- 标题
- 摘要
- 关键词
- 实验方案
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Optimal attack-aware RWA for scheduled lightpath demands
摘要： In Transparent optical networks (TONs), the data signals remain in the optical domain for the entire transmission path, creating a virtual topology over the physical connections of optical fibers. Due to the increasingly high data rates and the vulnerabilities related to the transparency of optical networks, TONs are susceptible to different physical layer attacks, including high-power jamming attacks. Developing strategies to handle such attacks and mitigating their impact on network performance is becoming an important design problem for TONs. Some approaches for handling physical layer attacks for static and dynamic traffic in TONs have been presented in recent years. In this work, we propose an integer linear program (ILP) formulation to control the propagation of such attacks in TONs for scheduled lightpath demands, which need periodic bandwidth usage at certain predefined times. We consider both the fixed window model, where the start and end timings of the demand are known in advance, and the sliding window model, where exact start and end times are unknown but fall within a larger window. We consider a number of potential objectives for attack-aware RWA and show how the flexibility to schedule demands in time can impact these objectives, compared to both attack-unaware and fixed window models.
关键词： Assignment (RWA),Scheduled lightpath demands (SLDs),Routing and wavelength,High-power jamming attacks,Integer linear program (ILP),Transparent optical networks (TONs)
更新于2025-09-19 17:13:59
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[IEEE 2017 International Conference on Optical Network Design and Modeling (ONDM) - Budapest (2017.5.15-2017.5.18)] 2017 International Conference on Optical Network Design and Modeling (ONDM) - A probabilistic approach for failure localization
摘要： This work considers the problem of fault localization in transparent optical networks. The aim is to localize single-link failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian Process (GP) classi?er is trained on historical data extracted from the examined network, with the goal of modeling and predicting the failure probability of each link therein. To limit the set of suspect links for every failure incident, the proposed approach is complemented with the utilization of a Graph-Based Correlation heuristic. The proposed approach is tested on a dataset generated for an OFDM-based optical network, demonstrating that it achieves a high localization accuracy. The proposed scheme can be used by service providers for reducing the Mean-Time-To-Repair of the failure.
关键词： Graph-Based Correlation,OFDM,Gaussian Process,transparent optical networks,fault localization
更新于2025-09-04 15:30:14