DDoS Attack Detection using Machine Learning Techniques

Authors

  • Sapna Jain, Dr. Anurag Sharma, Dr. Abhishek Badholia, Dr.Brijesh Patel, AnupamChoudhary

Abstract

A distributed denial of service (DDoS) attack is a form of cyber-attack in which the attacker attempts to deny the network/server services by inundating the network/server traffic with superfluous requests that make it incapable of serving legitimate user requests. Organizations around the world witnessed an average of 237 DDoS attack attempts per month in Q3 2017, according to Corero Network Security (A DDoSdefence and mitigation provider), which averages 8 DDoS attacks per day. This was an improvement of 35 percent over Q2 that year and an unprecedented 91 percent rise over Q1. A DDoS assault costs companies an average of $40,000 per hour, according to another report by Encapsulate. Software that detects and mitigates a DDoS attack is commercially available, but the high cost of this software makes it difficult for small and mid-scale enterprises to afford it. The proposed work aims to fill this void by providing a robust real-time open-source web application for DDoS attack prediction using machine learning using ensemble learning that can be used to keep their networks and servers protected from malicious DDoS attacks by small to mid-scale industries.

Published

2021-01-27

Issue

Section

Articles