Titlebook: Machine Learning for Networking; Second IFIP TC 6 Int Selma Boumerdassi,Éric Renault,Paul Mühlethaler Conference proceedings 2020 IFIP Inte

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Network Traffic Classification Using Machine Learning for Software Defined Networks,ffective to handle this large number of traffic generated by these technologies. At the same time, Software defined networking (SDN) introduced a programmable and scalable networking solution that enables Machine Learning (ML) applications to automate networks. Issues with traditional methods to cla

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A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detectihis research aims to detect network intrusion with the highest accuracy and fastest time. To achieve this, nine supervised machine learning algorithms were first applied to the UNSW-NB15 dataset for network anomaly detection. In addition, different attacks are investigated with different mitigation

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Root Cause Analysis of Reduced Accessibility in 4G Networks,orks’ events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance..This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important

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Space-Time Pattern Extraction in Alarm Logs for Network Diagnosis, cumbersome and time consuming, experts need tools helping them to quickly pinpoint the root cause when a problem arises. A structure called DIG-DAG able to store chain of alarms in a compact manner according to an input log has recently been proposed. Unfortunately, for large logs, this structure m

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Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Rcale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we c

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