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
[41]   Introducing a Classification Model Based on SVM for Network Intrusion Detection [J].
Dastfal, Ghodratolah ;
Nejatian, Samad ;
Parvin, Hamid ;
Rezaie, Vahideh .
ADVANCES IN SOFT COMPUTING, MICAI 2017, PT I, 2018, 10632 :54-66
[42]   Network intrusion detection based on IE-DBN model [J].
Jia, Huaping ;
Liu, Jun ;
Zhang, Min ;
He, Xiaohu ;
Sun, Weixi .
COMPUTER COMMUNICATIONS, 2021, 178 :131-140
[43]   The XGBoost Model for Network Intrusion Detection Boosted by Enhanced Sine Cosine Algorithm [J].
AlHosni, Nadheera ;
Jovanovic, Luka ;
Antonijevic, Milos ;
Bukumira, Milos ;
Zivkovic, Miodrag ;
Strumberger, Ivana ;
Mani, Joseph P. ;
Bacanin, Nebojsa .
THIRD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND CAPSULE NETWORKS (ICIPCN 2022), 2022, 514 :213-228
[44]   Network Intrusion Detection Using Genetic Algorithm and Neural Network [J].
Gomathy, A. ;
Lakshmipathi, B. .
ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY, 2011, 198 :399-408
[45]   An Intrusion Detection Algorithm Based On NFPA [J].
Zhong Anming .
2012 INTERNATIONAL CONFERENCE ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING (ICMPBE2012), 2012, 33 :491-497
[46]   An Intrusion Detection Algorithm Based on NFPA [J].
Zhong Anming ;
Yang Hongyu .
2011 AASRI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRY APPLICATION (AASRI-AIIA 2011), VOL 1, 2011, :328-331
[47]   Intrusion Detection Algorithm Based on Model Checking Interval Temporal Logic [J].
Zhu Weijun ;
Wang Zhongyong ;
Zhang Haibin .
CHINA COMMUNICATIONS, 2011, 8 (03) :66-72
[48]   An Intrusion Detection Model Based on Fuzzy C-means Algorithm [J].
Man, Liyu ;
Xian, Youan .
2018 8TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC), 2018, :120-123
[49]   Research on Intrusion Detection Model Based on Filter Feature Selection Algorithm [J].
Liu, Qingjie ;
Li, Yong .
2024 8TH INTERNATIONAL CONFERENCE ON COMMUNICATION AND INFORMATION SYSTEMS, ICCIS 2024, 2024, :120-125
[50]   An Intrusion Detection Model Based on Improved Whale Optimization Algorithm and XGBoost [J].
Zong, Xinlu ;
Li, Ruicheng ;
Ye, Zhiwei .
PROCEEDINGS OF THE THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 1, 2021, :542-547