| 1. |
- Araujo, Igor, et al.
(author)
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A GPU-assisted NFV framework for intrusion detection system
- 2021
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In: Computer Communications. - : Elsevier BV. - 1873-703X .- 0140-3664. ; 169, s. 92-98
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Journal article (peer-reviewed)abstract
- The network function virtualization (NFV) paradigm advocates the replacement of specific-purpose hardware supporting packet processing by general-purpose ones, reducing costs and bringing more flexibility and agility to the network operation. However, this shift can degrade the network performance due to the non-optimal packet processing capabilities of the general-purpose hardware. Meanwhile, graphics processing units (GPUs) have been deployed in many data centers (DCs) due to their broad use in, e.g., machine learning (ML). These GPUs can be leveraged to accelerate the packet processing capability of virtual network functions (vNFs), but the delay introduced can be an issue for some applications. Our work proposes a framework for packet processing acceleration using GPUs to support vNF execution. We validate the proposed framework with a case study, analyzing the benefits of using GPU to support the execution of an intrusion detection system (IDS) as a vNF and evaluating the traffic intensities where using our framework brings the most benefits. Results show that the throughput of the system increases from 50 Mbps to 1 Gbps by employing our framework while reducing the central process unit (CPU) resource usage by almost 40%. The results indicate that GPUs are a good candidate for accelerating packet processing in vNFs.
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| 2. |
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| 3. |
- Fan, Yuchuan, et al.
(author)
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Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems
- 2021
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In: Journal of Optical Communications and Networking. - : OPTICAL SOC AMER. - 1943-0620 .- 1943-0639. ; 13:10, s. E63-E71
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Journal article (peer-reviewed)abstract
- Error vector magnitude (EVM) has proven to be one of the optical performance monitoring metrics providing the quantitative estimation of error statistics. However, the EVM estimation efficiency has not been fully exploited in terms of complexity and energy consumption. Therefore, in this paper, we explore two deep-learning-based EVM estimation schemes. The first scheme exploits convolutional neural networks (CNNs) to extract EVM information from images of the constellation diagram in the in-phase/quadrature (IQ) complex plane or amplitude histograms (AHs). The second scheme relies on feedforward neural networks (FFNNs) extracting features from a vectorized representation of AHs. In both cases, we use short sequences of 32 Gbaud m-ary quadrature amplitude modulation (mQAM) signals captured before or after a carrier phase recovery. The impacts of the sequence length, neural network structure, and data set representation on the EVM estimation accuracy as well as the model training time are thoroughly studied. Furthermore, we validate the performance of the proposed schemes using the experimental implementation of 28 Gbaud 64QAM signals. We achieve a mean absolute estimation error below 0.15%, with short signals consisting of only 100 symbols per IQ cluster. Considering the estimation accuracy, the implementation complexity, and the potential energy savings, the proposed CNN- and FFNN-based schemes can be used to perform time-sensitive and accurate EVM estimation for mQAM signal quality monitoring purposes.
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| 4. |
- Fan, Yuchuan, et al.
(author)
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Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
- 2021
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In: Journal of Optical Communications and Networking. - : Institute of Electrical and Electronics Engineers Inc.. - 1943-0620 .- 1943-0639. ; 13:4, s. B12-B20
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Journal article (peer-reviewed)abstract
- We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive-white-Gaussian-noise-impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase-shift keying, 2.2% for 16-Ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate estimation, enabling solutions for intelligent optical performance monitoring.
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| 5. |
- Furdek Prekratic, Marija, 1985, et al.
(author)
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Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited]
- 2021
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In: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 13:2, s. A144-A155
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Journal article (peer-reviewed)abstract
- 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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| 6. |
- Monti, Paolo, 1973, et al.
(author)
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Network automation: challenges, enablers, and benefits
- 2021
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Conference paper (other academic/artistic)abstract
- Communication infrastructures are evolving towards an ad-hoc service provisioning scenario where programmability and flexibility are fundamental concepts. Network automation is expected to play a vital role in streamlining all aspects of the service provisioning process (i.e., deployment, maintenance, and tear down). However, to fully realize this autonomous operation vision, closed-loop automation procedures need to be developed. This tutorial will present the main motivations and challenges behind designing and operating closed-loop autonomous decision-making processes, including a brief overview of current standardization initiatives. The tutorial will then address several use cases showcasing how network automation can alleviate the complexity of the service provisioning processes and the benefits brought in by the introduction of network automation.
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| 7. |
- Natalino Da Silva, Carlos, 1987, et al.
(author)
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Autonomous Security Management in Optical Networks
- 2021
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In: 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings.
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Conference paper (peer-reviewed)abstract
- The paper describes the Optical Security Manager module and focuses on the role of Machine Learning (ML) techniques. Issues related to the accuracy, run-time complexity and interpretability of ML outputs are described and coping strategies outlined.
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| 8. |
- Natalino Da Silva, Carlos, 1987, et al.
(author)
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Scalable Physical Layer Security Components for Microservice-Based Optical SDN Controllers
- 2021
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In: European Conference on Optical Communication, ECOC. ; 2021
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Conference paper (peer-reviewed)abstract
- We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.
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| 9. |
- Natalino Da Silva, Carlos, 1987, et al.
(author)
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Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
- 2021
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In: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1089-7798 .- 1558-2558. ; 25:5, s. 1583-1586
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Journal article (peer-reviewed)abstract
- Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, we propose an optical spectrum anomaly detection scheme that exploits computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals. The proposed scheme achieves 100% detection accuracy even without prior knowledge of the anomalies. Furthermore, operation with encoded images of constellation diagrams reduces the runtime by up to 200 times.
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| 10. |
- Natalino Da Silva, Carlos, 1987, et al.
(author)
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Storage Protection with Connectivity and Processing Restoration for Survivable Cloud Services
- 2021
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In: Proceedings - International Conference on Computer Communications and Networks, ICCCN. - 1095-2055. ; 2021-July
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Conference paper (peer-reviewed)abstract
- The operation and management of software-based communication systems and services is a big challenge for infrastructure and service providers. The challenge is mainly associated with the larger number of configurable elements and the higher dynamicity in the software-based systems compared to the classical ones. On the other hand, the modularity and programmability in software-based networks enabled by technologies like Software Defined Networking (SDN) and Network Function Virtualization (NFV) provide new opportunities for operators to realize advanced network and service management strategies beyond the classical techniques. In our work, we elaborate on these new opportunities and propose a novel strategy for the management of survivable cloud services. In particular, we leverage the flexibility of SDN and NFV to combine proactive protection and reactive restoration mechanisms and we put forward a novel strategy for enhancing the survivability of cloud services. Through comprehensive evaluations, we demonstrate that the proposed strategy offers significant benefits in terms of availability and restorability of services while reducing, at the same time, the overhead caused by the relocation of cloud services in case of failures.
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