Prediction of DDoS Attacksusing Machine Learning and Deep Learning Algorithms
Saritha1, B. RamaSubba Reddy2, A Suresh Babu3
1Saritha, Research Scholar, Department of Computer Science Engineering, JNTUA College of Engineering, Anantapuramu, Andhra Pradesh, India
2B. RamaSubba Reddy, Professor, Department of Computer Science Engineering, SV College of Engineering, Tirupati, Andhra Pradesh, India
3A Suresh Babu, Professor and HOD, Department of Computer Science Engineering, JNTUA College of Engineering, Anantapuramu, Andhra Pradesh, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4860-4867 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8162118419/2019©BEIESP | DOI: 10.35940/ijrte.D8162.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the emergence of network-based computing technologies like Cloud Computing, Fog Computing and IoT (Internet of Things), the context of digitizing the confidential data over the network is being adopted by various organizations where the security of that sensitive data is considered as a major concern. Over a decade there is a massive growth in the usage of internet along with the technological advancements that demand the need for the development of efficient security algorithms that could withstand various patterns of the security breaches. The DDoS attack is the most significant network-based attack in the domain of computer security that disrupts the internet traffic of the target server. This study mainly focuses to identify the advancements and research gaps in the development of efficient security algorithms addressing DDoS attacks in various ubiquitous network environments.
Keywords: Ddos Attack, Machine Learning, Deep Learning, Volumetric Attacks, Protocol Attacks
Scope of the Article: Machine Learning.