基于趋势预测的复杂交通网络安全韧性恢复方法
A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting
- Beihang University（北京航空航天大学）
- NSFOCUS Technologies Group Co., Ltd.（绿盟科技集团股份有限公司）
- China Electronics Technology Taiji Group Corporation Limited（中国电子科技太极集团有限公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对复杂交通网络易受攻击的安全挑战，提出基于韧性趋势预测的恢复方法，通过SIRD-R故障传播模型和LSTM预测韧性，实验验证了方法的有效性与可扩展性。
中文摘要 AI 辅助
由于信息技术的快速发展，在航空、航天、车辆、船舶、电力和工业等各个领域已经建立了一个庞大而复杂的交通网络。然而，由于其结构的复杂性和多样性，复杂交通网络容易受到攻击，面临严峻的安全挑战。因此，本文创新性地提出了一种基于韧性趋势预测的交通网络韧性恢复方法。本文将风险值引入网络故障传播过程的分析中，建立了易感、感染、恢复、死亡-风险（SIRD-R）故障传播模型。通过整合网络韧性承载能力和韧性恢复能力，构建了包含实时韧性和整体韧性的交通网络韧性模型。然后，利用长短期记忆网络对复杂交通网络的韧性进行预测，并提出了基于预测的复杂交通网络韧性恢复策略。最后，通过在多种复杂交通网络上进行的实验分析，证明了所提方法的有效性和可扩展性，确认了其在实际场景中的适用性。
英文摘要
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
