A privacy-preserving framework for traffic data publishing

A privacy-preserving framework for traffic data publishing

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As explained in Chapter 7, sharing network traffic data has become a vital requirement in machine-learning (ML) algorithms when building an efficient and accurate network traffic classification and intrusion detection system (IDS). However, inappropriate sharing and usage of network traffic data could threaten the privacy of companies and prevent sharing of such data.This chapterpresents aprivacy-preserving strategy-based permutation framework, called PrivTra, in which data privacy, statistical properties, and data-mining utilities can be controlled at the same time. In particular, PrivTra involves the followings: (i) vertically partitioning the original dataset to improve the performance of perturbation; (ii) developing a framework to deal with various types of network traffic data, including numerical, categorical, and hierarchical attributes; (iii) grouping the portioned sets into a number of clusters based on the proposed framework; and (iv) accomplishing the perturbation process by altering the original attribute value with a new value (cluster centroid). The effectiveness of PrivTra is shown through several experiments, such as real network traffic, intrusion detection, and simulated network datasets. Through the experimental analysis, this chapter shows that PrivTra deals effectively with multivariate traffic attributes, produces compatible results as the original data, improves the performance of the five supervised approaches, and provides a high level of privacy protection.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Privacy preserving framework for network traffic data
  • 8.2.1 Desired requirements
  • 8.2.2 An overview of PrivTra
  • 8.2.3 Partitioning
  • 8.2.4 Clustering to enable privacy preserving
  • 8.3 Case study: SCADA platform and processing
  • 8.3.1 Water platform
  • 8.3.2 A water distribution system (WDS) scenario
  • 8.3.3 A scenario of attacks
  • 8.4 Evaluation
  • 8.4.1 Datasets
  • 8.4.2 Baseline methods
  • 8.4.3 Quality evaluation using benchmarking ML methods
  • 8.4.4 Experiment setup
  • 8.4.5 Experiment results
  • 8.4.6 Discussion and summary
  • 8.5 Conclusion

Inspec keywords: telecommunication traffic; pattern classification; statistical analysis; learning (artificial intelligence); data mining; data privacy; computer network security

Other keywords: sharing network traffic data; PrivTra; inappropriate sharing; data-mining utilities; intrusion detection; privacy-preserving framework; machine-learning algorithms; data privacy; multivariate traffic attributes; simulated network datasets; efficient network traffic classification; traffic data publishing

Subjects: Computer communications; Other topics in statistics; Knowledge engineering techniques; Other topics in statistics; Data security; Computer networks and techniques

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