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 The Abnormal Detection for Network Traffic of Power IoT Based on Device Portrait

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiaxuan Fei ◽  
Qigui Yao ◽  
Mingliang Chen ◽  
Xiangqun Wang ◽  
Jie Fan
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Traffic Data ◽  
Network Environment ◽  
Security Measures ◽  
Privacy Leakage ◽  
Traffic Characteristics

The construction of power Internet of things is an important development direction for power grid enterprises. Although power Internet of things is a kind of network, it is denser than the ordinary Internet of things points and more complex equipment types, so it has higher requirements for network security protection. At the same time, due to the special information perception and transmission mode in the Internet of things, the information transmitted in the network is easy to be stolen and resold, and traditional security measures can no longer meet the security protection requirements of the new Internet of things devices. To solve the privacy leakage and security attack caused by the illegal intrusion in the network, this paper proposes to construct a device portrait for terminal devices in the power Internet of things and detect abnormal traffic in the network based on device portrait. By collecting traffic data in the network environment, various network traffic characteristics are extracted, and abnormal traffic is analyzed and identified by the machine learning algorithm. By collecting the traffic data in the network environment, the features are extracted from the physical layer, network layer, and application layer of the message, and the device portrait is generated by a machine learning algorithm. According to the established attack mode, the corresponding traffic characteristics are analyzed, and the detection of abnormal traffic is achieved by comparing the attack traffic characteristics with the device portrait. The experimental results show that the accuracy of this method is more than 90%.

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
Sign in / Sign up

Export Citation Format

Share Document