Abstract

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.

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