| 1. |
- Song, Haokun, et al.
(författare)
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Cluster-Based Method for Eavesdropping Identification and Localization in Optical Links
- 2023
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Ingår i: Asia Communications and Photonics Conference, ACP. - 2162-108X.
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Konferensbidrag (refereegranskat)abstract
- We propose a cluster-based method to detect and locate eavesdropping events in optical line systems characterized by small power losses. Our findings indicate that detecting such subtle losses from eavesdropping can be accomplished solely through optical performance monitoring (OPM) data collected at the receiver. On the other hand, the localization of such events can be effectively achieved by leveraging in-line OPM data.
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| 2. |
- Song, Haokun, et al.
(författare)
-
Eavesdropping Detection and Localization in WDM Optical System
- 2023
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Ingår i: Proceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023.
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Konferensbidrag (refereegranskat)abstract
- Leveraging our initial work on detecting eavesdropping events in WDM optical systems [1], we propose a mechanism that utilizes bisecting k-means on dynamic optical performance monitoring (OPM) data to initialize the detection. We develop a method to detect and localize single and multiple eavesdropping events in WDM optical systems. Very small losses caused by eavesdropping can be detected using OPM data collected at the receiver, while the in-line OPM data enables localizing single and multiple eavesdropping events.
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| 3. |
- Song, Haokun, et al.
(författare)
-
Machine-learning-based method for fiber-bending eavesdropping detection
- 2023
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Ingår i: Optics Letters. - 0146-9592 .- 1539-4794. ; 48:12, s. 3183-3186
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Tidskriftsartikel (refereegranskat)abstract
- In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long short-term memory (LSTM) network is applied for eavesdropping and normal event classification. Experimental data are collected from a 60km single-mode fiber transmission link with eavesdropping implemented by a clip-on coupler. Results show that the proposed scheme achieves a 95.83% detection accuracy. Furthermore, since the scheme focuses on the time-domain waveform of the received optical signal, additional devices and a special link design are not required.
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