Umfang:
1 Online-Ressource(XIII, 486 p. 267 illus., 183 illus. in color.)
Ausgabe:
1st ed. 2020.
ISBN:
9783030457785
Serie:
Information Systems and Applications, incl. Internet/Web, and HCI 12081
Inhalt:
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. .
Inhalt:
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.
Weitere Ausg.:
ISBN 9783030457778
Weitere Ausg.:
ISBN 9783030457792
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe MLN (2. : 2019 : Paris) Machine learning for networking Cham : Springer, 2020 ISBN 9783030457778
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe ISBN 9783030457792
Sprache:
Englisch
Fachgebiete:
Informatik
Schlagwort(e):
Maschinelles Lernen
;
Deep learning
;
Mustererkennung
;
Internet der Dinge
;
Konferenzschrift
DOI:
10.1007/978-3-030-45778-5
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