Hybrid Deep Learning Model for Real-Time Detection of Distributed Denial of Service Attacks in Software Defined Networks

2021 ◽  
pp. 1-13
Author(s):  
Auther Makuvaza ◽  
Dharm Singh Jat ◽  
Attlee M. Gamundani
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Denial Of Service ◽  
Learning Model ◽  
Denial Of Service Attacks ◽  
Software Defined Networks ◽  
Distributed Denial Of Service ◽  
Real Time Detection ◽  
Deep Learning Model

scholarly journals An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

2021 ◽  
Vol 10 (5) ◽  
pp. 2442-2453
Author(s):  
Isaac Odun- Ayo ◽  
Williams Toro- Abasi ◽  
Marion Adebiyi ◽  
Oladapo Alagbe
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Web Applications ◽  
Learning Model ◽  
Project Work ◽  
Multi Layer Perceptron ◽  
Real Time Detection ◽  
High Level ◽  
Deep Learning Model ◽  
Cross Site

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

2018 ◽  
Author(s):  
Tossaporn Santad ◽  
Piyarat Silapasupphakornwong ◽  
Worawat Choensawat ◽  
Kingkarn Sookhanaphibarn
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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