Network Intrusion Detection Method Based on Rough Set Theory

doi:10.3969/j.issn.1000-3428.2006.16.050

Computer Engineering ›› 2006, Vol. 32 ›› Issue (16): 133-135. doi: 10.3969/j.issn.1000-3428.2006.16.050

• Security Technology • Previous Articles Next Articles

Network Intrusion Detection Method Based on Rough Set Theory

CHEN Weitong1,2; QIAN Yuntao1,2

1. College of Computer Science, Zhejiang University, Hangzhou 310027; 2. State Key Laboratory of Information Security(Graduate School of Chinese Academy of Sciences), Beijing 100039

Received:1900-01-01 Revised:1900-01-01 Online:2006-08-20 Published:2006-08-20

基于粗糙集理论的网络入侵检测方法

陈伟统1,2;钱沄涛1,2

1. 浙江大学计算机学院，杭州 310027；2. 信息安全国家重点实验室(中国科学院研究生院)，北京 100039

Abstract

Abstract: This paper presents a network intrusion detection method based on rough set theory, and uses a hybrid method with genetic algorithm to find a possibly short reduct which can reduce computing time. The result of the experiment shows that this model has high detection rate and low false reject rate in detecting DoS and probe attacks, and performs well in detecting R2L and U2R attacks either.

Key words: Rough set theory, Intrusion detection, Genetic algorithm, Heuristic algorithm

摘要： 提出了基于粗糙集理论的网络入侵检测方法，应用混合遗传算法求解粗糙集的约简，减少了计算时间。实验结果表明，该方法对DoS和Probe攻击具有很高的检测率和较低的误检率，并且对U2R和R2L攻击也有较好的检测率。

关键词: Rough Set理论, 入侵检测, 遗传算法, 启发式算法

CHEN Weitong;; QIAN Yuntao;. Network Intrusion Detection Method Based on Rough Set Theory[J]. Computer Engineering, 2006, 32(16): 133-135.

陈伟统;;钱沄涛;. 基于粗糙集理论的网络入侵检测方法[J]. 计算机工程, 2006, 32(16): 133-135.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.3969/j.issn.1000-3428.2006.16.050

http://www.ecice06.com/EN/Y2006/V32/I16/133

[1]	CHEN Zhonglei, YI Peng, CHEN Xiang, HU Tao. Real-time Anomaly Detection Framework via System Calls Based on Integrated Learning [J]. Computer Engineering, 2023, 49(6): 162-169,179.
[2]	BAI Zhixu, WANG Hengjun. Adversarial Example Generation Method Based on Improved Genetic Algorithm [J]. Computer Engineering, 2023, 49(5): 139-149.
[3]	SANG Yongxuan, WEI Jiangpo, WANG Bo, SONG Ying. Hybrid Heuristic Task Offloading Algorithm with Edge Caching Mechanism [J]. Computer Engineering, 2023, 49(4): 149-158.
[4]	SHI Lei, ZHANG Jitao, GAO Yufei, WEI Lin, TAO Yongcai. Intrusion Detection of Network Traffic Based on Transformer and BiLSTM [J]. Computer Engineering, 2023, 49(3): 29-36,57.
[5]	LIU Qiang, ZHANG Ying, ZHOU Weixiang, JIANG Xiantao, ZHOU Weina, ZHOU Mouguo. Adaptive Class Incremental Learning-Based IoT Intrusion Detection System [J]. Computer Engineering, 2023, 49(2): 169-174.
[6]	SUN Yangwei, QI Yong. Intrusion Detection Method for In-Vehicle CAN Based on Cluster Mixed Sampling and PSO-Stacking [J]. Computer Engineering, 2023, 49(1): 138-145.
[7]	LIU Jinshuo, ZHAN Daiyi, DENG Juan, WANG Lina. Network Intrusion Detection Based on Deep Neural Network and Federated Learning [J]. Computer Engineering, 2023, 49(1): 15-21,30.
[8]	DONG Weiyu, LI Haitao, WANG Ruimin, REN Huajuan, SUN Xuekai. Network Traffic Anomaly Detection Model Based on Stacked Convolutional Attention [J]. Computer Engineering, 2022, 48(9): 12-19.
[9]	MA Huawei, MA Kai, GUO Jun. Research on Vehicle Routing Problem with Drones Considering Multi-Delivery [J]. Computer Engineering, 2022, 48(8): 299-305.
[10]	HUANG Jinyao, LIU Tonglai, WU Jiaxin, WU Jigang. Route Planning and Scheduling Algorithm of Multi Period Home Health Care [J]. Computer Engineering, 2022, 48(7): 292-299.
[11]	JIN Haibo, ZHAO Xinyue. High-Reliability Intrusion Detection Algorithm Under Conformal Prediction Framework [J]. Computer Engineering, 2022, 48(7): 130-140.
[12]	SHENG Long, YUAN Lina, WU Nannan, JI Shaopei. Network Anomaly Detection Model Based on GSA and DE Optimizing Hybrid Kernel ELM [J]. Computer Engineering, 2022, 48(6): 146-153.
[13]	LI Jinguo, JIAO Xubin. Research on Intrusion Detection Model in Fog Computing Environment [J]. Computer Engineering, 2022, 48(5): 43-52.
[14]	SONG Yongchun, WANG Qianzhu, GAO Zhengnian. D2D-NOMA Resource Allocation Optimization Algorithm Based on HAGA [J]. Computer Engineering, 2022, 48(2): 275-280,290.
[15]	MIAO Xin, CHEN Xuan, BAO Hongying, ZHANG Jingxuan, YU Wei. Study on Path Sweep Coverage Problem in Mobile Sensor Networks [J]. Computer Engineering, 2022, 48(12): 150-155,164.

Please choose a citation manager

Content to export

Network Intrusion Detection Method Based on Rough Set Theory

基于粗糙集理论的网络入侵检测方法

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

模态框（Modal）标题

Please choose a citation manager

Content to export

Network Intrusion Detection Method Based on Rough Set Theory

基于粗糙集理论的网络入侵检测方法

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments