Recent Advances in Network Security

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 15 March 2027 | Viewed by 606

Editors


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Guest Editor
Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China
Interests: network security; applied cryptography; AI and data security

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Guest Editor
College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Interests: applied cryptography; network security; privacy-preserving computing; secure multiparty computation

Special Issue Information

Dear Colleagues,

This Special Issue, "Recent Advances in Network Security," presents cutting-edge research addressing the dynamic and escalating challenges of securing modern networks. The collection of papers highlights a paradigm shift from traditional, static defense mechanisms to more adaptive, intelligent, and programmable security frameworks.

Key themes explored include encrypted traffic analysis, where novel machine learning and statistical methods are employed to detect malicious activities without decrypting sensitive data, thus preserving privacy. Another significant focus is the programmable network data plane, which leverages technologies like P4 to enable line-rate, in-network security functions, allowing for the rapid deployment of custom defenses against emerging threats. Furthermore, this issue delves into AI-driven network security, showcasing how artificial intelligence and machine learning are revolutionizing threat intelligence, automating anomaly detection, and enabling autonomous response systems. Additional topics cover the implementation of zero-trust architecture, the application of blockchain for decentralized security services, the unique security considerations for 5G/6G and IoT ecosystems, secure Internet architecture and DDoS prevention. Collectively, these contributions provide a comprehensive overview of the innovative strategies and technologies shaping the future of network defense, offering invaluable insights for researchers and practitioners dedicated to creating a more secure and resilient digital infrastructure.

Dr. Zhuotao Liu
Dr. Lin Liu
Guest Editors

Manuscript Submission Information

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Keywords

  • traffic analysis
  • AI-driven network security
  • programmable network dataplane
  • secure Internet architecture
  • secure routing
  • DDoS prevention
  • website fingerprinting

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Published Papers (1 paper)

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Research

25 pages, 1772 KB  
Article
Performance Analysis of Sigmoid-Enhanced OSPF for Risk-Aware Adaptive Routing in Secure Networks
by Chakadkit Thaenchaikun and Komsan Kanjanasit
Network 2026, 6(3), 52; https://doi.org/10.3390/network6030052 - 10 Jul 2026
Abstract
Modern communication networks require routing protocols that can adapt to dynamic traffic conditions while accounting for topology-based structural risk. Conventional open shortest path first (OSPF) relies on static or linear link cost metrics, which are often inadequate for capturing the nonlinear behavior of [...] Read more.
Modern communication networks require routing protocols that can adapt to dynamic traffic conditions while accounting for topology-based structural risk. Conventional open shortest path first (OSPF) relies on static or linear link cost metrics, which are often inadequate for capturing the nonlinear behavior of network dynamics and structural risk. This paper proposes sigmoid-enhanced OSPF (SE-OSPF), which integrates topology-based structural risk into the OSPF routing metric through a nonlinear sigmoid function. The proposed framework employs two configurable sigmoid parameters, the midpoint (x0) and the steepness (k), to provide smooth cost transitions and adaptive routing decisions under varying network conditions. Simulation results on a Barabási–Albert scale-free topology demonstrate that SE-OSPF reduces the average end-to-end delay by 19.7% and packet jitter by 8.6% compared with Standard OSPF. In addition, SE-OSPF increases the average number of successfully delivered packets by up to 16.6% compared with Linear-OSPF while reducing maximum link utilization (MLU), indicating more balanced traffic distribution, improved load balancing, and reduced congestion. These results demonstrate that the proposed sigmoid-based routing metric effectively balances routing efficiency, packet delivery reliability, and network load distribution, establishing SE-OSPF as an effective framework for topology-based structural risk-aware adaptive routing in modern communication networks. Full article
(This article belongs to the Special Issue Recent Advances in Network Security)
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