Towards the use of BPANN Technique for Mitigating Layer 4 DDOS Attack in Electronic Voting

Abstract

System availability is a criterion that e-voting systems are required to satisfy. This involves ensuring that an e voting system is secure whenever a Distributed Denial-of-Service attack occurs and that voters could always have access to the system during election. System availability can be a serious security challenge faced by e-voting system, since publishing results after the conclusion of elections is a major function of the e-voting system, and this process cannot be delayed if the system’s security is under DDoS attack. In the proposed model, the problem of system availability was addressed using an efficient detection sub-system based on Back Propagation Artificial Neural Network (BPANN) to mitigate Transmission Control Protocol Synchronous (TCP SYN) flood attacks. This technique developed would classify TCP SYN Flood packets captured on a workstation that serves as the Internet Voting kiosk and parameters were extracted to analyze and classify the attack. The developed technique would classify TCP SYN Flood packets captured on a workstation that serves as the Internet Voting Kiosk thereby improving the network performance and availability of the e-voting server.