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Cloud Attacks Detection System for Cloud Load Balancing


Swathi Sambangi and Lakshmeeswari Gondi


Vol. 22  No. 5  pp. 729-740


One of the most recent problems of interest in cloud computing is securing the cloud networks by identification and mitigation of cloud network attacks such as the distributed denial of service attacks. By deploying efficient IDS in cloud networks which we term in this paper as the Cloud IDS, we can achieve cloud load balancing. For this, in this paper, we propose a machine learning based IDS for deployment in cloud networks. Our machine learning method has two stages. In the first stage, the traffic dimensionality is reduced by the proposed method. In the second stage, the network traffic classification is carried by the proposed network traffic similarity function. For experimental analysis, we have used CICIDS 2019 traffic data. The results proved that the proposed method has performed substantially better to state-of-art machine learning classifiers.


Cloud, DDoS attacks, Classification, Prediction, Intrusion detection, anomaly detection.