Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments

Authors: Hariprasad Sivaraman

DOI: https://doi.org/10.5281/zenodo.14259482

Short DOI: https://doi.org/g8s4fk

Country: USA

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Abstract: With the rise of hybrid cloud environments comes new security concerns, including sophisticated Distributed Denial of Service (DDoS) attacks that continuously increase in scale and severity. Multi-vector attacks use different attack vectors, unlike traditional single-vector DDoS attacks that can be released at the same target in a coordinated manner or at several targets, but only using one method of attack; multi-layered attack methods which hit systems at all levels of the network stack. The paper proposes a behavior-based DDoS detection system using machine learning that continuously learns the baselines of network behaviors dynamically and we identify and mitigate the multi-vector in hybrid cloud. Making the switch from static rules to adaptive behavioral models, this approach aims at improving detection rates while being resilient in increasingly complex, hybrid infrastructure.

Keywords: DDoS Detection, Multi-Vector Attacks, Hybrid Cloud, Machine Learning, Behavior Analysis, Anomaly Detection.

Paper Id: 231702

Published On: 2019-09-04

Published In: Volume 7, Issue 5, September-October 2019

Cite This: Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments - Hariprasad Sivaraman - IJIRMPS Volume 7, Issue 5, September-October 2019. DOI 10.5281/zenodo.14259482

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