A survey of DDoS attacks using machine learning techniques

M Arshi, MD Nasreen, K Madhavi - E3S Web of Conferences, 2020 - e3s-conferences.org
The DDoS attacks are the most destructive attacks that interrupt the safe operation of
essential services delivered by the internet community's different organizations. DDOS …

Review of detection DDOS attack detection using naive bayes classifier for network forensics

A Fadlil, I Riadi, S Aji - Bulletin of Electrical Engineering and Informatics, 2017 - beei.org
Abstract Distributed Denial of Service (DDoS) is a type of attack using the volume, intensity,
and more costs mitigation to increase in this era. Attackers used many zombie computers to …

A novel hybrid method for network anomaly detection based on traffic prediction and change point detection

M Alkasassbeh - arXiv preprint arXiv:1801.05309, 2018 - arxiv.org
In recent years, computer networks have become more and more advanced in terms of size,
applications, complexity and level of heterogeneity. Moreover, availability and performance …

Fuzzy rule interpolation and snmp-mib for emerging network abnormality

M Almseidin, M Alkasassbeh, S Kovacs - arXiv preprint arXiv:1811.08954, 2018 - arxiv.org
It is difficult to implement an efficient detection approach for Intrusion Detection Systems
(IDS) and many factors contribute to this challenge. One such challenge concerns …

[HTML][HTML] Introducing the slowdrop attack

E Cambiaso, G Chiola, M Aiello - Computer Networks, 2019 - Elsevier
In network security, Denial of Service (DoS) attacks target network systems with the aim of
making them unreachable. Last generation threats are particularly dangerous because they …

[PDF][PDF] Detecting distributed denial of service attacks using machine learning models

ES Alghoson, O Abbass - algorithms, 2021 - academia.edu
The Software Defined Networking (SDN) is a vital technology which includes decoupling the
control and data planes in the network. The advantages of the separation of the control and …

[PDF][PDF] A novel ddos attack detection based on gaussian naive bayes

A Fadil, I Riadi, S Aji - Bulletin of Electrical Engineering and …, 2017 - researchgate.net
Abstract Distributed Denial of Service (DDoS) is a type of attack using the volume, intensity,
and more costs mitigation to increase in this era. Attackers used many zombie computers to …

Implementing Chaos Based Optimisations on Neural Networks for Predictions of Distributed Denial-of-Service (DDoS) Attacks

A Jha, A Goel, D Mahajan, G Jain - 2023 2nd International …, 2023 - ieeexplore.ieee.org
A Distributed Denial-of-Service attack (DDoS) involves overwhelming a network with a large
amount of traffic that aims to disrupt the normal functioning of a network. DDoS attacks can …

Predict And Prevent DDOS Attacks Using Machine Learning and Statistical Algorithms

A Golduzian - arXiv preprint arXiv:2308.15674, 2023 - arxiv.org
A malicious attempt to exhaust a victim's resources to cause it to crash or halt its services is
known as a distributed denial-of-service (DDoS) attack. DDOS attacks stop authorized users …

Formation of Network Attack Detection System Architecture

B Norbekova, A Komilova, G Arakulov… - … Conference on Inventive …, 2024 - Springer
This research work intends to design and develop an architectural framework for the network
attraction detection process. The key components of the proposed system architecture …