IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment

2020 ◽  
Author(s):  
Chia-Wei Tseng ◽  
Li-Fan Wu ◽  
Shih-Chun Hsu ◽  
Sheng-Wang Yu
Keyword(s):  
Machine Learning ◽  
Dos Attacks

Machine Learning in an SDN Network Environment for DoS Attacks

2020 ◽  
pp. 231-243
Author(s):  
Mauricio Dominguez-Limaico ◽  
Edgar Maya-Olalla ◽  
Carlos Bosmediano-Cardenas ◽  
Charles Escobar-Teran ◽  
Juan Francisco Chafla-Altamirano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Environment ◽  
Dos Attacks

scholarly journals Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 279 ◽  
Author(s):  
Bambang Susilo ◽  
Riri Fitri Sari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Strategies ◽  
Learning Algorithm ◽  
Human Life ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Deep Learning Algorithm ◽  
Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

scholarly journals Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Francisco Sales de Lima Filho ◽  
Frederico A. F. Silveira ◽  
Agostinho de Medeiros Brito Junior ◽  
Genoveva Vargas-Solar ◽  
Luiz F. Silveira
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Service Providers ◽  
Detection System ◽  
Denial Of Service ◽  
Sampling Rate ◽  
Attack Detection ◽  
Dos Attacks ◽  
Internet Service ◽  
Benchmark Datasets

Users and Internet service providers (ISPs) are constantly affected by denial-of-service (DoS) attacks. This cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. This article presents a machine learning- (ML-) based DoS detection system. The proposed approach makes inferences based on signatures previously extracted from samples of network traffic. The experiments were performed using four modern benchmark datasets. The results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network traffic.

scholarly journals A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 51
Author(s):  
Swathi Sambangi ◽  
Lakshmeeswari Gondi
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Classification Problem ◽  
Attack Detection ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Multiple Sources ◽  
Denial Of Service Attack ◽  
Network Bandwidth

The problem of identifying Distributed Denial of Service (DDos) attacks is fundamentally a classification problem in machine learning. In relevance to Cloud Computing, the task of identification of DDoS attacks is a significantly challenging problem because of computational complexity that has to be addressed. Fundamentally, a Denial of Service (DoS) attack is an intentional attack attempted by attackers from single source which has an implicit intention of making an application unavailable to the target stakeholder. For this to be achieved, attackers usually stagger the network bandwidth, halting system resources, thus causing denial of access for legitimate users. Contrary to DoS attacks, in DDoS attacks, the attacker makes use of multiple sources to initiate an attack. DDoS attacks are most common at network, transportation, presentation and application layers of a seven-layer OSI model. In this paper, the research objective is to study the problem of DDoS attack detection in a Cloud environment by considering the most popular CICIDS 2017 benchmark dataset and applying multiple regression analysis for building a machine learning model to predict DDoS and Bot attacks through considering a Friday afternoon traffic logfile.

Machine Learning Based Detection and a Novel EC-BRTT Algorithm Based Prevention of DoS Attacks in Wireless Sensor Networks

2021 ◽  
Author(s):  
K. Lakshmi Narayanan ◽  
R. Santhana Krishnan ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
Vimal Shanmuganathan
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Dos Attacks

scholarly journals Bot Net Detection for Network Traffic using Ensemble Machine Learning Method

2020 ◽  
Vol 10 (1) ◽  
pp. 201-204
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Research Area ◽  
Network Activity ◽  
Machine Learning Techniques ◽  
Dos Attacks ◽  
Imbalance Problem ◽  
Ensemble Machine Learning ◽  
Private Data ◽  
Machine Learning Model

In todays era the need of security is raising due to hike in security risks discovered every day. A new vulnerability can be found in any software or product by the attacker as it launches in the market. Botnet carried out various attacks in distributed manner which results in extensive disruption of network activity through information and identity theft, email spamming, click fraud DDoS (Distributed Denial of Service) attacks, virtual deceit and distributed resource usage for cryptocurrency mining.The main aim f botnet is to steal private data of clients,sendind spam and viruses and DOS attacks in the network. The detection of Botnet like Rbot ,Virut and Neris are still vigorous research area due to unavailability of any technique to detect the entire ecosystem of botnet. As they are comprised of different configurations and profoundly armored by malwares writers to dodge detection systems by utilizing complicated dodging techniques. Hence only solution is to discover the infected botnets to control over the services and ports. This work aims to contribute in the botnet detection with its overview and existing methods. The study focuses on techniques like one-hot encoding and variance thresholding. These techniques are utilized to clean the botnet dataset. The performance of the machine learning model can be improved with feature selection methods. The work explores the dataset imbalance problem with the help of ensemble machine learning techniques. The performance is evaluated on the best received model that is trained and tested on datasets of various attacks.

Detection of DoS attacks using machine learning techniques

2020 ◽  
Vol 15 (3/4) ◽  
pp. 256
Author(s):  
Deepak Kumar ◽  
Vinay Kukreja ◽  
Virender Kadyan ◽  
Mohit Mittal
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Dos Attacks ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document