An intelligent recommendation algorithm for red team strategy in edge computing powered massive Cyber Defense Exercise

2021 ◽  
Vol 165 ◽  
pp. 141-148
Author(s):  
Moonsu Jang ◽  
Donghyun Kim ◽  
Daehee Seo ◽  
Yongmin Ju ◽  
Seungho Ryu ◽  
...  
Keyword(s):  
Edge Computing ◽  
Cyber Defense ◽  
Recommendation Algorithm

An efficient cyber defense framework for UAV-Edge computing network

2019 ◽  
Vol 94 ◽  
pp. 101970 ◽  
Author(s):  
Hichem Sedjelmaci ◽  
Aymen Boudguiga ◽  
Inès Ben Jemaa ◽  
Sidi Mohammed Senouci
Keyword(s):  
Edge Computing ◽  
Cyber Defense

scholarly journals Points-of-Interest Recommendation Algorithm Based on LBSN in Edge Computing Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47973-47983
Author(s):  
Keyan Cao ◽  
Jingjing Guo ◽  
Gongjie Meng ◽  
Haoli Liu ◽  
Yefan Liu ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computing Environment ◽  
Recommendation Algorithm ◽  
Points Of Interest

Vigilance in cyber defense: A strategy and individual differences-based approach

2014 ◽  
Author(s):  
Ben D. Sawyer ◽  
Victor Finomore ◽  
Joel Warm ◽  
Peter A. Hancock
Keyword(s):  
Individual Differences ◽  
Cyber Defense

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

Dynamic Service Placement and Load Distribution in Edge Computing

2020 ◽  
Author(s):  
Adyson Magalhaes Maia ◽  
Yacine Ghamri-Doudane ◽  
Dario Vieira ◽  
Miguel Franklin de Castro
Keyword(s):  
Load Distribution ◽  
Edge Computing ◽  
Service Placement

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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