Machine Learning for Networking : Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3-5, 2019, Revised Selected Papers / edited by Selma Boumerdassi, Éric Renault, Paul Mühlethaler.

Format:

Book

Contributor:

Boumerdassi, Selma, Editor.

Renault, Eric, Editor.

Mühlethaler, Paul, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 12081

Information Systems and Applications, incl. Internet/Web, and HCI ; 12081

Language:

English

Subjects (All):

Data mining.

Computer engineering.

Computer networks.

Application software.

Data protection.

Data Mining and Knowledge Discovery.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Data and Information Security.

Local Subjects:

Data Mining and Knowledge Discovery.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Data and Information Security.

Physical Description:

1 online resource (XIII, 486 pages) : 267 illustrations, 183 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, patternrecognition and classi cation for networks, machine learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new innovative machine learning methods, performanceanalysis of machine learning algorithms, experimental evaluations ofmachine learning, data mining in heterogeneous networks, distributed and decentralizedmachine learning algorithms, intelligent cloud-support communications,ressource allocation, energy-aware communications, software de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks.

Contents:

Network Anomaly Detection using Federated Deep Autoencoding Gaussian Mixture Model

Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Network Trafic Classifi cation using Machine Learning for Software Defined Networks

A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection

Q-routing: from the algorithm to the routing protocol

Language Model Co-occurrence Linking for Interleaved Activity Discovery

Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization

Denoising Adversarial Autoencoder for Obfuscated Tra c Detection and Recovery

Root Cause Analysis of Reduced Accessibility in 4G Networks

Space-time pattern extraction in alarm logs for network diagnosis

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Algorithm Selection and Model Evaluation in Application Design using Machine Learning

GAMPAL: Anomaly Detection for Internet Backbone Tra c by Flow Prediction with LSTM-RNN

Revealing User Behavior by Analyzing DNS Tra c

A new approach to determine the optimal number of clusters based on the Gap statistic

MLP4NIDS: an e cient MLP-based Network Intrusion Detection for CICIDS2017 dataset

Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection

Emotion-based Adaptive Learning Systems

Machine learning methods for anomaly detection in IoT networks, with illustrations

DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning

Arguments Against using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative

Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach

An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

A Learning Approach for Road Tra c Optimization in Urban Environments

CSI based Indoor localization using Ensemble Neural Networks

Bayesian Classi ers in Intrusion Detection Systems

A Novel Approach towards Analysis of Attacker Behavior in DDoS Attacks

Jason-RS, a Collaboration between Agents and an IoT Platform

Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief

Association Rules Algorithms for Data Mining Process Based on Multi Agent System

Internet of Things: Security Between Challenges and Attacks

Socially and biologically inspired computing for self-organizing communications networks. .

Other Format:

Printed edition:

ISBN:

978-3-030-45778-5

9783030457785

Access Restriction:

Restricted for use by site license.