Machine Learning for Networking

Table of Contents

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   Book Overview
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   Chapter 1 Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model
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   Chapter 2 Towards a Hierarchical Deep Learning Approach for Intrusion Detection
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   Chapter 3 Network Traffic Classification Using Machine Learning for Software Defined Networks
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   Chapter 4 A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection
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   Chapter 5 Q-routing: From the Algorithm to the Routing Protocol
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   Chapter 6 Language Model Co-occurrence Linking for Interleaved Activity Discovery
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   Chapter 7 Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization
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   Chapter 8 Denoising Adversarial Autoencoder for Obfuscated Traffic Detection and Recovery
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    Chapter 9 Root Cause Analysis of Reduced Accessibility in 4G Networks
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    Chapter 10 Space-Time Pattern Extraction in Alarm Logs for Network Diagnosis
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    Chapter 11 Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses
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    Chapter 12 Algorithm Selection and Model Evaluation in Application Design Using Machine Learning
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    Chapter 13 GAMPAL: Anomaly Detection for Internet Backbone Traffic by Flow Prediction with LSTM-RNN
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    Chapter 14 Revealing User Behavior by Analyzing DNS Traffic
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    Chapter 15 A New Approach to Determine the Optimal Number of Clusters Based on the Gap Statistic
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    Chapter 16 MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset
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    Chapter 17 Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection
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    Chapter 18 Emotion-Based Adaptive Learning Systems
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    Chapter 19 Machine Learning Methods for Anomaly Detection in IoT Networks, with Illustrations
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    Chapter 20 DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning
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    Chapter 21 Arguments Against Using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative
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    Chapter 22 Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach
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    Chapter 23 An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels
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    Chapter 24 A Learning Approach for Road Traffic Optimization in Urban Environments
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    Chapter 25 CSI Based Indoor Localization Using Ensemble Neural Networks
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    Chapter 26 Bayesian Classifiers in Intrusion Detection Systems
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    Chapter 27 A Novel Approach Towards Analysis of Attacker Behavior in DDoS Attacks
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    Chapter 28 Jason-RS, A Collaboration Between Agents and an IoT Platform
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    Chapter 29 Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief
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    Chapter 30 Association Rules Algorithms for Data Mining Process Based on Multi Agent System
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    Chapter 31 Internet of Things: Security Between Challenges and Attacks
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    Chapter 32 Socially and Biologically Inspired Computing for Self-organizing Communications Networks