Detection of distributed denial of service using deep learning neural network

2020 ◽  
Author(s):  
S. Sumathi ◽  
N. Karthikeyan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Deep Learning Neural Network

scholarly journals Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

2021 ◽  
Vol 11 (2) ◽  
pp. 1498
Author(s):  
Arnold Adimabua Ojugo ◽  
Rume Elizabeth Yoro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intelligent Systems ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Data Traffic ◽  
Denial Of Service Attack ◽  
Network Intrusion ◽  
Deep Learning Neural Network

Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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