Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features

TL;DR

This paper explores how Large Language Models and psycholinguistic features can be used to analyze textual data for psychological profiling of threat actors, enhancing cybersecurity defenses.

Contribution

It introduces a novel approach combining LLMs and psycholinguistic analysis for psychological profiling in cybersecurity contexts.

Findings

LLMs can effectively identify psychological traits from textual data.

Psycholinguistic features improve threat actor profiling accuracy.

Integrating psychology into cybersecurity enhances threat detection capabilities.

Abstract

The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats.