A Scheme for Edge-based Adaptive Tetrahedron Subdivision

1998 ◽  
pp. 61-70 ◽  
Author(s):  
Detlef Ruprecht ◽  
Heinrich Müller
Keyword(s):  
Edge Based

scholarly journals Test Architecture for Systolic Array of Edge-based AI Accelerator

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Umair Saeed Solangi ◽  
Muhammad Ibtesam ◽  
Muhammad Adil Ansari ◽  
Jinuk Kim ◽  
Sungju Park
Keyword(s):  
Systolic Array ◽  
Edge Based ◽  
Test Architecture

scholarly journals Enabling Internet of Media Things With Edge-Based Virtual Multimedia Sensors

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 59255-59269
Author(s):  
Anselmo Luiz Eden Battisti ◽  
Debora Christina Muchaluat-Saade ◽  
Flavia C. Delicato
Keyword(s):  
Edge Based

scholarly journals Edge-based formulation of elastic network models

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Maxwell Hodges ◽  
Sophia N. Yaliraki ◽  
Mauricio Barahona
Keyword(s):  
Network Models ◽  
Elastic Network ◽  
Elastic Network Models ◽  
Edge Based

scholarly journals Zagreb Indices of Edge Based on IB Product

2021 ◽  
Vol 1724 (1) ◽  
pp. 012036
Author(s):  
K. Pattabiraman ◽  
T. Suganya
Keyword(s):  
Zagreb Indices ◽  
Edge Based

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

Edge-Based Communication Optimization for Distributed Federated Learning

2021 ◽  
pp. 1-1
Author(s):  
Tian Wang ◽  
Yan Liu ◽  
Xi Zheng ◽  
Hong-Ning Dai ◽  
Weijia Jia ◽  
...  
Keyword(s):  
Communication Optimization ◽  
Edge Based
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