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

2020 ◽  
Vol 13 (2) ◽  
pp. A144
Author(s):  
Marija Furdek ◽  
Carlos Natalino ◽  
Andrea Di Giglio ◽  
Marco Schiano
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Optical Network ◽  
Security Management ◽  
Learning Models ◽  
Efficient Machine ◽  
Network Security Management ◽  
Machine Learning Models

scholarly journals Efficient machine learning models for prediction of concrete strengths

2021 ◽  
Vol 266 ◽  
pp. 120950
Author(s):  
Hoang Nguyen ◽  
Thanh Vu ◽  
Thuc P. Vo ◽  
Huu-Tai Thai
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Efficient Machine ◽  
Machine Learning Models

scholarly journals Root Cause Analysis for Autonomous Optical Network Security Management

2021 ◽  
Author(s):  
Carlos Natalino ◽  
Marco Schiano ◽  
Andrea Di Giglio ◽  
Marija Furdek
Keyword(s):  
Network Security ◽  
Physical Layer Security ◽  
Optical Network ◽  
Security Management ◽  
Root Cause Analysis ◽  
Physical Layer ◽  
Security Assessment ◽  
Cause Analysis ◽  
Root Cause ◽  
Network Security Management

<div>The ongoing evolution of optical networks towards autonomous systems supporting high-performance services be-yond 5G requires advanced functionalities for automated security management. These functionalities need to support risk reduction, security diagnostics and incident remediation strategies. To cope with evolving security threat scenarios, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms have been shown to perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) a crucial tool to enable timely attack response when human intervention is unavoidable.</div><div>We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alter-natives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding ML-based security assessment functionalities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). Extensive validation of the framework’s applicability and performance in the context of autonomous optical network security management is carried out using an experimental physical-layer security dataset, evaluating the benefits and drawbacks of the SL- and UL-based RCA techniques. Besides confirming that SL-based approaches can be trained to provide precise RCA output for known attack types, the study shows that the proposed UL-based RCA approach offers meaningful insights into the properties of anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.</div>

scholarly journals Constructing an Efficient Machine Learning Model for Tornado Prediction

2020 ◽  
Vol 19 (05) ◽  
pp. 1177-1187
Author(s):  
Fuad Aleskerov ◽  
Sergey Demin ◽  
Michael B. Richman ◽  
Sergey Shvydun ◽  
Theodore B. Trafalis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Superposition Principle ◽  
Learning Models ◽  
Analysis Techniques ◽  
Machine Learning Model ◽  
Choice Procedures ◽  
Efficient Machine ◽  
Machine Learning Models

Tornado prediction variables are analyzed using machine learning and decision analysis techniques. A model based on several choice procedures and the superposition principle is applied for different methods of data analysis. The constructed model has been tested on a database of tornadic events. It is shown that the tornado prediction model developed herein is more efficient than a previous set of machine learning models, opening the way to more accurate decisions.

scholarly journals A novel approach to ensemble MLP and random forest for network security

2020 ◽  
Vol 32 ◽  
pp. 03003
Author(s):  
Bhushan Deore ◽  
Aditya Kyatham ◽  
Shubham Narkhede
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Network Security ◽  
Multiple Models ◽  
Testing Time ◽  
Learning Models ◽  
The Third ◽  
Novel Approach ◽  
Management Concept ◽  
Machine Learning Models

The following paper provides a novel approach for Network Intrusion Detection System using Machine Learning and Deep Learning. This approach uses two MLP (Multi-Layer Perceptron) models one having 3 layers and other having 6 layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and also the testing time is reduced. Researchers have implemented various ways for the ensemble of multiple models but we are using contradiction management concept to ensemble machine learning models. Contradiction Management concept means if two machine learning models are contradicting in their decisions (in our case 3-layer MLP and Random Forest), then the third model’s (6-layer MLP) decision is considered whose accuracy is higher than the previous models. The third model is only used for testing when the previous two models contradict in their decision because the testing time of third model is higher than the two previous models as the third model has complex architecture. This approach increased the final accuracy as ensemble of multiple models is done and also testing time has reduced. The novelty of this paper is the choice and the combination of the models for the purpose of Network security.

scholarly journals Data-efficient machine learning for molecular crystal structure prediction

2021 ◽  
Vol 12 (12) ◽  
pp. 4536-4546
Author(s):  
Simon Wengert ◽  
Gábor Csányi ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Molecular Crystals ◽  
Crystal Structure Prediction ◽  
Learning Models ◽  
Training Scheme ◽  
Efficient Machine ◽  
Machine Learning Models

Using a cluster-based training scheme and a physical baseline, data efficient machine-learning models for crystal structure prediction are developed, enabling accurate structural relaxations of molecular crystals with unprecedented efficiency.

scholarly journals Root Cause Analysis for Autonomous Optical Network Security Management

2021 ◽  
Author(s):  
Carlos Natalino ◽  
Marco Schiano ◽  
Andrea Di Giglio ◽  
Marija Furdek
Keyword(s):  
Network Security ◽  
Physical Layer Security ◽  
Optical Network ◽  
Security Management ◽  
Root Cause Analysis ◽  
Physical Layer ◽  
Security Assessment ◽  
Cause Analysis ◽  
Root Cause ◽  
Network Security Management

<div>The ongoing evolution of optical networks towards autonomous systems supporting high-performance services be-yond 5G requires advanced functionalities for automated security management. These functionalities need to support risk reduction, security diagnostics and incident remediation strategies. To cope with evolving security threat scenarios, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms have been shown to perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) a crucial tool to enable timely attack response when human intervention is unavoidable.</div><div>We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alter-natives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding ML-based security assessment functionalities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). Extensive validation of the framework’s applicability and performance in the context of autonomous optical network security management is carried out using an experimental physical-layer security dataset, evaluating the benefits and drawbacks of the SL- and UL-based RCA techniques. Besides confirming that SL-based approaches can be trained to provide precise RCA output for known attack types, the study shows that the proposed UL-based RCA approach offers meaningful insights into the properties of anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.</div>

scholarly journals Root Cause Analysis for Autonomous Optical Network Security Management

2021 ◽  
Author(s):  
Carlos Natalino ◽  
Marco Schiano ◽  
Andrea Di Giglio ◽  
Marija Furdek
Keyword(s):  
Network Security ◽  
Physical Layer Security ◽  
Optical Network ◽  
Security Management ◽  
Root Cause Analysis ◽  
Physical Layer ◽  
Security Assessment ◽  
Cause Analysis ◽  
Root Cause ◽  
Network Security Management

<div>The ongoing evolution of optical networks towards autonomous systems supporting high-performance services be-yond 5G requires advanced functionalities for automated security management. These functionalities need to support risk reduction, security diagnostics and incident remediation strategies. To cope with evolving security threat scenarios, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms have been shown to perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) a crucial tool to enable timely attack response when human intervention is unavoidable.</div><div>We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alter-natives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding ML-based security assessment functionalities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). Extensive validation of the framework’s applicability and performance in the context of autonomous optical network security management is carried out using an experimental physical-layer security dataset, evaluating the benefits and drawbacks of the SL- and UL-based RCA techniques. Besides confirming that SL-based approaches can be trained to provide precise RCA output for known attack types, the study shows that the proposed UL-based RCA approach offers meaningful insights into the properties of anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.</div>

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