Advances in Computational Intelligence and Their Applications in Cybersecurity

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 516

Special Issue Editor

Faculty of Data Science, City University of Macau, Taipa, Macao
Interests: AI security; embodied AI; federated learning

Special Issue Information

Dear Colleagues,

In this Special Issue, we will focus on the integration of computational intelligence (CI) paradigms and advanced cybersecurity frameworks to strengthen digital defense mechanisms.

This Special Issue will highlight how bio-inspired algorithms, machine learning models, and evolutionary computation are applied to detect, predict, and mitigate complex cyber threats. A key aspect of this issue is the enhancement of system resilience and adaptive defense capabilities within the context of an increasingly interconnected and hostile digital landscape.

Specifically, in the realm of network security and data privacy, this issue will underscore the importance of intelligent real-time monitoring to identify zero-day attacks and secure Internet of Things (IoT) environments. It will showcase innovative strategies like deep learning, swarm intelligence, and fuzzy systems, tailored to handle the high dimensionality and dynamic nature of cyber data.

In summary, this Special Issue will emphasize the critical role of intelligent, adaptive computing techniques in evolving the next generation of robust and autonomous cybersecurity systems.

Dr. Wei Wan
Guest Editor

Manuscript Submission Information

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Keywords

  • computational intelligence
  • cybersecurity
  • intrusion detection systems
  • malware analysis
  • evolutionary computation
  • IoT security
  • adversarial attacks
  • network anomaly detection
  • bio-inspired computing

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Published Papers (1 paper)

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Research

22 pages, 389 KB  
Article
Adaptive Multipath Proofs for Privacy Protection and Security in Payment Channel Networks
by Wenqi Li, Zijie Pan and Yunqing Yang
Mathematics 2026, 14(7), 1199; https://doi.org/10.3390/math14071199 - 3 Apr 2026
Viewed by 309
Abstract
Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive [...] Read more.
Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks. Full article
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