scholarly journals Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN

2020 ◽  
Vol 111 ◽  
pp. 763-779 ◽  
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Ali Kashif Bashir ◽  
Rao Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ddos Detection ◽  
Adaptive Polling

scholarly journals A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

2021 ◽  
Vol 348 ◽  
pp. 01012
Author(s):  
Hailye Tekleselassie
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Knowledge Graph ◽  
Graph Classification ◽  
Deep Learning Algorithm ◽  
Ddos Detection ◽  
Basic Behavior ◽  
Graph System ◽  
Prevention Methods

This research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is proposed for DDoS detection. Whereas deep learning algorithm is used to develop a classifier model, knowledge-graph system makes the model expandable and flexible. It is analytically verified with CICIDS2017 dataset of 53.127 entire occurrences, by using ten-fold cross validation. Experimental outcome indicates that 99.97% performance is registered after connection. Fascinatingly, significant knowledge ironic learning for DDoS detection varies as a basic behavior of DDoS detection and prevention methods. So, security professionals are suggested to mix DDoS detection in their internet and network.

scholarly journals AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yuanyuan Wei ◽  
Julian Jang-Jaccard ◽  
Fariza Sabrina ◽  
Amardeep Singh ◽  
Wen Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Ddos Detection

scholarly journals Deep learning model for distributed denial of service (DDoS) detection

2022 ◽  
Vol 9 (2) ◽  
pp. 109-118
Author(s):  
Chaminda Tennakoon ◽  
◽  
Subha Fernando ◽  
Keyword(s):  
Deep Learning ◽  
Denial Of Service ◽  
False Negative ◽  
False Negative Rate ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Online Services ◽  
Ddos Detection ◽  
Osi Model

Distributed denial of service (DDoS) attacks is one of the serious threats in the domain of cybersecurity where it affects the availability of online services by disrupting access to its legitimate users. The consequences of such attacks could be millions of dollars in worth since all of the online services are relying on high availability. The magnitude of DDoS attacks is ever increasing as attackers are smart enough to innovate their attacking strategies to expose vulnerabilities in the intrusion detection models or mitigation mechanisms. The history of DDoS attacks reflects that network and transport layers of the OSI model were the initial target of the attackers, but the recent history from the cybersecurity domain proves that the attacking momentum has shifted toward the application layer of the OSI model which presents a high degree of difficulty distinguishing the attack and benign traffics that make the combat against application-layer DDoS attack a sophisticated task. Striding for high accuracy with high DDoS classification recall is key for any DDoS detection mechanism to keep the reliability and trustworthiness of such a system. In this paper, a deep learning approach for application-layer DDoS detection is proposed by using an autoencoder to perform the feature selection and Deep neural networks to perform the attack classification. A popular benchmark dataset CIC DoS 2017 is selected by extracting the most appealing features from the packet flows. The proposed model has achieved an accuracy of 99.83% with a detection rate of 99.84% while maintaining the false-negative rate of 0.17%, which has the heights accuracy rate among the literature reviewed so far.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Sign in / Sign up

Export Citation Format

Share Document