Detection and mitigation of DDoS attack in cloud computing using machine learning algorithm

Amjad, Aroosh and Alyas, Tahir and Farooq, Umer and Tariq, Muhammad Arslan (2019) Detection and mitigation of DDoS attack in cloud computing using machine learning algorithm. EAI Endorsed Transactions on Scalable Information Systems, 6 (26): e7. ISSN 2032-9407

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Abstract

Cloud computing, with its staggering and on-demand services had revamped the technology so far. Cloud consumers are freely to use the applications and software on the premises of Pay-as-you go concept. This concept decreased the cost and make the services less expensive and more reliable. One of the most important characteristic of cloud structure is on demand self-service. Cloud computing applications can be accessed anywhere at any time with much less cost. As cloud provide its consumers with its tremendous on demand services, besides this it is surviving from the excruciating security issues that are discourteous towards the cloud. There are, as many different attacks that results in making the servers down. One of the most hazardous attack is DDoS. This paper hiloghted the DDoS attack and its prevention technique which results in making the server side less vulnerable. The scenario includes, a transmission of million and trillion of packets in the form of DDoS at cloud-based websites, thus making it differentiated though different hosts. Making use of operating systems such as ParrotSec to make the attack possible. Last step includes detection and prevention through the most effective algorithms namely, Naïve Bayes and Random forest. This paper also focused the categories of attacks on cloud computing.

Item Type: Article
Uncontrolled Keywords: Cloud computing, DDoS, vulnerability, sql injection, mitigation, TCP/IP, UDP, ICMP packets, malicious, exploit
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:54
Last Modified: 08 Oct 2020 13:54
URI: https://eprints.eudl.eu/id/eprint/695

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