| Title | WANHEDA: A Machine Learning Based DDoS Detection System |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sudugala, A.U, Chanuka, W.H, Eshan, A.M.N, Bandara, U.C.S, Abeywardena, K.Y |
| Conference Name | 2020 2nd International Conference on Advancements in Computing (ICAC) |
| Date Published | Dec. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-8412-8 |
| Keywords | composability, Computer crime, DDoS attack detection, DDoS Attacks, denial-of-service attack, Human Behavior, Internet, Internet service providers, Intrusion detection, intrusion detection system, machine learning, machine learning algorithms, Metrics, pubcrawl, resilience, Resiliency, telecommunication traffic |
| Abstract | In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms. |
| URL | https://ieeexplore.ieee.org/document/9357130 |
| DOI | 10.1109/ICAC51239.2020.9357130 |
| Citation Key | sudugala_wanheda_2020 |
WANHEDA: A Machine Learning Based DDoS Detection System