# Detection and Amelioration of Social Engineering Vulnerability in Contingency Table Data using an Orthogonalised Log-linear Analysis

## TL;DR

This paper introduces a novel orthogonalised log-linear analysis method to identify and mitigate social engineering vulnerabilities in contingency table data by analyzing the susceptibility of data subsets to probabilistic inference.

## Contribution

It proposes a new statistical approach using orthogonalised log-linear analysis to assess and counteract social engineering risks in data inference scenarios.

## Key findings

- The method quantifies vulnerability based on subspace projections.
- It demonstrates how non-uniformity in data increases inference susceptibility.
- The approach offers a potential countermeasure to social engineering attacks.

## Abstract

Social Engineering has emerged as a significant threat in cyber security. In a dialog based attack, by having enough of a potential victim's personal data to be convincing, a social engineer impersonates the victim in order to manipulate the attack's target into revealing sufficient information for accessing the victim's accounts etc. We utilise the developing understanding of human information processing in the Information Sciences to characterise the vulnerability of the target to manipulation and to propose a form of countermeasure. Our focus is on the possibility of the social engineer being able to build the victim's profile by, in part, inferring personal attribute values from statistical information available either informally, from general knowledge, or, more formally, from some public database. We use an orthogonalised log linear analysis of data in the form of a contingence table to develop a measure of how susceptible particular subtables are to probabilistic inference as the basis for our proposed countermeasure. This is based on the observation that inference relies on a high degree of non-uniformity and exploits the orthogonality of the analysis to define the measure in terms of subspace projections.