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A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

Sheng Hong, Tianyu Yue, Yang You, Zhengnan Lv, Xu Tang, Jing Hu, Hongwei Yin

arXiv:2609.27903v1cs.AI

TL;DR

Traffic networks face serious security risks because attacks can propagate failures through interconnected nodes. The paper introduces risk into failure modeling, models and forecasts network resilience, and proposes forecast-based recovery; its experiments report that node risk lengthens failure propagation while reducing peak failed nodes.

  • Problem

    Attacks can propagate failures through interconnected traffic-network nodes, creating serious security risks and motivating study of failure propagation and resilience.

    Source: §1 · p. 2

  • Method

    The paper introduces risk into an SIRD failure-propagation model, models network resilience, and uses LSTM forecasting to inform a resilience recovery strategy.

    Source: §3 · p. 3, §4.2 · p. 7, §5 · p. 14

  • Results

    Introducing node risk lengthened failure propagation and lowered peak failed nodes, while networks with higher-risk nodes ended with fewer recovered nodes.

    Source: §4 · p. 6

  • Takeaways & Limitations

    Higher protection rates reduce failure probability and time cost, but cannot restore failed nodes to the initial 100% resilience state.

    Source: §4.2 · p. 9

    The SIRD model assumes uniform propagation, recovery, and crash rates, although real traffic-network nodes vary in these rates.

    Source: §3 · p. 3

Abstract

from arXiv · show

Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviation, aerospace, vehicles, ships, electric power, and industry. However, because of the complexity and diversity of its structure, the complex traffic network is vulnerable to being attacked and faces serious security challenges. Therefore, this paper innovatively proposes a traffic network resilience recovery method based on resilience trend forecasting. In this paper, the risk value is introduced into the analysis of the network fault propagation process, and the Susceptible, Infectious, Recovered, Dead-Risk (SIRD-R) fault propagation model is established. The resilience model of traffic network, which encompasses real-time resilience and overall resilience, is constructed through the integration of network resilience bearing capacity and resilience recovery capacity. Ten, the resilience of complex traffic networks is forecasted by using long short-term memory networks, and the resilience recovery strategy of complex traffic networks based on forecasting is proposed. Finally, the effectiveness and scalability of the proposed method are demonstrated through experimental analysis conducted on a diverse range of complex traffic networks, affirming its applicability in real-world scenarios

1. Introduction

Complex traffic networks are critical infrastructure but face serious security risks from attacks that can spread failures across interconnected nodes. The paper motivates studying this propagation and improving network resilience.

1. Introduction

  • Traffic networks support critical infrastructure across aviation, aerospace, ships, vehicles, electric power, and industry, making their security consequential.The paper describes these networks as vital to national security and development, and notes that they have been targeted by state-level hackers.Source: §1 · p. 1
  • The paper identifies inadequate security prevention and the resulting risks to human life and the social economy as major traffic-network concerns.It calls for studying post-attack failure propagation and developing resilience-enhancement methods.Source: §1 · p. 2
  • Attacks can spread node failures through network interconnections and ultimately cause network-wide collapse.The introduction cites cyberattack examples affecting national traffic networks and other industrial facilities.Source: §1 · pp. 1–2

2. Related Works

Prior work has developed resilience measures and recovery strategies, but the paper characterizes existing measurement and recovery research as incomplete and insufficiently comprehensive. It responds with resilience modeling, forecasting, and recovery research for traffic networks.

2. Related Works

  • Network resilience is described as sustaining an acceptable service level during an attack and responding to degradation before recovering normal operation.The paper notes that the concept has been applied to traffic-network security research.Source: §2 · p. 2
  • Prior measurement approaches use recovery-time performance, security performance, component resilience, topology, load changes, attack propagation, or robustness, adaptability, and recoverability.The cited studies use varied data and modeling perspectives to quantify network resilience.Source: §2 · p. 2
  • Earlier resilience recovery research addresses prevention and restoration through load redistribution, node reconstruction, monitoring, repair, and recovery algorithms.The paper reviews strategies including randomized failed-node maintenance and recovery schemes that consider resilience distance.Source: §2 · p. 2
  • The paper identifies existing traffic-network resilience measures as incomplete and recovery strategies as insufficiently comprehensive, then proposes modeling, forecasting, and forecast-based recovery.Its stated work establishes a traffic-network SIRD-R failure propagation model and forecasts resilience trends.Source: §2 · p. 2

3. Resilience Modeling and Optimization Method of Traffic Networks

The method models cascading node failures with a risk-adjusted SIRD-R process and measures resilience in real time and over the full recovery period. It forecasts resilience trends with LSTM and uses forecasts to guide node protection and recovery.

3. Resilience Modeling and Optimization Method of Traffic Networks

  • Attacks can trigger cascading failures; SIRD-R models susceptible, infectious, recovered, and collapsed nodes, with node risk representing heterogeneity in propagation, recovery, and collapse rates.Lower-risk nodes have a lower propagation rate, higher recovery rate, and lower collapse rate.Source: §3 · p. 3

3.2. Trafc Network Resilience Measurement

  • Real-time resilience is the network’s performance under attack relative to its normal performance at the same time.The performance function incorporates node weights and available capability under failure propagation.Source: §3.2 · pp. 4–5

3.2. Overall Resilience. The overall resilience of

  • Overall resilience combines the network’s minimum real-time resilience as bearing capacity with its resilience at the simulation cutoff as recovery capacity.The overall measure is based on the area under the resilience curve over the full period.Source: §3.2 · p. 5

3.3. Traffic Network Resilience Forecasting. The

  • An LSTM model forecasts the next resilience observation after resilience data are preprocessed and divided into training and test sets.The paper motivates LSTM for time-structured data by its gating mechanism for handling long-term dependence.Source: §3.3 · p. 6

3.4.1. Node Protection Based on Forecasting.

  • Forecast-guided recovery has two methods: protect unfailed nodes to lower their risk and adjust failure rates, and restore failed nodes to normal operation.The stated rate changes for protected nodes are reduced propagation, increased recovery, and reduced collapse rates.Source: §3.3 · p. 6, §3.4.1 · p. 6

4. Experimental Results and Analysis

Experiments report resilience improvements from forecasting-based optimization across tested network types and scales. They also examine risk-aware failure propagation, resilience forecasting, and protection and recovery strategies.

4. Experimental Results and Analysis

  • Adding node risk values lengthens failure propagation; peak failed-node counts and final recovered-node counts are lower than in the SIRD comparison.In the SIRD results, failed-node counts rise rapidly, then decline until no failed nodes remain in the stable state.Source: §4 · p. 6, Figure 9 · p. 7, Figure 10 · p. 7
  • Real-time resilience falls rapidly early in an attack, then begins to recover, but does not return to its pre-final-stability level when some nodes collapse.The passage attributes the initial decline to rapid failure spread and the later recovery to nodes recovering through security protections.Source: §4 · p. 6, Figure 11 · p. 8
  • Longer simulation cutoffs improve recovery capacity and overall resilience, while short simulations cannot represent the full failure-propagation process.The reported cutoff comparison holds bearing capacity unchanged at 0.4014.Source: §4 · pp. 6–7, Table 2 · p. 8

4.2. Experimental Analysis of Network Resilience

  • MRE 0.00639, MSE 0.00016, and R2 0.96842; LSTM forecasts most closely match actual resilience and have the smallest error versus CNN and RNN.The comparison covers traffic-network resilience forecasting.Source: §4.2 · p. 8, Table 3 · p. 9, Table 4 · p. 9
  • Under different conditions, resilience forecasts are very close to actual results, with very small errors and R2 coefficients close to 1.The reported condition-specific results are presented in Table 5 and Figure 14.Source: §4.2 · p. 9, Table 5 · p. 10, Figure 14 · p. 10
  • Forecast-based protection improves bearing, recovery, and overall resilience but cannot restore failed nodes; forecast-based recovery can restore initial resilience with sufficient time.Both strategies show further resilience improvements as their respective protection or recovery rates increase.Source: §4.2 · p. 9, Table 6 · p. 12, Table 7 · p. 12
  • Reducing forecast advance raises bearing and overall resilience with little change in recovery capacity, while combining node risk values improves resilience results.The reported comparisons are shown in Tables 8 and 9 and Figures 17 and 18.Source: §4.2 · p. 9, Figure 17 · p. 12, Table 8 · p. 13
  • Across aviation, industrial, power, and other networks, forecasting-based optimization improves resilience; from 20 to 8000 nodes, improvements remain effective with reasonable simulation-time increases.The method combines node protection and node recovery strategies.Source: §4.2 · pp. 9, 13, 14, Table 10 · p. 13, Table 11 · p. 14

5. Conclusion

The paper addresses gaps in complex traffic-network resilience modeling, forecasting, and recovery. It introduces risk-aware failure propagation and resilience models, forecasts resilience with LSTM networks, and proposes a forecasting-based recovery strategy.

5. Conclusion

  • The paper identifies incomplete resilience modeling, forecasting, and recovery as the focus of its research.It aims to support earlier perception and warning and proactive resilience optimization.Source: §5 · p. 14
  • The SIRD-R failure propagation model introduces risk value into analysis of traffic-network node failures after an attack.Source: §5 · p. 14
  • The traffic-network resilience model integrates resilience bearing capacity and recovery capacity across real-time and overall resilience.Source: §5 · p. 14
  • The paper forecasts traffic-network resilience with LSTM networks and proposes a recovery strategy based on those forecasts.The stated aim is to disrupt fault diffusion and optimize resilience in advance.Source: §5 · p. 14

Funding

The work reports support from the Beijing Natural Science Foundation, the National Key Research and Development Program, and additional research and university funds.

Funding

  • The work lists support from the Beijing Natural Science Foundation (L222005).Source: Funding · p. 14
  • The work lists support from the National Key Research and Development Program (2022YFB3103602).Source: Funding · p. 14
  • Other listed support includes CCF-NSFOCUS Kun-Peng, Industrial Internet Innovation and Development, and Fundamental Research Funds for the Central Universities.The listed identifiers are CCF-NSFOCUS202215, TC210804R, and CEIEC-2022-ZM02-0252.Source: Funding · p. 14
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