國立虎尾科技大學 |

Machine Learning for Networking = Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning for Networking/ edited by Selma Boumerdassi, Éric Renault, Paul Mühlethaler.
Reminder of title:	Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers /
other author:	Boumerdassi, Selma.
Description:	XIII, 486 p. 267 illus., 183 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-45778-5
ISBN:	9783030457785

Machine Learning for Networking = Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers /
Machine Learning for NetworkingSecond IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers /[electronic resource] :edited by Selma Boumerdassi, Éric Renault, Paul Mühlethaler. - 1st ed. 2020. - XIII, 486 p. 267 illus., 183 illus. in color.online resource. - Information Systems and Applications, incl. Internet/Web, and HCI ;12081. - Information Systems and Applications, incl. Internet/Web, and HCI ;9149.

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. .

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.

ISBN: 9783030457785

Standard No.: 10.1007/978-3-030-45778-5doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Machine Learning for Networking = Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers /
LDR:05147nam a22004215i 4500 001 1028103
003 DE-He213
005 20200701025925.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030457785 $9 978-3-030-45778-5
024 7 $a 10.1007/978-3-030-45778-5 $2 doi
035 $a 978-3-030-45778-5
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
245 1 0 $a Machine Learning for Networking $h [electronic resource] : $b Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers / $c edited by Selma Boumerdassi, Éric Renault, Paul Mühlethaler.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIII, 486 p. 267 illus., 183 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Information Systems and Applications, incl. Internet/Web, and HCI ; $v 12081
505 0 $a 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. .
520 $a 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.
650 0 $a Data mining. $3 528622
650 0 $a Computer organization. $3 596298
650 0 $a Application software. $3 528147
650 0 $a Data protection. $3 557764
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Computer Systems Organization and Communication Networks. $3 669309
650 2 4 $a Computer Applications. $3 669785
650 2 4 $a Security. $3 1114130
700 1 $a Boumerdassi, Selma. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1069412
700 1 $a Renault, Éric. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1264766
700 1 $a Mühlethaler, Paul. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1298571
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030457778
776 0 8 $i Printed edition: $z 9783030457792
830 0 $a Information Systems and Applications, incl. Internet/Web, and HCI ; $v 9149 $3 1253558
856 4 0 $u https://doi.org/10.1007/978-3-030-45778-5
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)