Deep learning (DL)-based adaptive transport layer control in UAV swarm networks

2020 ◽

pp. 175-188

Author(s):

V. Punitha ◽

C. Mala

Keyword(s):

Deep Learning ◽

Transport Layer ◽

Classification Model ◽

Learning Approach ◽

Ddos Attacks ◽

Application Layer ◽

Learning Models ◽

Technological Transformation ◽

Application Deployment ◽

Machine Learning Models

The recent technological transformation in application deployment, with the enriched availability of applications, induces the attackers to shift the target of the attack to the services provided by the application layer. Application layer DoS or DDoS attacks are launched only after establishing the connection to the server. They are stealthier than network or transport layer attacks. The existing defence mechanisms are unproductive in detecting application layer DoS or DDoS attacks. Hence, this chapter proposes a novel deep learning classification model using an autoencoder to detect application layer DDoS attacks by measuring the deviations in the incoming network traffic. The experimental results show that the proposed deep autoencoder model detects application layer attacks in HTTP traffic more proficiently than existing machine learning models.

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Transport-Layer Control to Increase Throughput in Bufferless Optical Packet-Switching Networks

Journal of Optical Communications and Networking ◽

10.1364/jocn.8.000947 ◽

2016 ◽

Vol 8 (12) ◽

pp. 947 ◽

Cited By ~ 8

Author(s):

Pablo Jesus Argibay-Losada ◽

Gokhan Sahin ◽

Kseniia Nozhnina ◽

Chunming Qiao

Keyword(s):

Packet Switching ◽

Transport Layer ◽

Optical Packet Switching ◽

Switching Networks ◽

Optical Packet Switching Networks ◽

Packet Switching Networks ◽

Layer Control

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Research of Intelligent Building Security Automatic Monitoring Alarm System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1185 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1185-1188

Author(s):

Li Ma ◽

Li Ying Cao ◽

Yue Ling Zhao

Keyword(s):

Information Acquisition ◽

Automatic Monitoring ◽

Information Management System ◽

Transport Layer ◽

Alarm System ◽

Intelligent Building ◽

System Composition ◽

System Information ◽

Control Layer ◽

Layer Control

In this study by analyzing the types and characteristics of intelligent building, this paper expounds the intelligent building security automatic monitoring alarm system of layered structure: the presentation layer, control layer, processing layer, transport layer and execution layer, support layer, collected layer, describes the system composition: front-end information acquisition system, information transmission control system, information management system, automatic alarm system. The system can improve the efficiency of the intelligent building security.

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High Load Control Mechanism for SIP Servers

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v5i2.167 ◽

2016 ◽

Vol 5 (2) ◽

pp. 47-56

Author(s):

Ahmadreza Montazerolghaem ◽

Seyed-Amin Hosseini-Seno ◽

Mohammad Hossein Yaghmaee ◽

Rahmat Budiarto

Keyword(s):

Control Mechanism ◽

Instant Messaging ◽

Transport Layer ◽

Application Layer ◽

Transport Layer Protocol ◽

Sip Proxy ◽

And Performance ◽

Transmission Layer ◽

Layer Control ◽

Transport Layer Protocols

To start voice, image, instant messaging, and generally multimedia communication, session communication must begin between two participants. SIP (session initiation protocol) that is an application layer control induces management and terminates this kind of sessions. As far as the independence of SIP from transport layer protocols is concerned, SIP messages can be transferred on a variety of transport layer protocols including TCP or UDP. Mechanism of Retransmission that is embedded in SIP could compensate for the missing packet loss, in case of need. This mechanism is applied when SIP messages are transmitted on an unreliable transmission layer protocol like UDP. Also, while facing SIP proxy with overload, it could cause excessive filling of proxy queue, postpone increase of other contacts, and add to the amount of the proxy overload. In the present work, while using UDP as transport layer protocol, invite retransmission timer (T1) was appropriately regulated and SIP functionality was improved. Therefore, by proposing an adaptive timer of invite message retransmission, attempts were made to improve the time of session initiation and consequently improve the performance. Performance of the proposed SIP was implemented and evaluated by SIPP software in a real network environment and its accuracy and performance were demonstrated.

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Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Electronics ◽

10.3390/electronics9071120 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1120 ◽

Cited By ~ 1

Author(s):

Chao Liang ◽

Bharanidharan Shanmugam ◽

Sami Azam ◽

Asif Karim ◽

Ashraful Islam ◽

...

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Resource Constraints ◽

Detection System ◽

Learning Algorithms ◽

Transport Layer ◽

Multi Agent Systems ◽

Multi Agent

With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

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.

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