Emerging Technologies for Network Security and Anomaly Detection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 574

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, 200 Chung-Pei Rd. Chung-Li District, Tao-Yuan 320314, Taiwan
Interests: software-defined networking; streaming data processing; network traffic analysis; FPGA system design; computer network security

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Guest Editor
Department of Electrical and Electronic Engineering, Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita-Shi, Osaka 564-8680, Japan
Interests: software-defined networking; computer network security; optical networking; network optimization; in-network caching; edge computing systems

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Guest Editor
Faculty of Science and Engineering, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-Shi, Kyoto 610-0321, Japan
Interests: computer network security; mobile networks; machine learning; UAV-assisted networks
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Special Issue Information

Dear Colleagues,

The increasing complexity of modern network infrastructures and the proliferation of cyber threats have created an urgent need for advanced security mechanisms and robust anomaly detection techniques. Traditional security solutions are often insufficient in detecting sophisticated cyberattacks, necessitating the development of innovative methodologies to ensure network resilience. This Special Issue aims to explore new technologies and approaches that enhance network security and anomaly detection, focusing on leveraging AI, machine learning, and emerging computational models.

We invite original research papers, review articles, and case studies contributing to the theoretical foundations, algorithmic advancements, and practical applications of network security and anomaly detection. Contributions that address real-world security challenges and propose novel frameworks are highly encouraged.

Topics of Interest

We welcome submissions on (but not limited to) the following topics:

  • AI and machine learning for intrusion detection and anomaly detection;
  • Blockchain and distributed ledger technologies for secure networking;
  • Zero-trust architectures and their applications in modern networks;
  • Next-generation intrusion prevention and detection systems;
  • Federated learning and privacy-preserving network security;
  • Threat intelligence and automated cyber threat response;
  • Secure network protocols and cryptographic techniques;
  • Deep learning approaches for network traffic analysis;
  • Adversarial attacks and defenses in network security;
  • Quantum computing and its implications for network security;
  • Real-time anomaly detection in cloud, edge, and IoT networks;
  • Software-defined networking (SDN) and network function virtualization (NFV) for security enhancement;
  • Large-scale datasets and benchmarks for network anomaly detection;
  • Case studies on security breaches and lessons learned.

Prof. Dr. Yu-Kuen Lai
Prof. Dr. Kouji Hirata
Dr. Tomotaka Kimura
Guest Editors

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Keywords

  • network security
  • anomaly detection
  • intrusion detection
  • machine learning
  • artificial intelligence (AI)
  • zero-trust architecture
  • threat intelligence
  • blockchain
  • software-defined networking (SDN)

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Published Papers (1 paper)

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Research

37 pages, 18679 KiB  
Article
Real-Time DDoS Detection in High-Speed Networks: A Deep Learning Approach with Multivariate Time Series
by Drixter V. Hernandez, Yu-Kuen Lai and Hargyo T. N. Ignatius
Electronics 2025, 14(13), 2673; https://doi.org/10.3390/electronics14132673 - 1 Jul 2025
Viewed by 361
Abstract
The exponential growth of Distributed Denial-of-Service (DDoS) attacks in high-speed networks presents significant real-time detection and mitigation challenges. The existing detection frameworks are categorized into flow-based and packet-based detection approaches. Flow-based approaches usually suffer from high latency and controller overhead in high-volume traffic. [...] Read more.
The exponential growth of Distributed Denial-of-Service (DDoS) attacks in high-speed networks presents significant real-time detection and mitigation challenges. The existing detection frameworks are categorized into flow-based and packet-based detection approaches. Flow-based approaches usually suffer from high latency and controller overhead in high-volume traffic. In contrast, packet-based approaches are prone to high false-positive rates and limited attack classification, resulting in delayed mitigation responses. To address these limitations, we propose a real-time DDoS detection architecture that combines hardware-accelerated statistical preprocessing with GPU-accelerated deep learning models. The raw packet header information is transformed into multivariate time series data to enable classification of complex traffic patterns using Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and Transformer architectures. We evaluated the proposed system using experiments conducted under low to high-volume background traffic to validate each model’s robustness and adaptability in a real-time network environment. The experiments are conducted across different time window lengths to determine the trade-offs between detection accuracy and latency. The results show that larger observation windows improve detection accuracy using TCN and LSTM models and consistently outperform the Transformer in high-volume scenarios. Regarding model latency, TCN and Transformer exhibit constant latency across all window sizes. We also used SHAP (Shapley Additive exPlanations) analysis to identify the most discriminative traffic features, enhancing model interpretability and supporting feature selection for computational efficiency. Among the experimented models, TCN achieves the most balance between detection performance and latency, making it an applicable model for the proposed architecture. These findings validate the feasibility of the proposed architecture and support its potential as a real-time DDoS detection application in a realistic high-speed network. Full article
(This article belongs to the Special Issue Emerging Technologies for Network Security and Anomaly Detection)
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