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- Etezadi, Ehsan, 1993, et al.
(författare)
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DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation
- 2022
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Ingår i: 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings. ; , s. 3694-3699
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Konferensbidrag (refereegranskat)abstract
- Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.
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| 2. |
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| 3. |
- Fan, Yuchuan, et al.
(författare)
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EVM Estimation for Performance Monitoring in Coherent Optical Systems : An Approach of Linear Regression
- 2022
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Ingår i: Optics InfoBase Conference Papers. - : Optica Publishing Group (formerly OSA). - 9781557528209
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Konferensbidrag (refereegranskat)abstract
- We experimentally demonstrate the effectiveness of a simple linear regression scheme for optical performance monitoring when applied after modulation format identification. It outperforms the FFNN-based benchmark scheme providing 0.2% mean absolute error for EVM estimation., © 2022 The Author(s)
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| 4. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
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Machine learning for network security management, attacks, and intrusions detection
- 2022
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Ingår i: Machine Learning for Future Fiber-Optic Communication Systems. ; , s. 317-336
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter focuses on challenges, progress and pitfalls in applying ML to physical-layer security management. In the context of trustworthy networks, we motivate the need for automation in support of the work of network security professionals. We summarize the characteristics of known attack techniques targeting the physical layer and outline the framework for optical network security management. Supervised, semisupervised and unsupervised learning techniques that can aid automation of network security management are described with a focus on their performance requirements in the context of security. Accuracy, complexity, and interpretability of these techniques are examined on a use case of jamming and polarization scrambling attacks performed experimentally in a telecom operator network testbed. Finally, several open research challenges in the context of optical network security are outlined along with possible avenues to tackle some of them.
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| 6. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Microservice-Based Unsupervised Anomaly Detection Loop for Optical Networks
- 2022
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Ingår i: 2022 Optical Fiber Communications Conference and Exhibition, OFC 2022 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network.
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| 7. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Root Cause Analysis for Autonomous Optical Network Security Management
- 2022
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Ingår i: IEEE Transactions on Network and Service Management. - 1932-4537. ; 19:3, s. 2702-2713
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Tidskriftsartikel (refereegranskat)abstract
- The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL-and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.
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