Publications

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BFIS: Efficient Unknown Protocol Feature Extraction Method For Satellite Communication Systems

Published in APSIPA ASC 2025, 2025

With the rapid development of fields such as the Internet and satellite communications, the number of network protocol types has gradually increased, including numerous proprietary or unknown protocols. However, most research has focused on common network protocols, neglecting satellite communication protocols. Given the relatively simple features of captured bitstream data, extracting key bitstream sequences as protocol features is an effective method for satellite protocol feature extraction. Based on the analysis of satellite protocol structures, including DVB compliant with ETSI standards, this paper proposes a feature extraction method utilizing the BFIS algorithm and frequent sequence splicing via the FSS algorithm. This approach dynamically stores subsequence frequencies in a feature-analysis matrix, calculates similarities between different modes, and is employed to extract features of unknown satellite protocols based on DVB. The clustering results are then mapped to actual protocols. Experimental results on both simulated satellite protocol data and the ISCX VPN-nonVPN dataset show that the BFIS algorithm significantly improves accuracy, achieving 97.62% accuracy on DVB datasets and 95.16% accuracy on ISCX VPN-nonVPN dataset, demonstrating its effectiveness in extracting satellite protocol features.

Recommended citation: Xianwen Ling, Kun Zhang∗ and Rong Tong, Dianying Chen http://lingxianwen.github.io/files/paper4.pdf

Robustness of interdependent directed higher-order networks against cascading failures

Published in Physica D: Nonlinear Phenomena, 2024

To understand the robustness of interdependent directed higherorder networks, we propose a new theoretical framework to model and analyze the robustness of such networks under random failures by percolation theory. We find that adding higher-order edges makes the network more vulnerable which quantifies and compares by two criteria: the size of the giant connected components and the percolation threshold. Increasing the hyperdegree distribution of heterogeneity or the hyperedge cardinality distribution of heterogeneity in interdependent directed higher-order networks will also make the network more vulnerable. Interestingly, the phase transition type changes from continuous to discontinuous with the increase of coupling strength, and partially interdependent directed higher-order networks exist hybrid phase transition. Moreover, by applying our theoretical analysis to real interdependent directed higher-order networks further validated our conclusion, it has implications for the design of flexible complex systems.

Recommended citation: Dandan Zhao, Xianwen Ling, Hao Peng, Ming Zhong Jianmin Han, Wei Wang∗ http://lingxianwen.github.io/files/paper3.pdf

Robustness of directed higher-order networks

Published in Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023

We propose a theoretical percolation model to analyze the robustness of directed higher-order networks. We study the size of the giant connected components and the percolation threshold of our proposed model by the theory and Monte-Carlo simulations on artificial networks and real-world networks. We find that the percolation threshold is affected by the inherent properties of higher-order networks, including the heterogeneity of the hyperdegree distribution and the hyperedge cardinality, which represents the number of nodes in the hyperedge. Increasing the hyperdegree distribution of heterogeneity or the hyperedge cardinality distribution of heterogeneity in higher-order networks will make the network more vulnerable, weakening the higher-order network’s robustness. In other words, adding higher-order directed edges enhances the robustness of the systems. Our proposed theory can reasonably predict the simulations for percolation on artificial and real-world directed higher-order networks.

Recommended citation: Dandan Zhao; Xianwen Ling; Xiongtao Zhang; Hao Peng ; Ming Zhong ; Cheng Qian ; Wei Wang* http://lingxianwen.github.io/files/paper2.pdf

Disintegrate hypergraph networks by attacking hyperedge

Published in Journal of King Saud University - Computer and Information Sciences, 2022

According to study results, higher-order interactions are becoming more generally accepted as an essential element of complex systems. Hypergraphs may be used to explore the relationships between higher-order structures and functions in complex systems and capture higher-order interactions. After the initial failure of a hypergraph network, cascading failures may occur, just as they can with a simple network. Previous research has concentrated on random initial failures, and how hypergraph networks adapt to targeted attacks remains unanswered. In this research, we build a mathematical framework to explore the robustness of hypergraph networks against targeted attacks based on the magnitude of the hyperedge’s cardinality. We discovered that when the probability of large cardinality hyperedges being deleted grows, the network becomes more fragile.

Recommended citation: Hao Peng, Cheng Qian, Dandan Zhao, Ming Zhong, Xianwen Ling, Wei Wang* http://lingxianwen.github.io/files/paper1.pdf