Statistical based distributed denial of service attack detection research in internet of things

被引：0

作者：

Chen H.-S. ^{[1
]}

Chen J.-J. ^{[1
]}

机构：

[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 05期

关键词：

Abnormal traffic detection; Attack simulation; Distributed denial of service; Internet of things simulation; Statistical analysis;

D O I：

10.13229/j.cnki.jdxbgxb20190448

中图分类号：

学科分类号：

摘要：

To solve the problem of large-scale Distributed Denial of Service (DDoS) attack detection in Internet of Things (IoT) simulation environment, the Docker virtualized container technology is used to construct the IoT traffic simulation platform. First, four different types of attack traffic are generated by simulating Mirai botnet and executing commands, and normal traffic is generated by manual click and IoT experiment box auto execution. Then, statistical analysis is carried out on the original traffic to generate two different levels of datasets: packet-level and second-level. Third, three statistical analysis methods and indicators are proposed, including segmented HURST exponent, sliding-window based entropy and sliding-window based confidence interval. Finally, the DDoS attack traffic detection rules are generated by the training dataset. The experimental results show that the sliding-window based confidence interval abnormal traffic detection method can achieve an accuracy of 72.11%. © 2020, Jilin University Press. All right reserved.

引用

页码：1894 / 1904

页数：10

共 16 条

[1] Kolias C, Kambourakis G, Stavrou A, Et al., DDoS in the IoT: mirai and other botnets, Computer, 50, 7, pp. 80-84, (2017)
[2] Hilton S., Dyn analysis summary of friday october 21 attack Dyn
[3] Sahi A, Lai D, Li Y, Et al., An efficient DDoS TCP flood attack detection and prevention system in a cloud environment, IEEE Access, 5, pp. 6036-6048, (2017)
[4] Gurulakshmi K, Nesarani A., Analysis of IoT bots against DDOS attack using machine learning algorithm, 2018 2nd International Conference on Trends in Electronics and Informatics(ICOEI), pp. 1052-1057, (2018)
[5] Doshi R, Apthorpe N, Feamster N., Machine learning DDoS detection for consumer internet of things devices, 2018 IEEE Security and Privacy Workshops(SPW), pp. 29-35, (2018)
[6] Ozcelik M, Chalabianloo N, Gur G., Software-Defined edge defense against IoT-based DDoS, 2017 IEEE International Conference on Computer and Information Technology(CIT), pp. 308-313, (2017)
[7] Mishra A, Dixit A., Resolving threats in IoT: ID spoofing to DDoS, 2018 9th International Conference on Computing, Communication and Networking Technologies(ICCCNT), pp. 1-7, (2018)
[8] Ben S N, Biondi F, Bontchev V, Et al., Detection of mirai by syntactic and behavioral analysis, 2018 IEEE 29th International Symposium on Software Reliability Engineering(ISSRE), pp. 224-235, (2018)
[9] Agrawal N, Tapaswi S., Low rate cloud DDoS attack defense method based on power spectral density analysis, Information Processing Letters, 138, pp. 44-50, (2018)
[10] Hirakawa T, Ogura K, Bista B B, Et al., A defense method against distributed slow HTTP DoS attack, 2016 19th International Conference on Network-Based Information Systems(NBiS), pp. 152-158, (2016)

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