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[null,"1.","Record Nr.",null,"UNINA9910556874403321"]
[null,null,"Titolo",null,"Machine learning for networking : 4th international conference, MLN 2021, virtual event, December 1-3, 2021 : proceedings / / edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-98978-X"]
[null,null,"Descrizione fisica",null,"1 online resource (171 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13175"]
[null,null,"Disciplina",null,"004.6"]
[null,null,"Soggetti",null,"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Evaluation of Machine Learning Methods for Image Classification: A Case Study of Facility Surface Damage -- 1 Introduction -- 2 Research Methodology -- 2.1 Maximum Likelihood -- 2.2 Random Forest -- 2.3 Evaluation Index of Classification Model -- 3 Analytical Results -- 4 Conclusion -- References -- One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN -- 1 Introduction -- 2 Related Work -- 3 One Dimensional Convolutional Neural Network (1D-CNN) -- 4 Proposed Approach -- 4.1 CICDDoS2019 Dataset -- 4.2 Dataset Manipulation -- 4.3 Proposed 1D-CNN Architecture -- 5 Evaluation and Analysis -- 5.1 1D-CNN Evaluation Criteria -- 5.2 Effectiveness for Applying 1D-CNN in SDN -- 6 Conclusion -- References -- Multi-Armed Bandit-Based Channel Hopping: Implementation on Embedded Devices -- 1 Introduction -- 2 Background -- 3 Further MAB Algorithms -- 4 SW-UCB and D-UCB Under Constraints -- 4.1 Relevant Fixed-Point Arithmetic -- 4.2 Implementation Shortcuts -- 4.3 Integration into IEEE 802.15.4 CSL -- 5 Evaluation -- 5.1 Monte Carlo Simulations -- 5.2 Real-World Packet Delivery Ratios -- 5.3 Overhead on CC2538 SoCs -- 6 Conclusions and Future Work -- References -- Cross Inference of Throughput Profiles Using Micro Kernel Network Method -- 1 Introduction -- 1.1 Profiles from Network Emulations -- 1.2 Contributions -- 2 TCP Throughput Profile -- 3 mKN-ML Method -- 4 Testbed and Emulation Measurements -- 4.1 Testbed Measurements -- 4.2 Mininet mKN Measurements -- 4.3 Multi-site Federation: Mininet Emulation -- 5 "]
[{"$nulls":4},"Estimated Profiles -- 5.1 mKN Generic RTT Set: Concave Target Profiles -- 5.2 VFSIE Measurements: Concave Target Profiles -- 5.3 Exploratory Scenario Profiles -- 6 Generalization Equations -- 7 Conclusions -- References."]
[{"$nulls":4},"Machine Learning Models for Malicious Traffic Detection in IoT Networks /IoT-23 Dataset/ -- 1 Introduction -- 2 Review of the Related Works -- 2.1 Traditional Methods for Network Traffic Analysis -- 2.2 Approaches to Detecting IoT Malicious Traffic Using Machine Learning -- 3 IoT 23 Dataset -- 3.1 Description of IoT-23 Dataset -- 3.2 Data Engineering -- 4 Preprocessing -- 5 Models -- 5.1 Decision Tree Classifier -- 5.2 Logistic Regression -- 5.3 Random Forest Classifier -- 5.4 XGBoost Classifier -- 5.5 Artificial Neural Networks -- 6 Results -- 7 Discussion -- 8 Conclusion -- References -- Application and Mitigation of the Evasion Attack against a Deep Learning Based IDS for IoT -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning IDS -- 2.2 Evasion Attack -- 2.3 Adversarial Attack Defense -- 3 Proposed Evasion Attack Strategy -- 3.1 Oracle Evasion Attack -- 3.2 Modified FGSM -- 4 Defense Strategy -- 4.1 Adversarial Example Training -- 4.2 Outlier Detection -- 5 Validation Results -- 6 Conclusions -- References -- DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering -- 1 Introduction -- 2 Related Work -- 3 Motivation -- 4 Building DynamicDeepFlow Neural Network -- 4.1 Input and Output Structures -- 4.2 Overall Architecture of DDF -- 4.3 Training Algorithm -- 5 Experimental Data and Implementation Details -- 5.1 Experimental Data -- 5.2 Experimental Setup -- 5.3 Implementation Details -- 6 Results -- 6.1 Anomalous Network Traffic Pattern Identification -- 6.2 Sensitivity to Number of Cluster -- 6.3 Visualization of VAE Features -- 7 Conclusion -- References -- Unsupervised Anomaly Detection Using a New Knowledge Graph Model for Network Activity and Events -- 1 Introduction -- 2 Related Work -- 3 AEN Graph Model Overview -- 4 Proposed Anomaly Detection Model -- 4.1 Measure of Anomalousness."]
[{"$nulls":4},"4.2 Features Model -- 5 Experimental Evaluation -- 5.1 Dataset -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Deep Reinforcement Learning for Cost-Effective Controller Placement in Software-Defined Multihop Wireless Networking -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 Experiments, Results and Analysis -- 5 Conclusion and Future Research -- References -- Distance Estimation Using LoRa and Neural Networks -- 1 Introduction -- 2 LoRa Technology -- 3 Related Work -- 4 Neural Network -- 5 Experimental Setup -- 6 Results and Analysis -- 7 Conclusion -- References -- Author Index."]