On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks

Bensalem, Mounir; Jukan, Admela

doi:10.1109/globecom38437.2019.9013238

Cited by 19 publications

(12 citation statements)

References 11 publications

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“…ML was applied to optical network security in [8], where the optical spectrum is scrutinized with SVM to identify malicious light sources traversing unauthorized paths. In [37], ANN was applied to detect high-power jamming and identify promising attack mitigation strategies. In [4], we applied various supervised learning approaches for detecting in-band, out-of-band jamming and polarization scrambling attacks on an optical link by analysing the experimental OPM data collected from a coherent receiver.…”

Section: B Optical Network Security Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

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Section: B Optical Network Security Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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“…Network adaptation can benefit from e.g. attack-aware preplanning of backup resources [28], fast frequency hopping [29], connection rerouting, modulation format and spectrum reassignment (e.g., using a procedure described in [30]), or periodical proactive resource reallocation [13].…”

Section: Optical Layer Security In Evolving Network Operation a Network Security Management Frameworkmentioning

confidence: 99%

“…In [12], SVM was applied to detect the presence of unauthorized optical signals by inspecting the optical spectrum. ANN-based approach for detecting high-power jamming attacks was proposed in [13]. Detection and identification of attacks using experimental data obtained by performing in-and out-of-band-jamming, as well as polarization scrambling attacks at the link level was carried out in [14] using various SL techniques.…”

Section: Introductionmentioning

confidence: 99%

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

Furdek

Natalino

Giglio

et al. 2020

J. Opt. Commun. Netw.

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As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating Machine Learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity.

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“…They are generally robust but don't exploit the short-/long-term inter-dependency of features, e.g., positional and navigation AIS information. Contrarily, the use of ML techniques, such as neural networks, long short-term memory (LSTM), have demonstrated promising results for the anomaly detection problem [14]- [20].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories

Singh¹,

Fowdur²,

Gawlikowski³

et al. 2021

Preprint

Self Cite

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Understanding and representing traffic patterns are key to detecting anomalies in the maritime domain. To this end, we propose a novel graph-based traffic representation and association scheme to cluster trajectories of vessels using automatic identification system (AIS) data. We utilize the (un)clustered data to train a recurrent neural network (RNN)-based evidential regression model, which can predict a vessel's trajectory at future timesteps with its corresponding prediction uncertainty. This paper proposes the usage of a deep learning (DL)-based uncertainty estimation in detecting maritime anomalies, such as unusual vessel maneuvering. Furthermore, we utilize the evidential deep learning classifiers to detect unusual turns of vessels and the loss of AIS signal using predicted class probabilities with associated uncertainties. Our experimental results suggest that using graphbased clustered data improves the ability of the DL models to learn the temporal-spatial correlation of data and associated uncertainties. Using different AIS datasets and experiments, we demonstrate that the estimated prediction uncertainty yields fundamental information for the detection of traffic anomalies in the maritime and, possibly in other domains.

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On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks

Cited by 19 publications

References 11 publications

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories

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