Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles

TL;DR

This paper introduces ICD-IPD, a novel method for detecting cloned social media identities using incomplete profile data, multi-view feature learning, missing value imputation, and machine learning classification, outperforming existing methods.

Contribution

The paper presents a new approach combining multi-view feature extraction, missing data imputation, and gradient boosting for cloned identity detection in social-sensor clouds.

Findings

Outperforms state-of-the-art methods in precision, recall, and F1-score.

Effectively handles incomplete profile data with missing value imputation.

Utilizes a combination of profile, similarity, and difference features for improved detection.

Abstract

We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from a social media. It then learns a multi-view representation associated with a given account and extracts two categories of features for every single account. These two categories of features include profile and Weighted Generalised Canonical Correlation Analysis (WGCCA)-based features that may potentially contain missing values. To counter the impact of such missing values, a missing value imputer will next impute the missing values of the aforementioned profile and WGCCA-based features. After that, the proposed approach further extracts two categories of augmented features for each account pair identified previously, namely, 1) similarity and 2) differences-based features. Finally, these features are concatenated and fed into a Light Gradient Boosting Machine classifier to detect identity cloning. We evaluated and compared the proposed approach against the existing state-of-the-art identity cloning approaches and other machine or deep learning models atop a real-world dataset. The experimental results show that the proposed approach outperforms the state-of-the-art approaches and models in terms of Precision, Recall and F1-score.