Detecting and confronting flash attacks from IoT botnets

Autor:	Ponsy R. K. Sathia Bhama, C. U. Om Kumar
Rok vydání:	2019
Předmět:	business.industry Computer science Botnet Denial-of-service attack Cloud computing Computer security computer.software_genre Theoretical Computer Science Crowds Hardware and Architecture Server Malware The Internet business computer Software Information Systems
Zdroj:	The Journal of Supercomputing. 75:8312-8338
ISSN:	1573-0484 0920-8542
DOI:	10.1007/s11227-019-03005-2
Popis:	Gone are the days when cloud providers were attacked by flash crowds causing a DoS or malware running on a very large number of servers creating a DDoS. As the number of IoT devices connected to the Internet steadily increases, the cloud faces threats of flash crowds of IoT botnets controlled by malware such as Mirai, Bashlite and cryptojacking. In this paper, we propose and implement an adaptive filter that curtails DDoS attacks from a variety of compromised IoT bots. Experiments conclude that detection of IoT Botnets can be achieved with an accuracy rate of 99.69% and the detection of cryptojacking with a misclassification rate of 1.5%. The performance of the proposed adaptive filter is tested using the Amazon public cloud platform, and the results show that the adaptive filter can significantly reduce illegitimate botnet requests from variants such as FBOT, ARIS, EXIENDO and APEP and can reduce the instances processing time by 19%, connection time by 34% and the waiting time by 18%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1597781448b7ba47b3dc33de97ee19f1 https://doi.org/10.1007/s11227-019-03005-2 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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