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crossrefFuture Internet2023-07-26Cited by 17

Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN

Jiacheng Hou, Tianhao Tao, Haoye Lu, Amiya Nayak

Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking–ICN (SDN-ICN). We propose a statistical model that generates users’ content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network–double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.

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crossrefFuture Internet2023-06-14Cited by 9

Task-Aware Meta Learning-Based Siamese Neural Network for Classifying Control Flow Obfuscated Malware

Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters, Seyit Camtepe

Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated mal…

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crossrefFuture Internet2025-12-03

Graph-SENet: An Unsupervised Learning-Based Graph Neural Network for Skeleton Extraction from Point Cloud

Jie Li, Wei Guo, Wenli Zhang

Extracting 3D skeletons from point clouds is a challenging task in computer vision. Most existing deep learning methods rely heavily on supervised data requiring extensive manual annotation. Consequently, re-labeling is often necessary for cross-category applications, while the p…

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crossrefFuture Internet2024-01-16Cited by 14

Service Function Chain Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

Xu Feng, Mengyang He, Lei Zhuang, Yanrui Song, Rumeng Peng

SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose s…

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crossrefFuture Internet2025-03-13Cited by 3

Deep Neural Network-Based Modeling of Multimodal Human–Computer Interaction in Aircraft Cockpits

Li Wang, Heming Zhang, Changyuan Wang

Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. To address the limitations of single-modal interaction methods, a multimodal interaction model based on gaze and EEG target selection is proposed using deep learnin…

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crossrefFuture Internet2021-04-28Cited by 260

Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks

Abdulsalam O. Alzahrani, Mohammed J. F. Alenazi

Software-defined Networking (SDN) has recently developed and been put forward as a promising and encouraging solution for future internet architecture. Managed, the centralized and controlled network has become more flexible and visible using SDN. On the other hand, these advanta…

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crossrefFuture Internet2025-05-22Cited by 11

A Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks

Ali Abdi Seyedkolaei, Fatemeh Mahmoudi, José García

The rapid expansion of the Internet of Things (IoT) and industrial Internet of Things (IIoT) ecosystems has introduced new security challenges, particularly the need for robust intrusion detection systems (IDSs) capable of adapting to increasingly sophisticated cyberattacks. In t…

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