%0 Generic %1 noauthororeditor %A Furdek, Marija %A Natalino, Carlos %A Schiano, Marco %A Di Giglio, Andrea %B Photonics West %D 2019 %I SPIE %K myown sendate sendate-extend %T Experiment-based identification of service disruption attacks in optical networks %U https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10946/109460D/Experiment-based-detection-of-service-disruption-attacks-in-optical-networks/10.1117/12.2509613.full?SSO=1 %X The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. We experimentally demonstrate the effects of signal insertion attacks with varying intensity in a real-life scenario. By applying data analytics tools, we analyze the properties of the obtained dataset to determine how the relationships among different OPM parameters of the signal change in the presence of an attack as opposed to the normal operating conditions. In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. We demonstrate the potential and the challenges of unsupervised learning for attack detection, propose guidelines for attack signature identification needed for the detection of the considered attack methods, and discuss remaining challenges related to optical network security.

@conference{noauthororeditor, abstract = {The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. We experimentally demonstrate the effects of signal insertion attacks with varying intensity in a real-life scenario. By applying data analytics tools, we analyze the properties of the obtained dataset to determine how the relationships among different \ac{OPM} parameters of the signal change in the presence of an attack as opposed to the normal operating conditions. In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. We demonstrate the potential and the challenges of unsupervised learning for attack detection, propose guidelines for attack signature identification needed for the detection of the considered attack methods, and discuss remaining challenges related to optical network security.}, added-at = {2019-03-12T12:24:24.000+0100}, author = {Furdek, Marija and Natalino, Carlos and Schiano, Marco and Di Giglio, Andrea}, biburl = {https://www.bibsonomy.org/bibtex/21e83c34e986c6a0f8236b495c3240516/marifur}, booktitle = {Photonics West}, interhash = {1f063be80be6357259ecd7d96ed00fc5}, intrahash = {1e83c34e986c6a0f8236b495c3240516}, keywords = {myown sendate sendate-extend}, month = {February}, publisher = {SPIE}, timestamp = {2019-03-12T12:42:14.000+0100}, title = {Experiment-based identification of service disruption attacks in optical networks}, url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10946/109460D/Experiment-based-detection-of-service-disruption-attacks-in-optical-networks/10.1117/12.2509613.full?SSO=1}, venue = {San Francisco, USA}, year = 2019 }