Journal of Computer Applications, 2020, 40(11): 3224-3228.
[9]	CHI Yaping, MO Chongwei, YANG Yintan, CHEN Chunxia. Design and implementation of intrusion detection model for software defined network architecture [J]. Journal of Computer Applications, 2020, 40(1): 116-122.
[10]	YANG Hongyu, WANG Fengyan. Network intrusion detection model based on improved convolutional neural network [J]. Journal of Computer Applications, 2019, 39(9): 2604-2610.
[11]	CAO Weidong, XU Zhixiang. Efficient semi-supervised multi-level intrusion detection algorithm [J]. Journal of Computer Applications, 2019, 39(7): 1979-1984.
[12]	PAN Jianguo, LI Hao. Intrusion detection approach for IoT based on practical Byzantine fault tolerance [J]. Journal of Computer Applications, 2019, 39(6): 1742-1746.
[13]	GUO Xudong, LI Xiaomin, JING Ruxue, GAO Yuzhuo. Intrusion detection based on improved sparse denoising autoencoder [J]. Journal of Computer Applications, 2019, 39(3): 769-773.
[14]	HU Jian, SU Yongdong, HUANG Wenzai, XIAO Peng, LIU Yuting, YANG Benfu. Intrusion detection method based on ensemble transfer learning via weighted mutual information [J]. Journal of Computer Applications, 2019, 39(11): 3310-3315.
[15]	SHEN Xueli, QIN Shujuan. Anomaly detection based on synthetic minority oversampling technique and deep belief network [J]. Journal of Computer Applications, 2018, 38(7): 1941-1945.