Abstract
Successful profile cloning attacks have far-reaching consequences for the victims. People whose profiles are cloned suffer defamation, mistrust, loss of job, interdiction, public disgrace, dent of reputation, and defrauding. This chapter aims to identify and propose a model that detects profile cloning attacks on online social network platforms. The proposed model is based on unsupervised machine learning clustering and statistical similarity verification methods for the filtration of profiles. The model computes statistical values for attribute similarity measure (ASM) and friends network similarity measure (FNSM). The model has a precision score of 100%. The attribute weight and friends network similarity measures show percentile figures ranging from 0.45 to 1.00. Profile accounts that fall within this range for both ASM and FNSM measures are likely to turn out to be cloned. The higher the figures, the more the suspicion of being a fake account to the supposed original one. The strength of the model is that it exposes the actual clone using the outcome of the computation.
| Original language | English |
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| Title of host publication | Risk Detection and Cyber Security for the Success of Contemporary Computing |
| Publisher | IGI Global |
| Pages | 334-360 |
| Number of pages | 27 |
| ISBN (Electronic) | 9781668493199 |
| ISBN (Print) | 9781668493175 |
| DOIs | |
| Publication status | Published - 9 Nov 2023 |