AI in Network Security: New Opportunities and Threats

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 567

Special Issue Editors


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Guest Editor
Department of Computer Science and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK
Interests: cyber security; digital forensics; Internet of Things; network security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: IoT security; device security; AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to explore the multifaceted relationship between artificial intelligence (AI) and network security. It intends to dissect how AI can be both a powerful tool for enhancing network security and a potential source of new threats within the digital realm.

The scope encompasses a wide range of aspects related to AI and network security. It includes looking at the opportunities AI offers, such as advanced threat detection through machine learning algorithms that can analyze vast amounts of network traffic data in real time. AI-powered intrusion detection systems can quickly identify and respond to malicious activities. However, it also focuses on the threats. For example, AI-based attacks where malicious actors can use AI to develop more sophisticated and evasive hacking techniques.

This Special Issue will focus on (but is not limited to) the following topics:

  • AI-driven security analytics for early threat identification.
  • The use of AI in authentication mechanisms to prevent unauthorized access.
  • How adversarial AI can be used to test and improve network security defense.
  • The implications of AI-enabled malware and how to counter it.
  • Ethical considerations in using AI for network security, such as avoiding false positives and protecting user privacy.

Dr. Shancang Li
Dr. Qindong Sun
Guest Editors

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Keywords

  • AI
  • security analytics
  • network security
  • network security defense

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Published Papers (1 paper)

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Research

18 pages, 2163 KB  
Article
Transmission Opportunity and Throughput Prediction for WLAN Access Points via Multi-Dimensional Feature Modeling
by Wei Li, Xin Huang, Danju Lv, Yueyun Yu, Yan Zhang, Zhicheng Zhu and Ting Zhou
Electronics 2025, 14(15), 2941; https://doi.org/10.3390/electronics14152941 - 23 Jul 2025
Viewed by 333
Abstract
With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, [...] Read more.
With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, and dynamic channel fluctuations complicate throughput prediction. To address this, we propose a method combining the Snow-Melting Optimizer (SMO) with decision tree regression models to optimize feature selection and model transmission opportunities (TXOP) and AP throughput. Experimental results show that the Extreme Gradient Boosting (XGBoost) model performs best, achieving high prediction accuracy for TXOP (MSE = 1.3746, R2 = 0.9842) and AP throughput (MAE = 2.5071, R2 = 0.9896). This approach effectively captures the nonlinear relationships between throughput and network factors in dense WLAN scenarios, demonstrating its potential for real-world applications. Full article
(This article belongs to the Special Issue AI in Network Security: New Opportunities and Threats)
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