A Novel Framework for Prevention against DDoS Attacks using Software Defined Machine Learning Model

doi:10.23940/ijpe.22.08.p6.580588

Abstract

Abstract: A DDoS attack is similar to a Denial-of-Service attack in that harmful traffic is generated from various sources and orchestrated from a single point. Distributed Denial of Service attack prevention is substantially more difficult than DoS attack prevention from a single IP address since the traffic sources are scattered, typically all over the world. The main aim of this research paper is to provide a novel framework in which DDoS can be detected at an early stage by making use of a machine learning model and then proper mitigation methods can be taken. For experimentation, SNORT, an open-source access tool is used and TCP-SYN DDoS traffic is generated using hping 3 tool towards ONOS SDN controller. Our proposed method detects the DDoS attack in early stages and traffic from the destination end is blocked as soon it detects the malicious traffic.

Key words: DDoS, SDN, malicious traffic, machine learning

Ankush Mehra and Sumit Badotra. A Novel Framework for Prevention against DDoS Attacks using Software Defined Machine Learning Model [J]. Int J Performability Eng, 2022, 18(8): 580-588.

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References

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