Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (10): 889-899.doi: 10.23940/ijpe.21.10.p7.889899

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Adaptive Model to Detect Anomaly and Real-Time Attacks in Cloud Environment Using Data Mining Algorithm

D. Sakthivela,* and B. Radhab   

  1. aComputer Science, Sree Saraswathi Thyagaraja College, Bharathiar University, Tamilnadu, 641046, India;
    bComputer Science, Sri Krishna Arts and Science College, Bharathiar University, Tamilnadu, 641046, India
  • Submitted on ; Revised on ; Accepted on

Abstract: The potential benefits and enhancement of services have made cloud computing an attractive field in the current era. Cloud computing has various benefits that has grabbed a focal point among researchers. Cloud service providers pose numerous security challenges and are highly susceptible to attacks. In the context of cloud computing, the anomalies and insiders attacks will deactivate the service providers, which results in the malfunctioning of the entire system. Traditional defense systems in the network are not efficient in handling insider attacks and intrusion. In this work, the anomaly identification technique is developed to identify the attack incidence, and the proposed approach uses the fuzzy min-max neural network (FMM-NN). The classification accuracy is enhanced by the effective identification of features using a neural network. The performance investigation and outcome of the FMM-NN identifies and classifies the real-time attacks in the cloud environment with high identification accuracy.

Key words: cloud computing, neural network, attacks, detection of intrusion, cloud security