User Behaviour Modelling Based DDoS Attack Detection

被引：0

作者：

Cepheli, Ozge ^{[1
]}

Buyukcorak, Saliba ^{[1
]}

Kurt, Gunes Karabulut ^{[1
]}

机构：

[1] Istanbul Tech Univ, Elekt & Haberlesme Muhendisligi Bolumu, Telsiz Arastirma Lab, Istanbul, Turkey

来源：

2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2014年

关键词：

DDoS; user modeling; expectation maximization; EM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Distributed Denial of Service (DDoS) attacks are one of the most important threads in network systems. Due to the distributed nature, DDoS attacks are very hard to detect, while they also have the destructive potential of classical denial of service attacks. In this study, a novel 2-step system is proposed for the detection of DDoS attacks. In the first step an anomaly detection is performed on the destination IP traffic. If an anomaly is detected on the network, the system proceeds into the second step where a decision on every user is made due to the behaviour models. Hence, it is possible to detect attacks in the network that diverges from users' behavior model.

引用

页码：2186 / 2189

页数：4

共 50 条

[21] DDoS Attack Detection Method Based on Fractional Fourier Transform
Chen Shi-wen
Wu Jiang-xing
Guo Tong
Lan Ju-long
[J]. 2012 THIRD INTERNATIONAL CONFERENCE ON THEORETICAL AND MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE (ICTMF 2012), 2013, 38 : 250 - 255
[22] DDoS attack detection method based on LSTM traffic prediction
Cheng, Jieren
Luo, Yihan
Tang, Xiangyan
Ou, Mingwang
[J]. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2019, 47 (04): : 32 - 36
[23] FLDDoS: DDoS Attack Detection Model based on Federated Learning
Zhang, Jiachao
Yu, Peiran
Qi, Le
Liu, Song
Zhang, Haiyu
Zhang, Jianzhong
[J]. 2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021), 2021, : 635 - 642
[24] WEB DDoS Attack Detection Method Based on Semisupervised Learning
Yu, Xiang
Yu, Wenchao
Li, Shudong
Yang, Xianfei
Chen, Ying
Lu, Hui
[J]. SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
[25] DDoS ATTACK DETECTION METHODS BASED ON DEEP LEARNING IN HEALTHCARE
Wang, Chaoying
Zhu, Ting
[J]. JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2023, 23 (04)
[26] Research on DDoS Attack Detection Based on ELM in IoT Environment
Li, Zhihui
Wei, Lihong
Li, Wei
Wei, Lai
Chen, Minshi
Lv, Ming
Zhi, Xulong
Wang, Chenguang
Gao, Nan
[J]. PROCEEDINGS OF 2019 IEEE 10TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2019), 2019, : 144 - 148
[27] DDoS Attack Detection Algorithm Based on IP Entropy Model
Wang Xintong
Liu Guqing
Yang Jungang
Ran Jinzhi
[J]. PROCEEDINGS OF THE 2015 INTERNATIONAL INDUSTRIAL INFORMATICS AND COMPUTER ENGINEERING CONFERENCE, 2015, : 179 - 182
[28] A Research Review on SDN-Based DDOS Attack Detection
Zhu, Weidong
Yi, Xiujuan
[J]. PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND MANAGEMENT INNOVATION (MSMI 2017), 2017, 31 : 145 - 149
[29] A CGAN-based DDoS Attack Detection Method in SDN
Liu
Luo
Jiang
Wang
Li
Jia
[J]. IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2021, : 1030 - 1034
[30] Detection of Hijacking DDoS Attack Based on Air Interface Traffic
Fu, Qing-Yi
Wang, Hui-Ming
[J]. IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (10) : 2225 - 2229

← 1 2 3 4 5 →