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1. |
Record Nr. |
UNISA996418296803316 |
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Titolo |
Application and Theory of Petri Nets and Concurrency [[electronic resource] ] : 41st International Conference, PETRI NETS 2020, Paris, France, June 24–25, 2020, Proceedings / / edited by Ryszard Janicki, Natalia Sidorova, Thomas Chatain |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (xi, 435 pages) : illustrations |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 12152 |
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Disciplina |
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Soggetti |
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Computer science |
Algorithms |
Computer science—Mathematics |
Discrete mathematics |
Data structures (Computer science) |
Information theory |
Database management |
Computer Science Logic and Foundations of Programming |
Design and Analysis of Algorithms |
Discrete Mathematics in Computer Science |
Data Structures and Information Theory |
Database Management System |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Application of Concurrency to System Design -- Languages and Synthesis -- Semantics -- Process Mining and Applications -- Extensions and Model Checking -- Tools. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 41st International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2020, which was supposed to be held in Paris, France, in June 2020. The conference was held virtually due to the COVID-19 |
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pandemic. The 17 regular and 6 tool papers presented together in this volume were carefully reviewed and selected from 56 submissions. The focus of the conference is on following topics: application of concurrency to system design; languages and synthesis; semantics; process mining and applications; extensions and model checking; tools. |
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2. |
Record Nr. |
UNINA9910556874403321 |
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Titolo |
Machine learning for networking : 4th international conference, MLN 2021, virtual event, December 1-3, 2021 : proceedings / / edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (171 pages) |
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Collana |
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Lecture Notes in Computer Science ; ; v.13175 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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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 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. |
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. |
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. |
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