On neutrality of future networks: SDN as a baseline

Author(s):  
Khaoula Bel Kamel ◽  
Hana Bouafif ◽  
Mohamed Karim Sbai
Keyword(s):  
2019 ◽  
Vol 16 (7) ◽  
pp. 207-228
Author(s):  
Wanwei Huang ◽  
Chunfeng Du ◽  
Jianwei Zhang ◽  
Changhai Wang
Keyword(s):  

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1107
Author(s):  
Slawomir Nowaczewski ◽  
Wojciech Mazurczyk

Customer Edge Switching (CES) is an extension of the already known classical firewall that is often described and used in future networks like 5G. It extends its functionality by enabling information exchange with other firewalls to decide whether the inspected network traffic should be considered malicious or legitimate. In this paper, we show how the Passive DNS can be used to further improve security of this solution. First, we discuss CES solution and its internals. We also determine how it uses DNS and CETP protocols. Secondly, we describe the basics of the Passive DNS and how it impacts the DNS protocol. Thirdly, we evaluate how the Passive DNS can be extended to collect also CETP information. Finally, we integrate the solutions and present obtained experimental results.


2021 ◽  
pp. 101337
Author(s):  
Sara Imran Khan ◽  
Zakria Qadir ◽  
Hafiz Suliman Munawar ◽  
Soumya Ranjan Nayak ◽  
Anil Kumar Budati ◽  
...  

2021 ◽  
Author(s):  
Prakash Ramachandran ◽  
Sunku Ranganath ◽  
Malini Bhandaru ◽  
Sujata Tibrewala

2018 ◽  
Vol 10 (9) ◽  
pp. 1339 ◽  
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Chunhui Liu ◽  
Yu Liu ◽  
Yufeng Wang ◽  
...  

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.


2008 ◽  
Vol 7 (11) ◽  
pp. 914 ◽  
Author(s):  
R. Nejabati ◽  
G. Zervas ◽  
G. Zarris ◽  
Y. Qin ◽  
E. Escalona ◽  
...  

Author(s):  
Liang Zhao ◽  
Yasir Zaki ◽  
Asanga Udugama ◽  
Umar Toseef ◽  
Carmelita Gorg ◽  
...  
Keyword(s):  

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