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Protecting Multi-Domain Command and Control Information System Network Traffic with Segment Routing 1

10th China Conference on Command and Control, C2 2022

作者： Yuan, Zongyang Chen, Tao Ren, Bangbang Luo, Lailong The Science and Technology on Information Systems Engineering Laboratory National University of Defence Technology Changsha China

ISBN: (数字)9789811960529

ISBN: (纸本)9789811960512

In the Multi-Domain Command and Control Information System (MDCCIS), data security and privacy protection play key roles in improving network security. However, some network devices may be illegally hijacked, which may result in data leakage. To solve this problem, Avoidance Routing (AR) which bypasses the untrusted network devices has been proposed to protect the traffic. However, existing methods have to deploy agent nodes in the network, which confines the selection of routing paths and degrades the flexibility of the network Recently, a new source routing technology, Segment Routing (SR), has been proposed to steer flows along any path as network operators expected. In this paper, we leverage a new mechanism of SR to find proper avoidance routing paths for traffic protection. We formulate this problem as an integer programming model and propose two algorithms to get a feasible solution with different security Service Level Agreement (SLA) constraints. Experimental results show that our C-SAR algorithm protects the traffic with a 95 % success rate in low-risk networks and the P-SAR algorithm can realize a 50 % success rate even in uncertain networks. © 2022, Chinese Institute of Command and Control.

关键词： Integer programming

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Stochastic Block Models for Complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

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Computer systems science & engineering 2023年第10期47卷 993-1021页

作者： Junfeng Sun Chenghai Li Yafei Song Peng Ni Jian Wang College of Air and Missile Defence Air Force Engineering UniversityXi’an710051China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing100076China

The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security *** solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the ***,a prediction model of TCAN-BiGRU is established respectively for each *** uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from *** learns the before-after status of situation data to extract more feature information from sequences for ***,IQPSO is proposed to optimize the hyperparameters of ***,the prediction results of the subsequence are superimposed to obtain the final predicted *** the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs *** the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.

关键词： Network security situation prediction SSA IQPSO TCAN-BiGRU

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A reconfigurable dynamic Bayesian network for digital twin modeling of structures with multiple damage modes

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Theoretical & Applied Mechanics Letters 2023年第4期13卷 251-260页

作者： Yumei Ye Qiang Yang Jingang Zhang Songhe Meng Jun Wang Xia Tang School of Mechanical Technology Wuxi Institute of TechnologyWuxi 214121China National Key Laboratory of Science and Technology for National Defence on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150001China Beijing Institute of Astronautical Systems Engineering Beijing 100076China

Dynamic Bayesian networks(DBNs)are commonly employed for structural digital twin *** present,most researches only consider single damage mode *** is not sufficient for a reusable spacecraft as various damage modes may occur during its service life.A reconfigurable DBN method is proposed in this *** structure of the DBN can be updated dynamically to describe the interactions between different *** common damages(fatigue and bolt loosening)for a spacecraft structure are considered in a numerical *** results show that the reconfigurable DBN can accurately predict the acceleration phenomenon of crack growth caused by bolt loosening while the DBN with time-invariant structure cannot,even with enough *** definition of interaction coefficients makes the reconfigurable DBN easy to track multiple damages and be extended to more complex *** method also has a good physical interpretability as the reconfiguration of DBN corresponds to a specific *** predictions do not require precise knowledge of reconfiguration conditions,making the method more practical.

关键词： Dynamic Bayesian network Reusable spacecraft Damage Reconfiguration

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Bayesian Inference of Mechanical Property Parameters and Layup Orientation Angle in a Composite Wing

Bayesian Inference of Mechanical Property Parameters and Lay...

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2023 International Annual Conference on Complex systems and Intelligent science, CSIS-IAC 2023

作者： Gong, Yulian Tang, Yuhua Zhang, Jianguo Beihang University School of Reliability and Systems Engineering Beijing China Technology and Industry for National Defence Prc Deep Space Exploration Laboratory State Administration of Science Beijing China

ISBN: (纸本)9798350309003

A Bayesian method is adopted to infer the mechanical property parameters and layup orientation angle of a composite wing in this paper. In order to make the calculation feasible, a basis-adaptive PC-Kriging model is proposed, which can replace the finite element model of the composite wing accurately. Considering that the simulation model of the composite wing is a high-dimensional model, AIES algorithm is used to model calibration of the composite wing, because it shows good performance in processing high-dimensional strongly correlated posterior distribution parameters. Finally, comparing the predicted parameters with the real parameters, the error is found to be small, which indicates that the proposed Bayesian method has good performance. © 2023 IEEE.

关键词： Kriging

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Stone Soup: No Longer Just an Appetiser 26

Stone Soup: No Longer Just an Appetiser

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26th International Conference on Information Fusion, FUSION 2023

作者： Hiscocks, Steven Barr, Jordi Perree, Nicola Wright, James Pritchett, Henry Rosoman, Oliver Harris, Michael Gorman, Roisín Pike, Sam Carniglia, Peter Vladimirov, Lyudmil Oakes, Benedict Defence Science and Technology Laboratory Cyber and Information Systems Salisbury United Kingdom Defence Research and Development Canada Radar Sensing and Exploitation Ottawa Canada University of Liverpool Electrical Engineering & Electronics Cdt in Distributed Algorithms Liverpool United Kingdom

ISBN: (纸本)9798890344854

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community *** Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable *** announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community. © 2023 International Society of Information Fusion.

关键词： State estimation

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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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EBD-YOLO：a lightweight auxiliary driving detection algorithm based on YOLOv5

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Optoelectronics Letters 2025年

作者： SUN Lianshuai SHI Tao DING Yao LI Song Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems， School of Electrical Engineer-ing and Automation， Tianjin University of Technology College of Electrical Engineering， North China University of Science and Technology

A lightweight road-assistant detection algorithm， EBD-YOLO， based on YOLOv5s is proposed to address the problems of high model complexity， computation cost， and difficulty in deployment on resource-limited embedded terminals in existing assisted driving detection algorithms. First， lightweight Transformer model EfficientViT was used as the backbone feature extraction network of YOLOv5s model to reduce network parameters and calculation costs. Secondly， a Focal-GIoU Loss function is proposed for bounding box regression to accelerate model convergence and reduce loss. Thirdly， the feature pyramid structure is improved to a weighted bi-directional feature pyramid network (BiFPN) to enhance localization and semantic features. Then， a dynamic head framework is added to unify the attention mechanism with the object detection head to improve its performance. Finally， a Soft-CIoU＿NMS algorithm is proposed in the post-processing stage to enhance occluded targets' localization and detection ability and reduce the missed detection rate. We conducted experiments on the KITTI and BDD100K datasets for autonomous driving， and the results showed that the EBD-YOLO model reduced in size by 38.4% and 37.2%， respectively. In comparison， the computational cost was reduced by 48.1%. As measured by mAP@0.5， the detection accuracy improved by 0.5% and 5.8%， respectively， and mAP@0.5:0.95 improved by 2.8% and 7%， respectively. These improvements satisfied the requirements for deployment on embedded terminals in cars.

关键词：

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SWiFT Model Development and Testing in the ARA Transonic Wind Tunnel

SWiFT Model Development and Testing in the ARA Transonic Win...

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AIAA science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Coppin, Joe Martin, Peter Bdeiwi, Houda Preece, Adam Aerospace Systems Group Dstl Defence Science and Technology Laboratory Hampshire FarehamPO17 6AD United Kingdom Aerodynamics Department Aircraft Research Association Limited Manton Lane BedfordMK417PF United Kingdom

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

A new research wind tunnel model, known as SWiFT, has been designed, fabricated and tested in the Aircraft Research Association (ARA) Transonic Wind Tunnel as part of a larger programme of work under Research Task Group 298 of the Applied Vehicle technology Panel, NATO science and technology Organisation. The model was designed for compatibility with a number of high speed wind tunnels, including theNational Transonic Facility atNASALangley, as well as for testing at cryogenic temperatures which would enable high Reynolds numbers to be achieved. Mechanical design of the wind tunnel model is presented, including analysis which contributed to key model design decisions. Details of the ARA test are presented, including a description of the test matrix, transition fixing, flow visulisation techniques and a summary and analysis of key results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Implementation of Non-Gaussian Motion Models Within Stone Soup

Implementation of Non-Gaussian Motion Models Within Stone So...

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International Conference on Information Fusion

作者： Zhen Yuen Chong Henry Pritchett Qing Li Runze Gan Yaman Kındap Simon Godsill Department of Engineering University of Cambridge Cambridge UK Cyber and Information Systems Defence Science and Technology Laboratory Salisbury UK

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In recent years, state-space models for highly manoeuvrable objects have been proposed based on non-Gaussian, continuous time, jump-based Lévy processes, the so-called Lévy state-space model [1]–[4]. In these models, the standard Brownian motion driving process for continuous time processes is replaced with a heavy-tailed non-Gaussian alternative. This retains all the flexibility of its Gaussian counterpart in terms of possible dynamical model structures and operations with irregular time stamps or heterogeneous data sources. These models aim to operate in areas such as surveillance of irregularly moving drones or people, and tracking wildlife or biological data. Implementation is relatively straightforward since the Kalman filters of the Brownian motion case can be replaced in the nonGaussian case by mixtures of Kalman filters within a marginalised particle filtering framework [5]. While the Stone Soup tracking software environment includes both Kalman filtering and generic particle filtering, it does not currently allow the combination of these tasks within a marginalised particle filtering framework. We discuss the significant challenges involved in incorporating these models and algorithms into Stone Soup, and present initial simulation results for the new software.

关键词： Tracking Biological system modeling Surveillance Wildlife Filtering algorithms Information filters Mathematical models Data models State-space methods Kalman filters

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