A Network Intrusion Detection Algorithm Based on FSA Model

被引:0
作者
Wu, Fei [1 ]
Wu, Donghui [1 ]
Yang, Yingen [1 ]
机构
[1] Jangxi Normal Univ, Coll Comp Informat Engn, Nanchang 330022, Peoples R China
来源
PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY | 2016年 / 60卷
关键词
Intrusion detection; Finite state automaton; Protocol analysis technology; State transition diagrams; Session list;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
At present network attack technology is constantly updated, which bring network security workers huge challenges. In view of the fact that the existing intrusion detection technology is difficult to detect multi-step fragmentation attacks, distributed attacks and evading attacks, a network intrusion detection algorithm called FSA algorithm is proposed based on finite state automaton (FSA) model in this paper, and the key implementation technology is analyzed. The state transition diagram is used to illustrate the attack triggering and transfer process, and according to different protocol data, four different mechanisms are designed to detect invasion based on FSA. Experiments show that the algorithm not only can more precisely detect common attacks, but also can detect the unobvious attacks such as distributed and fragment attack very well, which can not be detected by other detection technologies. It is believe that it removes the limitations of the current intrusion detection technology and has an important research and practice value.
引用
收藏
页码:615 / 621
页数:7
相关论文
共 50 条
[21]   Research on the Detection of Network Intrusion Prevention with SVM Based Optimization Algorithm [J].
Wang, Debing ;
Xu, Guangyu .
INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS, 2020, 44 (02) :269-273
[22]   Research on computer network intrusion detection algorithm based on deep learning [J].
Wei Junbo .
2024 IEEE 7TH INTERNATIONAL CONFERENCE ON AUTOMATION, ELECTRONICS AND ELECTRICAL ENGINEERING, AUTEEE, 2024, :186-190
[23]   An Improved Algorithm for Network Intrusion Detection Based on Deep Residual Networks [J].
Hu, Xuntao ;
Meng, Xiancai ;
Liu, Shaoqing ;
Liang, Lizhen .
IEEE ACCESS, 2024, 12 :66432-66441
[24]   Unsupervised Classification Algorithm for Intrusion Detection based on Competitive Learning Network [J].
Liu, Jifen ;
Gao, Maoting .
ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1, 2008, :519-+
[25]   An industrial network intrusion detection algorithm based on IGWO-GRU [J].
Yang, Wei ;
Shan, Yao ;
Wang, Jiaxuan ;
Yao, Yu .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (06) :7199-7217
[26]   Intrusion detection algorithm based on image enhanced convolutional neural network [J].
Wang, Qian ;
Zhao, Wenfang ;
Ren, Jiadong .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (01) :2183-2194
[27]   A novel network intrusion detection algorithm based on Fast Fourier Transformation [J].
Liu, Weiyou ;
Liu, Xu ;
Di, Xiaoqiang ;
Qi, Hui .
2019 1ST INTERNATIONAL CONFERENCE ON INDUSTRIAL ARTIFICIAL INTELLIGENCE (IAI 2019), 2019,
[28]   Intrusion detection method based on improved social network search algorithm [J].
Yang, Zhongjun ;
Wang, Qi ;
Zong, Xuejun ;
Wang, Guogang .
COMPUTERS & SECURITY, 2024, 140
[29]   An approach to network intrusion detection algorithm based on BP-HMM [J].
Huang Guangqiu ;
Ren Dayong .
Advanced Computer Technology, New Education, Proceedings, 2007, :1321-1326
[30]   SVM Based MLP Neural Network Algorithm and Application in Intrusion Detection [J].
Hou, Yong ;
Zheng, Xue Feng .
ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT III, 2011, 7004 :340-345