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Information security 26th international conference, ISC 2023, Groningen, the Netherlands, November 15-17, 2023, proceedings /

This book constitutes the proceedings of the 26th International Conference on Information Security, ISC 2023, which took place in Groningen, The Netherlands, in November 2023. The 29 full papers presented in this volume were carefully reviewed and selected from 90 submissions. The contributions were...

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Corporate Authors: ISC (Conference : Information security) Groningen, Netherlands),
Other Authors: ISC (Conference : Information security), Athanasopoulos, Elias,, Mennink, Bart,, SpringerLink (Online service)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, [2023]
Physical Description: 1 online resource (xiv, 598 pages) : illustrations (some color).
Series: Lecture notes in computer science ; 14411.
Subjects:
Computer security -- Congresses.
Computer networks -- Security measures -- Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Privacy
  • Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion Attacks
  • 1 Introduction
  • 2 Threat Model
  • 3 Background and Related Work
  • 3.1 Synthetic Data Generation
  • 3.2 Privacy-Preserving Techniques
  • 3.3 Model Inversion Attribute Inference Attacks
  • 3.4 Attribute Disclosure Risk
  • 4 Experimental Setup
  • 4.1 Data Set
  • 4.2 Privacy-Preserving Techniques on Synthetic Training Data
  • 4.3 Target Machine Learning Model
  • 4.4 Model Inversion Attribute Inference Attacks.
  • 5 Performance of the Target Models
  • 6 Results of Model Inversion Attribute Inference Attacks
  • 6.1 Attacks on the Model Trained on Original Data
  • 6.2 Attacks on the Model Trained on Protected Synthetic Data
  • 7 Correct Attribution Probability
  • 8 Conclusion and Future Work
  • References
  • Privacy-Preserving Medical Data Generation Using Adversarial Learning
  • 1 Introduction
  • 2 Background
  • 2.1 Related Works
  • 2.2 Differential Privacy
  • 2.3 Rényi Differential Privacy
  • 3 Algorithmic Framework
  • 3.1 GAN
  • 3.2 Variational Autoencoders
  • 3.3 Model Architecture
  • 3.4 Privacy Loss.
  • 4 Experimental Evaluation
  • 4.1 Datasets
  • 4.2 Comparison
  • 4.3 Synthetic Data Generation
  • 5 Conclusion
  • References
  • Balanced Privacy Budget Allocation for Privacy-Preserving Machine Learning
  • 1 Introduction
  • 2 Preliminary
  • 2.1 Notation
  • 2.2 Local Differential Privacy
  • 2.3 Classification Methods Using Machine Learning
  • 3 Related Work
  • 3.1 Unified LDP-Algorithm
  • 3.2 Scalable Unified Privacy-Preserving Machine Learning Framework (SUPM)
  • 4 Contribution-Based Privacy-Budget Allocation
  • 4.1 Contribution-Based Dimension Reduction Using Odds Ratio.
  • 4.2 Privacy-Preserving Machine Learning with Balanced Privacy Budget Allocation
  • 5 Experiments Analysis
  • 5.1 Experiment Settings
  • 5.2 Logistic Regression
  • 5.3 Support Vector Machine
  • 6 Conclusion
  • References
  • Intrusion Detection and Systems
  • SIFAST: An Efficient Unix Shell Embedding Framework for Malicious Detection
  • 1 Introduction
  • 2 Problem Definition and Background
  • 2.1 Malicious Unix Shell Operations
  • 2.2 Threat Model
  • 3 Related Works
  • 3.1 Command and Script Detection Using NLP Techniques
  • 3.2 Command and Script Detection Using Hybrid Features
  • 4 Methodology.
  • 4.1 Data Preprocessing and AShellTokenizer
  • 4.2 Command Embedding
  • 4.3 Script Embedding
  • 5 Experiment Settings and Results
  • 5.1 Experiment Settings
  • 5.2 Evaluations
  • 6 Conclusion
  • References
  • VNGuard: Intrusion Detection System for In-Vehicle Networks
  • 1 Introduction
  • 2 Background and Related Work
  • 2.1 Local Interconnect Network (LIN)
  • 2.2 Automotive Ethernet (AE)
  • 2.3 Intrusion Detection Systems for In-Vehicle Networks
  • 3 Attack Scenarios for LIN
  • 4 Attack Scenarios for AE
  • 5 Methodology
  • 5.1 Data Extraction
  • 5.2 Data Pre-processing
  • 5.3 Model Structure.

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