Abstract
Cloud Computing is basically the use of software and hardware to provide a service over an internet network. Users use applications or can access files from any device with the help of cloud computing. The main thing is that device must be connected through the Internet. Cloud computing has many advantages like scalability, less maintenance, virtualization and requested resources to the users with reduced infrastructure cost, and greater flexibility. It faces many drawbacks like security attack as Distributed Denial of Service (DDoS).
DDoS attack is well-defined as a way of attack that includes multiple conceded computer systems attack a goal, like a server, any resource and website, and due to this a denial of service for the end users of the intended resource. The fake connection requests, flooding of inward messages, or distorted packets forces the whole system to slow down and shut down, in that way denying service to genuine end users and systems. In this paper we have analyzed and proposed the machine learning algorithms for detecting DDoS attack in cloud computing environment. This paper is using isolation forest anomaly detection technique and then the correlation will be used to for detection of DDoS attack.
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Sharma, V., Verma, V., Sharma, A. (2019). Detection of DDoS Attacks Using Machine Learning in Cloud Computing. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0111-1_24
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DOI: https://doi.org/10.1007/978-981-15-0111-1_24
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