| Description |
1 online resource (xiii, 382 pages) : illustrations (some color) |
| Series |
Lecture notes in computer science ; 11933 |
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LNCS sublibrary. SL 4, Security and cryptology |
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Lecture notes in computer science ; 11933.
|
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LNCS sublibrary. SL 4, Security and cryptology.
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| Contents |
Intro -- Preface -- Organization -- Contents -- Artificial Intelligence for Cybersecurity -- Cross-Domain Recommendation System Based on Tensor Decomposition for Cybersecurity Data Analytics -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Problems and Definitions -- 3.1 The Concept of Tensor -- 3.2 Tensor Decomposition -- 3.3 Recommendation with HOSVD -- 3.4 Cross-Domain Recommendation -- 3.5 Transfer Learning -- 4 Cross-Domain with Tensor Decomposition -- 4.1 Extract Rating Mode -- 4.2 Transfer Rating Model -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Results -- 6 Conclusions |
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4 Experiments and Results -- 4.1 Datasets and Evaluation Measures -- 4.2 Parameter Settings -- 4.3 Results and Analysis -- 5 Conclusion -- A Performance Comparison Between Deep Cascade Forest and Other Classifiers -- B Parameters of LSTM in our Experiment -- References -- Multiplex PageRank in Multilayer Networks Considering Shunt -- Abstract -- 1 Introduction -- 2 Discussion -- 3 Simulations -- 4 Conclusions -- References -- Machine Learning for Cybersecurity -- LogGAN: A Sequence-Based Generative Adversarial Network for Anomaly Detection Based on System Logs -- 1 Introduction -- 2 Related Work |
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2.1 Supervised Anomaly Detection -- 2.2 Semi-supervised Anomaly Detection -- 2.3 Unsupervised Anomaly Detection -- 3 Method -- 3.1 Log Parser -- 3.2 Adversarial Learning -- 3.3 Anomaly Detection -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Result and Discussion -- 5 Conclusion -- References -- Security Comparison of Machine Learning Models Facing Different Attack Targets -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Relevant Machine Learning Models -- 3.2 Adversarial Attack -- 3.3 Security Evaluation -- 4 Attack on Test Data -- 4.1 Background and Settings |
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4.2 Experiments and Results -- 5 Attack on Train Data -- 5.1 Background and Settings -- 5.2 Experiments and Results -- 6 Attack on Model Parameters -- 6.1 With Complete Authority -- 6.2 With Part of Authority and Perfect Knowledge -- 6.3 With Part of Authority and Limited Knowledge -- 7 Conclusion -- References -- Adversarial Training Based Feature Selection -- Abstract -- 1 Introduction -- 2 Framework of Adversarial Training-Based Feature Selection -- 2.1 Objective Functions in Adversarial Training -- 2.2 Optimization Method -- 2.3 Adversarial Training Based Feature Selection |
| Summary |
This book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications) |
| Notes |
Includes author index |
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Online resource; title from PDF title page (SpringerLink, viewed December 24, 2019) |
| Subject |
Computer security -- Congresses
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Computer security
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| Genre/Form |
Electronic books
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proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Liu, Feng, editor
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Xu, Jia (Professor of Computer Science), editor.
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Xu, Shouhuai, editor.
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Yung, Moti, editor.
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| ISBN |
9783030346379 |
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3030346374 |
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3030346366 |
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9783030346362 |
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9783030346386 |
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3030346382 |
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