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The technology is growing rapidly and cloud computing usage is increasing. Most of the big and small companies use the cloud nowadays. Cloud computing has the economic benefit which is paid as you use (i.e. pay on the demand). With the increase of cloud usage, security problems on the cloud also increasing. Some mechanisms like firewall, vulnerability scanners and Intrusion Detection System (IDS) and other methods are used to mitigate the intrusions, but they are not enough to detect attacks against the cloud due to new intrusion releases. There are a variety of security methods for improving cloud security from threats and vulnerabilities. In this paper, a new hybrid cloud-based IDRS based on Grey wolf optimizer (GWO) and Neural Network (NN) is proposed to secure and detect intrusions over the cloud. GWO is one of the effective metaheuristic algorithms in many fields such as security. In this paper, GWO is employed to train an NN and the results are compared with other classification algorithms. For experimental results, most up-to-date intrusion
International Conference on Cyber Security and Computer Science
ICONCS
İsmai M. Nur
Erkan Ülker