An Analysis of DDoS Attack Detection and Mitigation Using Machine Learning System

Abstract

Nowadays, many companies and/or governments require a secure system and/or an accurate intrusion detection system (IDS) to defend their system service and the user’s private information. In network security, developing an accurate discovery system for distributed denial of service (DDos) attacks is one of challenging tasks. DDos attacks jam the network service of the target using multiple bots hijacked by crackers and send frequent packets to the target server. Servers of many companies and/or governments have been victims of the attacks. In such a command by multiple bots from another network and then leave the bots quickly after command execute. The proposed strategy is to develop an intelligent detection system for DDos attacks by detecting patterns of DDos attacks using system packet analysis and exploiting machine learning techniques to study the patterns of DDos attacks. In this study, we analysed large numbers of network packets provided by the Center for applied internet data analysis and Applied the detection system using an Ad-hoc On-demand distance Vector (AODV) and Adaptive information dissemination (AID) protocols. The discovery system is accurate in detecting DDos.