| 1. |
- Chiaraviglio, Luca, et al.
(författare)
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Minimum Cost Design of Cellular Networks in Rural Areas with UAVs, Optical Rings, Solar Panels and Batteries
- 2019
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Ingår i: IEEE Transactions on Green Communications and Networking. - 2473-2400.
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Tidskriftsartikel (refereegranskat)abstract
- Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites. We then target the minimization of the installation costs for the considered UAV-based cellular architecture, by taking into account the constraints of UAVs coverage, SPs energy consumption, levels of the batteries and the deployment of the optical ring. After providing the problem formulation, we derive an innovative methodology to ensure that a single ring of installed optical fibers is deployed. Moreover, we propose a new algorithm, called DIARIZE, to practically tackle the problem. Our results, obtained over a set of representative rural scenarios, show that DIARIZE performs very close to the optimal solution, and in general outperforms a reference design based on fixed BSs.
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| 2. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
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Demonstration of Machine-Learning-Assisted Security Monitoring in Optical Networks
- 2019
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Ingår i: Proceedings of the 45th European Conference on Optical Communication, ECOC 2019.
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Konferensbidrag (refereegranskat)abstract
- We report on the first demonstration of machine-learning-assisted detection, identification and localisation of optical-layer attacks integrated into network management system and verified on real-life experimental attack traces from a network operator testbed.
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| 3. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
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Enhancing optical network security with machine learning
- 2019
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Ingår i: International Conference on Transparent Optical Networks. - 2162-7339. ; 2019-July
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Konferensbidrag (refereegranskat)abstract
- As critical communication infrastructure, optical networks have a vital role in safe and dependable transmission of massive amounts of data, supporting essential societal services. However, these networks are inherently vulnerable to a multitude of deliberate, man-made attacks targeting service disruption at the physical layer. Physical-layer attack techniques can range in their scope and effects, level of sophistication, locality, detectability, etc. An example of a relatively unsophisticated attack method is a deliberate fiber cut, typically targeting critical network elements (e.g., links with the highest betweenness) and resulting in straightforward transmission interruption [1]. More refined attack techniques rely on the insertion of harmful signal (e.g. in- and out-of-band jamming) [2], or on external tampering with the fiber to degrade the transmission quality (e.g., polarization scrambling via fiber squeezing) [3]. Diverse attack techniques cause different effects, which complicates their detectability. For example, some attacks add unfilterable noise, some reduce the power of the affected optical channels, while some inflict changes in the state of polarization too quick for the coherent receiver to compensate [3]. Therefore, monitoring only the spectrum [4], or individual signal parameters such as the power, optical signal-to-noise ratio (OSNR), or presence of errors may result in inaccurate diagnostics and root cause attribution. This obstacle in quick recovery of affected services is further pronounced for newly emerging attack techniques whose effects may deviate from the attack signatures previously known to the network management system [5].The complexity of the evolving physical-layer security landscape and the intricate interplay of different optical performance monitoring (OPM) parameters in the presence of diverse attack methods can greatly benefit from the application of machine learning techniques capable of deep data analysis. In this talk, we present how different data analytics and machine learning approaches can be applied to interpret the OPM data reported from the commercially available coherent receivers to identify anomalous operation and trigger security threat warnings. The analytical techniques are applied to experimental data obtained from an operator's metropolitan testbed subjected to in- and out-of-band jamming, and external polarization scrambling attacks. We begin with an analysis of the optical signal degradation caused by the different attack methods. We then investigate the application of several supervised learning approaches that, once trained on the experimental data, can detect the presence of an attack and identify its type and intensity. The accuracy of several classifiers is scrutinized, along with the relevance of OPM parameters reported by the coherent receivers and the impact of missing features. To gain insight into the potential of the network to detect emerging, previously unseen attack techniques, we further analyse the performance of unsupervised learning techniques that detect the anomalies in signal parameters introduced by attacks. The presented findings help achieve timely and accurate detection of physical-layer attacks and serve as a prerequisite for fast and effective attack response and network recovery.
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| 4. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
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Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning
- 2019
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Ingår i: Metro and Data Center Optical Networks and Short-Reach Links II; 109460D. - : SPIE. - 0277-786X .- 1996-756X. - 9781510625341 ; 10946
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Konferensbidrag (refereegranskat)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 optical performance monitoring (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.
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| 5. |
- Lashgari, Maryam, 1991, et al.
(författare)
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Cost Benefits of Centralizing Service Processing in 5G Network Infrastructures
- 2019
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Ingår i: Optics InfoBase Conference Papers. - 2162-2701.
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Konferensbidrag (refereegranskat)abstract
- We assess the benefits of centralizing service processing in a few high-scale data center locations within an operator infrastructure. Results show up to 74% less cost while provisioning latency and availability constrained services.
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| 6. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks
- 2019
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 37:16, s. 4173-4182
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Tidskriftsartikel (refereegranskat)abstract
- Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the various attack techniques causing diverse physical- layer effects, as well as the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect, and their signatures are unknown. This paper presents a Machine Learning (ML) framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field-deployed testbed with coherent receivers. We perform in-band and out-of-band jamming signal insertion attacks, as well as polarization modulation attacks, each with varying intensities. We then evaluate 8 different ML classifiers in terms of their accuracy, and scalability in processing experimental data. The optical parameters critical for accurate attack identification are identified and the generalization of the models is validated. Results indicate that Artificial Neural Networks (ANNs) achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.
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| 7. |
- Raza, Muhammad Rehan, et al.
(författare)
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Machine Learning Methods for Slice Admission in 5G Networks
- 2019
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Ingår i: OECC/PSC 2019 - 24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing 2019. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- The paper discusses how the slice admission problem can be aided by machine learning strategies. Results show that both supervised and reinforcement learning might lead to profit maximization while containing losses due to performance degradation.
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| 8. |
- Raza, Muhammad Rehan, et al.
(författare)
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Reinforcement Learning for Slicing in a 5G Flexible RAN
- 2019
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Ingår i: Journal of Lightwave Technology. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8724 .- 1558-2213. ; 37:20, s. 5161-5169
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Tidskriftsartikel (refereegranskat)abstract
- Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i)slice degradation penalty vs. slice revenue factors, and (ii)proportion of high vs. low priority services.
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