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Proficient Detection of Flash Attacks Using a Predictive Strategy

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Emerging Research in Computing, Information, Communication and Applications

Abstract

The availability of service through cloud computing upon the emergence of IoT and wireless networking has exposed the security of information to an even greater risk of abuse. These security threats are caused by cyber criminals through Malware, Cracker, Insider and Zombie. Among the strategies attacking the network DoS or DDoS is disastrous as its objective is to paralyse the complete network system. The bursty or volatile nature of traffic is indicative of a surge in traffic flow. Such an event also implies the incidence of a DDoS attack. If left unchecked this attack will cause a disruption in service. The study focuses on deriving a statistical model for discriminating malign traffic by devising a predictive model using error optimization technique and Hurst Correlogram analysis for effectively detecting and controlling the spread of bot-induced DDoS attack in a cloud environment. This work proposes ARIMA model that detects DDoS with an accuracy of 92% which is further assessed through MLR with an accuracy of 84%. The Hurst estimation of the univariate lies very close to the Hurst of raw data needs mention and proves the maturity of the prediction strategy.

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Correspondence to C. U. Om Kumar .

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Om Kumar, C.U., Sathia Bhama, P.R.K. (2022). Proficient Detection of Flash Attacks Using a Predictive Strategy. In: Shetty, N.R., Patnaik, L.M., Nagaraj, H.C., Hamsavath, P.N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 789. Springer, Singapore. https://doi.org/10.1007/978-981-16-1338-8_32

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  • DOI: https://doi.org/10.1007/978-981-16-1338-8_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1337-1

  • Online ISBN: 978-981-16-1338-8

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