# Machine-Learning Based Selection and Synthesis of Candidate Metal-Insulator
Transition Metal Oxides

Alexandru B. Georgescu<EMAIL_ADDRESS>Department of Chemistry, 800 East
Kirkwood Avenue, Indiana University, Bloomington, Indiana 47405, United States
Department of Materials Science and Engineering, Northwestern University,
Evanston, IL 60208, USA  Peiwen Ren  Department of Materials Science and
Engineering, Northwestern University, Evanston, IL 60208, USA  Christopher
Karpovich  Department of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, MA 02139 USA  Elsa Olivetti  Department of
Materials Science and Engineering, Massachusetts Institute of Technology,
Cambridge, MA 02139 USA  James M. Rondinelli<EMAIL_ADDRESS>Department of Materials Science and Engineering, Northwestern University,
Evanston, IL 60208, USA

###### Abstract

The discovery of materials that exhibit a metal-insulator transition (MIT) is
key to the development of multiple types of novel efficient microelectronic
and optoelectronic devices. However, identifying MIT materials is challenging
due to a combination of high computational cost of electronic structure
calculations needed to understand their mechanism, the mechanisms’ complexity,
and the labor-intensive experimental validation process. To that end, we use a
machine learning classification model to rapidly screen a high-throughput
crystal structure database to identify candidate compounds exhibiting
thermally-driven MITs. We focus on three candidate oxides, Ca2Fe3O8, CaCo2O4,
and CaMn2O4, and identify their MIT mechanism using high-fidelity density
functional theory calculations. Then, we provide a probabilistic estimate of
which synthesis reactions may lead to their realization. Our approach couples
physics-informed machine learning, density functional theory calculations, and
machine learning-suggested synthesis to reduce the time to discovery and
synthesis of new technologically relevant materials.

##  1 Introduction

Electronically active correlated electron materials and their properties can
be challenging to predict, requiring high-fidelity, resource-intensive
calculations to understand their electronic structure, followed by significant
time and resource investment for their synthesis, as well as the accurate
experimental measurement of their electronic properties. Further, the
dependence of the predicted property on the exact electronic structure
methodology - which often includes adjustable parameters - , combined with the
high time and resource cost of synthesizing a novel material, make the cycle
of discovery relatively slow.

This is particularly true for metal-insulator transition (MIT) materials,
which undergo a phase transition from a metallic state to an insulating state,
or vice versa, through changes in external conditions, such as temperature,
pressure, or doping 1, 2, leading to a wide variety of applications 3, 4, 5,
6, 7, 8. Here, we focus on thermally-driven MITs, as the state of these
materials usually can be switched not just by temperature, but also with an
applied electric field, mechanical stress, or light, a group with strong
potential technological interest - however, there are only under 70
stoichiometrically distinct materials that display this property, which we
have previously provided a database for 9.

Despite the same macroscopic outcome - a change in electrical resistivity with
temperature - the mechanisms, the stoichiometry, and the crystal structure of
these materials are often dissimilar, and their interplay is often the subject
of debate - and often the exact mechanism is the subject of debate. The
transition from the metallic phase to the insulating phase is usually
triggered by a coupled change in symmetry in both the electronic phase and in
crystal structure, which is often difficult to decouple experimentally and
theoretically; however recent experiments have made progress using for example
isotope effects 10 and superlattice structures 11, 12, 13, 14. Theoretical
methods to simulate the ground state show dependence on the computational
methodology, making the prediction of new compounds difficult 15, 16, 17, 18,
19; complexities associated with doping and vacancies can often further
complicate the picture 20, 21, 22. The temperature dependence of the
transition poses an additional challenge, as density functional theory (DFT)
is a ground state (T=0K) theory. While a zero temperature calculation may help
elucidate the nature of the insulating ground state, it is difficult to
predict with high accuracy whether a metal-insulator transition will occur
with temperature. As a result, much of the focus in the literature has been on
explaining the difference between the two states for previously experimentally
observed MITs. Even though computational models have so far been able to
reproduce varying aspects of the metal-insulator transition, and the
temperature dependence of the electronic 18 or structural degrees of freedom
23, this has always been done on materials with a previously experimentally
confirmed metal-insulator transition, and at high computational expense, and
is at this point not feasible in a high throughput way. Finally, these
calculations do not provide guidance on how to synthesize a new material after
its prediction.

Machine learning tools are increasingly used to understand and discover
materials, to process - and find patterns - both in theoretical and in
experimental data24, 25, 26, 27, 28, 29, as well as to substitute explicit DFT
calculations 30, 31, 32, 26. As machine learning tools become more accurate
with larger training datasets, materials discovery becomes more challenging
when the number of materials with a given property is limited, as is the case
for metal-insulator transition compounds, and quantum materials more broadly.
We have previously begun addressing this challenge using a tree-based machine
learning model - which requires fewer training examples than neural networks-
, with physically informed feature choices 9.

Here, we use a combination of machine learning (ML) tools and detailed
electronic structure calculations, to identify candidate MIT oxides and their
synthesis pathways. We have previously reported a tree-based ML classifier
trained on an MIT database and novel features, and now we use it to filter
compounds in the high-throughput DFT database Materials Project. We obtain 36
compounds from our filtration, from which we down-select to Ca2Fe3O8, CaCo2O4,
and CaMn2O4 for high-fidelity DFT study. We find that the insulating state of
Ca2Fe3O8 is driven by a combination of trigonal-symmetry induced orbital order
and magnetism, similar to what we’ve recently discussed in the 2D van der
Waals dihalides and trihalides (MX2 and MX3), as well as in face and edge-
connected octahedral perovskites 33, 34. This ferrite’s ground state displays
an unusual 4+ ionization state for the Fe atom, which may nonetheless be
stabilized by the material’s trigonal crystal field. CaCo2O4 is likely to have
a mechanism similar to that of LaCoO3 35, 36, 37 with a low-spin insulating
state and a high-spin metallic state, while CaMn2O4 may have a magnetically-
driven transition. For the Fe and the Mn compounds, we use a non-magnetic
calculation as a proxy for the metallic phase. Last, we propose synthesis
precursors and possible transitions using natural language processing. These
synthesis pathways should lower the time lag between our proposal and possible
validation of these materials and their electronic properties.

##  2 Methods

Our computational methodology includes multiple steps (Figure 1). The first,
which was reported in previous work9, is training a tree-based (XGboost) ML-
based classifier on a wide variety of materials, consisting of all the known
materials with a thermally-driven metal-insulator transition at the time of
building the classifier. 111This classifier uses a combination of features
that are known from previous work as being key to whether a material is an MIT
or not (unscreened Hubbard U, charge transfer energy), structural features
related to relevant energy scales (metal-metal, and metal-ligand distances),
and other features which were found to be relevant via F1 scores by the
classifier model (Global Instability Index, Average Deviation of the Covalent
Radius). The second step is to use this tool to scan over a high throughput
database of crystal structures, namely Materials Project38, within a range of
parameters where we believe that the classifier’s predictions are at their
most reliable: the materials space scanned should of course include materials
not in our dataset, but should not stray too far in order to be reliable. A
total of 529 structures were pulled from the MP database. After running these
compounds through the MIT binary classifier, 59 structures not in the training
set were predicted to exhibit a MIT; as multiple structures are polymorphs of
the same stoichiometry, there are only 36 unique chemical formulas as a result
of our filtration; these are presented in Table 1, with their respective
d-electron count. To interface our classifier with the Materials Project, we
use the Pymatgen package to interface with the Materials API 39. There are 3
query parameters: the chemical composition, energy above the convex hull, and
the number of atoms in the unit cell. The chemical composition search space is
constrained to AmBnXk ternary compounds, with elements A and B as the cations,
X as the anion, and m, n, k arbitrary non-zero integers, with A=$\\{$Ca, Sr,
Ba, La$\\}$, B=$\\{$Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zr, Nb, Mo, Tc, Ru, Rh,
Pd,Ag$\\}$, and X = $\\{$N, S, O$\\}$. In order to limit ourselves to
materials that are possible to synthesize, we limit the search to materials
that less than 100 meV per atom above the convex hull.

Figure 1: (a) Workflows for MIT materials identification. In this work we’ve
performed filtration of materials in the Materials Project, identified a few
key materials and performed DFT calculations on them and are proposing them
for experimental validation. (b) Crystal structures of the candidate MIT
materials. The top right image shows the structure of a single layer of Fe-O
polyhedra, which may lead to complex spin structures.

Next, we curate the identified materials, labeling their current experimental
status, and eliminating materials with less than one electron per d-shell (in
an ionic model) for the transition metal atoms, namely CaMoN3 , BaCr2O7 ,
BaMo3O10 , BaNb4O11 , CaNb2O6 . Finally, we perform density functional theory
calculations on the compounds where the experimental status is not known, to
assess the possibility of a metal-insulator transition and its mechanism on
three compounds, Ca2Fe3O8, CaCo2O4 and CaMn2O4 . We next perform density
functional theory calculations using Vienna Ab initio Simulation Package
(VASP) 40, 41, 42; unless otherwise mentioned, the SCAN meta-GGA exchange-
correlation functional 43 is used, which has been proven to give
electronically accurate ground states on MIT and correlated insulating
materials with $3d$-orbital transition metal ions 44. To predict hypothetical
synthesis parameters for the identified possible MIT compounds, we use ML
methods developed in 45 to predict precursors, synthesis route, and solid-
state synthesis temperatures.

##  3 Results and Discussion

Compound | $d^{n}$ |  | Compound | $d^{n}$ |  | Compound | $d^{n}$
---|---|---|---|---|---|---|---
CaMn4S8 | 1,2 |  | Ca4Mn4O11 | 3,4 |  | Ca2Co3O8 | 5
Ca2Cr3O8 | 2 |  | CaMn4O8 | 3,4 |  | Ca3Co2O7 | 5
CaCrN2 | 2 |  | LaMn2O5 | 3,4 |  | CaCoO3 | 5
CaCr4O8 | 2, 3 |  | BaMn3O6 | 3, 4 |  | CaCo2O4 | 6
CaNi2O8 | 3 |  | CaMn3O6 | 3, 4 |  | Ca2Co2O5 | 6
Ca2Mn3O8 | 3 |  | Ca2Fe3O8 | 4 |  | Ca3Ni2O7 | 6
Ca3Cr3N5 | 3 |  | Ca2Mn2O5 | 4 |  | CaNiO3 | 6
CaCr2O4 | 3 |  | Ca3Fe2O7 | 4 |  | BaNi4O8 | 6, 5
CaMnO3 | 3 |  | CaMn2O4 | 4 |  | CaNi2O4 | 7
BaMn6O12 | 3, 4 |  | CaMn4N4 | 4, 5 |  |  |
BaMn4O8 | 3, 4 |  | BaFe4O7 | 5 |  |  |
Table 1: Table of materials identified by the classifier as possibly
displaying an MIT, as sorted by their transition metal d-orbital filling,
$d^{n}$.

We performed DFT calculations using the SCAN functional for CaCo2O4 (or
Ca(CoO2)2 as given in Table 1). Figure 2 shows the band structure and the
density of states (DOS) plots for both the low-spin ($S=0$) and intermediate-
spin ($S=1$) configurations side by side. The high spin ($S=2$) state is
significantly higher in energy and not show. Consequences of a Hubbard $U$
correction on the electronic structure are presented in the SI.

The low-spin configuration has a band gap as dictated by the crystal field
splitting (band gap of $1.67\;eV$), while the high-spin structure is metallic;
the low-spin structure is the ground state. These results are similar to those
of the metal-insulator transition in LaCoO3. The Co atoms in CaCo2O4 and
LaCoO3 both have a $+3$ oxidation state, resulting in a $d^{6}$ electronic
configuration. In LaCoO3, as the temperature increases, a superposition of the
intermediate and high-spin multiplet states become more populated, closing the
band gap. We propose that CaCo2O4 will have a similar transition from a non-
magnetic insulating state at low temperatures to a metallic state at high
temperatures.

The Ca2Fe3O8 polymorph identified by our classifier, (mp-1311759 on Materials
Project), is a pseudo-2D material, with Ca ions filling the interstitial space
between layers of FeO6 octahedra. This material is isosymmetric to the binary
Mn5O8 compound, however its dimensionality is reduced by replacing layers of
the Mn ion with Ca.

The 2D layers display a honeycomb lattice structure that strongly suggests the
possibility of novel magnetic states, particularly as a result of spin
frustration. A detailed study of non-collinear spin structures is beyond the
scope of this paper, as we focus on collinear calculations in this work. Here
Fe4+ is in a $d^{4}$ electronic configuration, an unusual oxidation state for
oxides with octahedral crystal fields. As we have shown in previous work 33,
34, edge connected octahedra with transition metal ions in a pseudo-2D
material lead to an additional, trigonal splitting of the orbitals. In this
material, it may provide an additional ligand field stabilization energy,
which may be responsible for the material’s relative stability as computed by
the materials project ($\sim$36meV/atom above the Hull).

By doubling the unit cell to two formula units to allow for anti-ferromagnetic
ordering, our calculations predict an anti-ferromagnetic, insulating ground
state, while the first excited state predicted is ferromagnetic and metallic -
the ground state found on Materials Project. The material might then display a
transition to a metallic state both as a function of temperature, but also as
a function of magnetic field, making it a magnetoresistive material. In order
to obtain the ground state we’ve identified, we had to double the unit cell
from Materials Project, allowing us to find a magnetic anti-alignment between
the layers, while each layer has a non-zero magnetic moment. In the
ferromagnetic state, which has the second lowest energy, the material is
metallic, pointing to the possibility of controlling its resistivity by
applied magnetic field. The magnetic moment of each Fe site in the ground
state is 2$\mu_{B}$, an intermediate spin state; the point group symmetry of
the atoms is $C_{2h}$ \- a symmetry breaking subset of the trigonal symmetry.
As discussed in previous work, this then suggests that there is no degeneracy
left in the Fe d-basis. The orbital basis then consists of a fully-occupied
lowest energy orbital, and two non-degenerate, half-filled, spin-polarized
orbitals on each Fe atom. Up to the writing of this paper, this material has
not been experimentally observed. Synthesis methods presented in this work may
help guide its validation.

Figure 2: a) and b) Density of states for CaMn2O4 suggests the possibility of
a magnetism-driven transition. c) and d) Density of states for CaCo2O4 in the
non-magnetic S=0 and FM S=1 state, show a similar possible transition
mechanism as in LaCoO3, with the ground state non-magnetic, and a spin-driven
transition as the temperature is raised.  Figure 3: a) The magnetic order for
one layer of Ca2Fe3O8: all Fe are in a S=1 state, the structurally equivalent
Fe(1) and Fe${}_{(1^{\prime})}$ have different spin orientation, while the
Fe(2) has a different symmetry, but has the same spin state as Fe(1). The
magnetic order breaks the symmetry of the honeycomb, and leads to aligned spin
chains within each layer, with non-zero magnetic moment per layer. The
magnetic moments of alternating layers cancel out, leaving the material with
net zero total magnetization. (b) Projected density of states for the ground
state of the material

CaCo2O4 exhibits a network of both edge and corner sharing CoO6 octahedra,
with Ca atoms interspersed. Co3+ adopts a d6 electronic configuration. Despite
the additional symmetry breaking beyond octahedral due to both corner and
edge-connected octahedra, the octahedral crystal field splitting dominates,
and we find that the non-magnetic $S=0$ state, with the three orbitals lowest
in energy completely filled, is the ground state. Antiferromagnetic states
could not be stabilized in this material, suggesting that they may be
unphysical in this material. We were able to stabilize $S=1$and $S=2$ states
by enforcing a total magnetization in the material: these states are metallic,
and the lowest energy state is still higher in energy than the $S=0$ state,
and metallic - as is the case of LaCoO3. The band gap and energy difference
betweeen the insulating and metallic states is higher, so it is likely that
the electronic transition may be sharper than in LaCoO3, and is likely to
happen at a higher temperature.

Similar to LaCoO346, this material may have potential applications in
microelectronic applications, for example in Resistive RAM memories, and in
potential mm radio frequency (mm RF) devices due to its combination of sharp
transition, high operating temperature, and low electric field needed to
induce a transition.

Figure 4: ML-generated synthesis precursor predictions for the 3 compounds
discussed in the main text (a,b,c) and predicted calcination (d) and sintering
(e) temperatures for their respective predicted solid-state synthesis routes.

In LaCoO3, the ground state of the Co $d$-electrons is the
$t^{6}_{2g}e^{0}_{g}$ low-spin, $S=0$ configuration. The band gap is
determined by the crystal field splitting between the empty $e_{g}$ and filled
$t_{2g}$ states. As the temperature is raised, electrons are excited from the
$t_{2g}$ orbitals into the $e_{g}$ orbitals, leading to a combination of a
higher energy $t^{4}_{2g}e^{2}_{g}$ configuration (high-spin, $S=2$), and
intermediate-spin ($t^{5}_{2g}e^{1}_{g}$, $S=1$). The calculations using SCAN
show a higher energy difference between the insulating state and the metallic
state in the new compound than in LaCoO3, so we investigated how this relative
energy difference may depend on the particular choice of treatment of the
correlated state. To do so, we also performed calculations using the PBE
functional with an additional +U correction, in the simplified Dudarev +U
formalism, using the Perdew-Burker-Erzenhof (PBE) exchange correlation
formalism. Our results suggest the possibility of a sharper metal-insulator
transition or a higher transition temperature than that of LaCoO3 of
$T_{MIT}=600K$, as well as the possibility that CaCo2O4 may always be an
insulator.

CaCo2O4 has been synthesized in polycrystalline form 47, and has not been
studied conclusively to exclude potential MIT: the polycrystalline sample did
exhibit insulating behavior ($\rho$) when measured in the temperature ($T$)
range of $10K$ to $380K$, leaving open the possibility of a transition at
higher temperature as suggested by our calculations. The material also showed
metallic-temperature-dependent large thermoelectric power. The polycrystalline
nature of the sample may also contribute to extra resistivity during
measurement since charge carriers can scatter off the grain boundaries. This
suggests the strong possibility that new measurements on higher quality
samples of this material may elaborate further on the promising electronic
structure of this material.

Experimentally, this material exhbits a TN of 240K, however there are no
single-crystal measurements of this material’s resistivity. Suppression of
magnetic order in this material may lead to a closing of its gap. CaMn2O4
crystallizes in the orthorhombic Pbcm space group. Ca2+ is bonded in an
8-coordinate geometry to eight O2- atoms. Mn3+ is bonded to six O2- atoms,
form a mixture of edge and corner-sharing MnO6 octahedra. We have also
performed multiple magnetic calculations, and while our search over possible
magnetic order is not exhaustive, the material is insulating even in the
ferromagnetic state (which is normally the most likely to be metallic of
possible magnetic states). We find that in the non-magnetic state, the
material is metallic. We conclude that this material may display an MIT with
magnetism playing a key role.

Figure 4 (top) shows the predicted precursors for each compound along with the
corresponding probability of a solid-state, sol-gel, hydrothermal, or
precipitation synthesis route. For instance, for Ca2Fe3O8 we have 5
precipitation, 3 sol-gel, and 1 solid-state route predicted, for CaCo2O4 we
have 5 hydrothermal, 3 solid-state, and 1 sol-gel route predicted, and for
CaMn2O4 we have 6 sol-gel and 3 solid-state routes predicted. Plotting the
predicted probabilities allows us to examine what reactions might have more
than one viable option. For instance, for the synthesis of CaMn2O4 from
Ca(NO3)2 and MnO2 we see that sol-gel and solid-state are almost equally
likely routes in terms of how promising they are to synthesize the target
compound.

From the predicted synthesis reactions for the MIT compounds, we took the ones
which were predicted to be solid-state and predicted appropriate calcination
and sintering temperature distributions for the reactions. Figure 4 (bottom)
depicts the generated calcination and sintering temperature distributions for
the solid-state routes predicted for each of the MIT compounds. Lower
processing conditions are desirable in many cases due to energy savings
capabilities and manufacturing requirements. We see that the model predicts
that for CaCo2O4 synthesizing the compound using Ca(NO3)2 and Co3O4 would
require a much lower calcination temperature than using CaCO3 and Co3O4 or
CaCO3 and CoCO3. Similarly, for the synthesis of CaMn2O4, we see that using
CaCO3 and MnO2 affords a lower predicted calcination temperature range than
CaO and MnO2 or CaO and MnO. Such predictions reveal which reactions are
compatible in certain temperature ranges and thus can better inform
experimental design.

##  4 Conclusions

In this work, we proposed a workflow for the computational prediction of
correlated materials that display a metal-insulator transition. Our current
work integrated ML tools and DFT, starting with the previously built database
and ML classifier, applying it to high-throughput libraries to filter
materials, evaluation of possible mechanisms and synthesis of specific
materials. Provided experimental validation of one or more of the materials we
propose, our materials discovery methodology would rapidly accelerate the
discovery of new metal-insulator transition materials - and correlated
electron materials more generally. Our tools are readily available online for
use by other scientists.

As no reliable quantitative methods of determining suitable reaction
conditions exist, our approach could help guide experimentalists with initial
suggestions for synthesis precursors, route, and conditions to aid in
synthesis planning for the discovery and design of new materials. We note that
we do not perform here DFT calculations to estimate which synthesis method is
more likely to lead to the compounds we’ve predicted: these are unlikely to be
reliable without an in-depth study of all the possible factors involved in the
synthesis (pH, temperature, aqueous environment 48).

We note that we have also found that the classifier identifies the
brownmillerite Ca2Co2O5 as a metal-insulator transition compound: while this
material may display an MIT, we find that the complex unit cell required to
simulate it realistically, as well as the interplay of polymorphism and
magnetic states, make it difficult to study its transition mechanism within
the scope of this paper. Nonetheless, this material may display a metal-
insulator transition, provided it can be grown as a single phase.

This research was supported in part through the computational resources and
staff contributions provided for the Quest high performance computing facility
at Northwestern University which is jointly supported by the Office of the
Provost, the Office for Research, and Northwestern University Information
Technology. The information, data, or work presented herein was funded in part
by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of
Energy, under Award Number DE-AR0001209 and the National Science Foundation
(DMR-2324173). The views and opinions of authors expressed herein do not
necessarily state or reflect those of the United States Government or any
agency thereof.

The Supporting Information is available free of charge at: [Link to be
inserted], and relevant structures and the pipeline used to identify them can
be found at:
https://github.com/alexandrub53/MachineLearning-MetalInsulator

Information includes

  1. 1.

Comparison of CaCo2O4 and LaCoO3 energy differences between S=0 and S=1 states
for varying U

  2. 2.

Relative energies for different magnetic configurations for Ca2Fe3O8 and
CaMn2O4

  3. 3.

Additionally, the GitHub repository contains the scripts used to identify the
materials identified in this paper, and optimized crystal structures for the
three compounds studied.

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

Jean Marie Tshimula1,2,3 D’Jeff K. Nkashama1,3 Jean Tshibangu Muabila1,4 René
Manassé Galekwa1,2,5
Hugues Kanda1 Maximilien V. Dialufuma1,6 Mbuyi Mukendi Didier1,2,7,8 Kalala
Kalonji1,9
Serge Mundele1 Patience Kinshie Lenye1 Tighana Wenge Basele1,2,10 Aristarque
Ilunga1,2
Christian N. Mayemba1 Nathanaël M. Kasoro2 Selain K. Kasereka2 Hardy Mikese11
Pierre-Martin Tardif3 Marc Frappier3 Froduald Kabanza3 Belkacem Chikhaoui12
Shengrui Wang3 Ali Mulenda Sumbu13 Xavier Ndona14 Raoul Kienge-Kienge Intudi15
1Groupe de Recherche de Prospection et Valorisation des Données (Greprovad),
Global 2Department of Computer Science, University of Kinshasa, DRC
3Department of Computer Science, Université de Sherbrooke, Canada 4LISV-UVSQ,
Université Paris-Saclay, France 5University of Klagenfurt, Austria 6Montreal
Behavioural Medicine Centre, Centre Intégré Universitaire de Santé et Services
Sociaux du Nord-de-l’Île-de-Montréal (CIUSSS-NIM), Canada 7Biomedical Research
Unit, Hospital Monkole, Kinshasa, DRC 8University of Florida, USA 9School of
Electrical Engineering and Computer Science, University of Ottawa, Canada
10Karlstad University, Sweden 11Institut Supérieur Pédagogique de Kikwit, DRC
12Applied Artificial Intelligence Institute, TELUQ University, Canada
13Faculty of Psychology and Education Sciences, University of Kinshasa, DRC
14Harrisburg University of Science and Technology, USA 15School of
Criminology, University of Kinshasa, DRC. Correspondence email:
<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
###### 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.

##  1 Introduction

Psychological profiling plays a crucial role in cybersecurity, particularly in
understanding and identifying the traits and motives of cybercriminals. In
computer science, cybersecurity aims to safeguard technology within computer
systems, implementing security measures to prevent risks and threats that
could harm the system. This field regulates security measures to thwart third-
party invaders or intruders who engage in malicious activities such as
stealing private, business, or organizational information for personal gain
Weimann (2004); Li and Liu (2021); Cremer et al. (2022).

In the domain of cybercrime, understanding the identity and motives of
intruders plays a key role in mitigating risks to information security
McBrayer (2014); Kumar and Carley (2016); Li (2017); Ablon (2018); Bada and
Nurse (2020); Hunter et al. (2021); Chng et al. (2022); Thackray et al.
(2016). Psychological profiling emerges as a valuable tool for understanding
the psychological traits and characteristics of cybercriminals, which
strengthens strategies against potential cyber threats and assists in the
identification of intruders and their motives through an examination of
behavior, nature, and thought process.

Profiling in cybersecurity involves diverse criminological and criminal-law-
based components, encompassing personal traits, criminal expertise, social
attributes, and motivational factors. These elements help in understanding the
predispositions, personality traits, demographics, socio-economic status, and
motivations of cybercriminals, including those who are particularly elusive
Hani et al. (2024); Holt et al. (2024).

Cybercriminals frequently exhibit a range of psychological traits that
strongly shape their behaviors and actions Bada and Nurse (2020); Chng et al.
(2022); Montañez et al. (2020). These individuals often possess a strong
command of cyber technology, which they exploit for harmful purposes and
various motives; common motives include financial gain, as seen in activities
such as data theft and other forms of cyber fraud Li (2017); Holt et al.
(2021). Many are driven by greed, pursuing financial rewards, while others
seek power or revenge against certain groups or institutions. Some
cybercriminals are thrill-seekers, relishing the risk involved in their
illicit activities, or opportunists who take advantage of vulnerabilities for
personal benefit Thackray et al. (2016); Saroha (2014); McBrayer (2014). There
are also those who simply disregard legal and ethical standards, compromising
their reputations within the cyber community. Traits of fearlessness, with
little regard for potential consequences, and a lack of empathy are also
prevalent. Moreover, some individuals demonstrate boldness, testing their
hacking abilities against individuals and organizations. Collectively, these
traits paint a complex picture of the motivations and behaviors driving
cybercriminals in various scenarios Thackray et al. (2016); Li (2017); Madarie
(2017); Chng et al. (2022); Maalem Lahcen et al. (2020).

Motivating factors behind cybercriminal personality traits include revenge and
blackmailing. Understanding these traits can help minimize security risks and
enable better analysis and resolution of cybercrimes Kipane (2019). In
addition, integrating findings from Large Language Models (LLMs) and
psycholinguistic tools, such as the Linguistic Inquiry and Word Count (LIWC)
dictionary and the Medical Research Council (MRC) psycholinguistic database Ke
et al. (2024); Boyd et al. (2022); Coltheart (1981), into psychological
profiling can significantly enrich the understanding of cybercriminal
behaviors and motivations. This holistic approach to psychological profiling
can not only reveal the complex personalities of cybercriminals but also
strengthen overall security measures, protecting both individuals and
organizations from cyber threats. In this paper, we explore the intersection
of psychology and cybersecurity, with a specific emphasis on the role of LLMs
and psycholinguistic features in profiling cyber threats.

The remainder of this work is organized as follows. Section §2 discusses the
fundamental role of psychological profiling in cybersecurity, outlining how it
aids in understanding and mitigating the behaviors of cybercriminals. Section
§3 explores the application of LLMs in psychological profiling, highlighting
their potential to decode complex patterns of cybercriminal activity. In
Section §4, we examine the incorporation of psycholinguistic features into
cybersecurity strategies, demonstrating how these tools can enhance the
precision of psychological profiles. Section §5 discusses different
perspectives on psychological profiling in cybersecurity. Section §6 addresses
the ethical considerations and privacy implications inherent in the use of
psychological profiling and data analysis in cybersecurity. Finally, Section
§7 discusses future directions for research in this area and Section §8
concludes the paper with reflections on the evolving landscape of
cybersecurity profiling.

##  2 Psychological Profiling in Cybersecurity

Researchers and practitioners reveal a complex profile of cyber criminals,
showcasing traits such as tech-savvy, well-networked, vengeful, goal-oriented,
greedy, manipulative, risk-takers, opportunists, rule-breakers, fearless,
emotionless, and daring McBrayer (2014); Palassis et al. (2021); Saroha
(2014); Thackray et al. (2016); Li (2017); Yang et al. (2018); Holt et al.
(2021). More specifically, Saroha (2014) identified a range of characteristics
including smartness, creativity, and a need for control, shedding light on the
multifaceted nature of individuals involved in cyber crimes, and uncovering
motivating factors like monetary gain, thrill-seeking, and political beliefs
that drive individuals towards engaging in cyber criminal activities.

In addition to profiling traits, understanding the psychological effects of
cybercrime remains essential. Gross et al. (2016) indicated that exposure to
cyber terrorism triggers heightened levels of stress and anxiety among
individuals, akin to the psychological effects of conventional terrorism,
emphasizing the pivotal role of perceived threats in shaping individuals’
attitudes towards government surveillance, regulation, and military responses
in the face of cyber threats. Curtis and Oxburgh (2023) underscored the
significant influence of law enforcement’s lack of cybercrime knowledge on low
conviction rates and victim underreporting. The study revealed that victims
often delay reporting cybercrimes due to embarrassment or a perception that
they are better equipped to handle the situation themselves. This highlights
the importance of training officers to increase their preparedness in dealing
with cybercrime cases and engaging with victims.

In a related vein, Palassis et al. (2021) explored the psychological impacts
of hacking victimization and underlined the need for support organizations to
address these issues. The study underscores the importance of raising
awareness about the psychological effects of cybercrime and promoting support
opportunities for victims. Its findings provide valuable insights for
clinicians and support organizations, informing the development of treatment
guidelines and interventions to address the negative psychological impacts of
hacking. Gomez and Villar (2018) investigated how limited experience and
domain knowledge in cyberspace lead to the use of cognitive shortcuts and
inappropriate heuristics, resulting in elevated levels of dread.

In recent investigations, building upon prior research, Geer (2023)
highlighted the importance of leveraging cybercriminals’ cognitive biases to
influence their behaviors during attacks. The study suggested that by using
algorithms informed by cyberpsychology research, defenders can present low-
risk, low-reward targets to steer hackers away from high-value assets. Studies
show that attackers exhibit risk-averse behavior, preferring attacks on less
secure machines to avoid the appearance of failure. Research on human subjects
engaging in cybercriminal behavior revealed a strong relationship between key
risk-taking and cybercriminal behaviors. Bolton (2019) indicated that
participants’ exposure to fictional media, particularly crime-related
television shows, can influence their attitudes towards criminal
investigations and profiling techniques. The study revealed a correlation
between media consumption habits and the perceived realism of investigative
procedures portrayed in television episodes. Additionally, participants’
beliefs about the role of criminal profilers and the importance of intuition
in investigations were influenced by their media exposure. This underscores
the nuanced relationship between media consumption and perceptions of criminal
behavior and profiling accuracy.

Expanding upon the evolving understanding of cybercriminal behavior,
Lickiewicz (2011) highlighted the significance of intelligence, personality
traits, and social skills in the effectiveness of cyber attacks. The study
emphasized the role of environmental factors, such as family relationships and
educational background, in shaping the behaviors of hackers. It suggested that
a holistic approach, considering both individual characteristics and external
influences, is crucial for developing a comprehensive psychological profile of
cyber criminals. Additionally, the study noted the need for interdisciplinary
collaboration between information technology and investigative psychology to
combat cybercrime.

Psychological profiling, rooted in behavioral analysis and psychological
theory, aims to uncover patterns and traits indicative of malicious intent in
cyber activities. This approach utilizes various aspects of human behavior,
such as language use, decision-making processes, and emotional responses, to
discern the psychological profiles of threat actors Thackray et al. (2016);
Jiang et al. (2018); Kipane (2019); Hani et al. (2024); Budimir et al. (2021);
Bada and Nurse (2021); Montañez et al. (2020); Gaia et al. (2020); Zambrano et
al. (2023); Kioskli and Polemi (2022). Leveraging techniques from psychology,
including personality assessment and psycholinguistic analysis, enables the
identification of anomalous behaviors and potential indicators of cyber
threats.

For instance, Kioskli and Polemi (2022) emphasized the importance of profiling
potential attackers in cybersecurity to enhance the accuracy of vulnerability
severity scores using psychological and behavioral traits. Research
investigated the influence of cultural and psychological factors on cyber-
security behavior, utilizing the Big Five Framework to assess personality
traits and their impact on user attitudes towards privacy and self-efficacy
Halevi et al. (2016); Odemis et al. (2022). More specifically, Hani et al.
(2024) proposed machine learning models for psychological profiling of hackers
based on the “Big Five” personality traits model (OCEAN - Openness,
Conscientiousness, Extroversion, Agreeableness, Neuroticism) and their models
achieved 88% accuracy in mapping personality clusters with different types of
hackers (White Hat, Grey Hat, etc.), identifying cyber-criminal behaviors.
Gaia et al. (2020) discovered that individuals attracted to hacking exhibit
high scores on Machiavellianism and Psychopathy scales, with Grey Hat hackers
showing opposition to authority, Black Hat hackers scoring high on thrill-
seeking, and White Hat hackers displaying tendencies towards Narcissism. The
Dark Triad traits significantly predict interest in different types of
hacking, while thrill-seeking emerges as a key motivator for Black Hat
hackers. Perceptions of apprehension for violating privacy laws negatively
impact Grey Hat and Black Hat hacking.

Table 1: Summary of LLM applications in psychological profiling in cybersecurity Research |  Focus |  Cybersecurity applications |  Sources of data
---|---|---|---
Petrov et al. (2024) |  Simulating human psychological behaviors using LLMs |  Evaluating psychometric properties for profiling potential threats |  Standardized personality constructs
Pellert et al. (2023) |  Repurposing psychometric inventories for LLMs |  Profiling values, morality, and beliefs to detect radicalization |  Standard psychometric inventories
Sorokovikova et al. (2024) |  Fine-tuning LLMs on Big Five traits |  Profiling based on language to identify potential threats |  Psychometric test items
Safdari et al. (2023) |  Administering personality tests on LLMs |  Mimicking specific human personality profiles for threat detection |  Personality tests
Huang et al. (2023) |  PsychoBench framework for evaluating LLM personalities |  Understanding complex psychological profiles for enhanced cybersecurity |  Personality traits, interpersonal relationships, motivational tests, emotional abilities
Frisch and Giulianelli (2024) |  Conditioning LLM agents on personality profiles |  Mimicking human traits for improved phishing and social engineering detection |  Persona conditioning data
Yamin et al. (2021) |  Weaponized use of LLMs in cyber attacks |  Generating malicious code, automated hacking, phishing |  Training data on malware and exploits
Motlagh et al. (2024) |  Generating malicious payloads with LLMs |  Creating new strains of malware |  Relevant cybersecurity data
Beckerich et al. (2023) |  Using LLMs for automated hacking |  Vulnerability scanning and developing exploits |  Hacking toolkits
Schmitt and Flechais (2023) |  Social engineering and phishing |  Mimicking human language for cyber attacks |  Historical phishing data
Zhang et al. (2024) |  PsySafe for framework understanding and mitigating risks arising from dark psychological states |  Identifying vulnerabilities, evaluating safety, and implementing defense mechanisms |  Psychological assessments, behavioral evaluations

Moreover, Kipane (2019) revealed that cybercriminals exhibit a range of
behaviors and traits that deviate from societal norms, influenced by factors
such as heredity, education, culture, and socio-economic status. Profiling
methods focus on identifying key psychological features, modus operandi, and
criminal motivations to aid in early detection and investigation of
cybercrimes. The study emphasizes the significance of expert knowledge and
advanced technologies in enhancing law enforcement efforts to combat
cybercrime. Overall, the research underscores the evolving nature of criminal
profiling in the digital era and the critical role it plays in addressing the
growing threat of cybercriminal activities. In response to the escalating
threat posed by cybercrimes, Thackray et al. (2016) highlighted the diverse
motivations of hackers, including recreation, prestige, revenge, profit, and
ideology, which influence their engagement in cyber activities. The study
underscores the importance of not only teaching coding skills but also
educating individuals about the risks and consequences of online actions to
prevent cyber-crime involvement. Additionally, the research emphasizes the
need to identify at-risk groups and individuals to target awareness campaigns
and promote informed online behavior for future generations. Lastly, the study
suggests that understanding social psychological theories can enhance
communication with hacker communities and individuals, ultimately contributing
to more effective cybersecurity practices.

##  3 LLMs in Psychological Profiling

Large Language Models (LLMs), such as OpenAI’s GPT series of models, Google’s
PaLM and Gemini, and Meta’s LLaMA family of open-source models, have
demonstrated remarkable capabilities in natural language understanding and
generation tasks Minaee et al. (2024). As these models continue to evolve and
become more sophisticated, researchers and practitioners are exploring their
potential applications beyond language tasks, venturing into the realm of
psychological profiling (see Table 1). These models are utilized to profile
individuals based on their language use patterns and communication styles,
facilitating the early detection of potential threats Ke et al. (2024).

The potential applications of LLM-based psychological profiling are vast and
diverse Abdurahman et al. (2024); Ke et al. (2024); Hani et al. (2024);
Pellert et al. (2023); Petrov et al. (2024); Huang et al. (2023). In mental
health settings, these techniques aid in the early detection of psychological
disorders and the development of personalized treatment plans Lai et al.
(2023); Chung et al. (2023); Hagendorff (2023). In human-AI interaction,
understanding the perceived personalities of LLMs improves user engagement and
trust, leading to more natural and effective interactions Sharma et al.
(2024).

However, the application of LLMs to psychological profiling is not without
challenges and ethical considerations. Existing personality models and
assessment methods have been developed primarily for human subjects, and their
suitability for evaluating artificial intelligence systems is questionable.
Additionally, the fluid and context-dependent nature of LLM “personalities”
raises concerns about the reliability and validity of traditional personality
assessment techniques when applied to these models Sorokovikova et al. (2024).
As researchers delve deeper into this emerging field, they must grapple with
the complexities of transferring human-centric concepts like personality to
artificial intelligence systems. LLMs are explored for psychological profiling
tasks, such as detecting personality traits, values, and other non-cognitive
characteristics Hani et al. (2024); Frisch and Giulianelli (2024); Pellert et
al. (2023); Petrov et al. (2024); Huang et al. (2023); Song et al. (2024);
Safdari et al. (2023); Hani et al. (2024); Sorokovikova et al. (2024); Zhang
et al. (2024).

In exploring the multifaceted landscape of psychological profiling with LLMs,
researchers have embarked on various avenues to understand their potential
applications. For instance, Petrov et al. (2024) focused on investigating the
ability of LLMs to simulate human psychological behaviors using prompts to
adopt different personas and respond to standardized measures of personality
constructs to assess their psychometric properties. Pellert et al. (2023)
repurposed standard psychometric inventories originally designed for assessing
human psychological characteristics, such as personality traits, values,
morality, and beliefs, to evaluate analogous traits in LLMs. Sorokovikova et
al. (2024) fine-tuned LLMs on psychometric test items related to the Big Five
personality traits for evaluating personalities based on language. Safdari et
al. (2023) introduced a method for administering personality tests on LLMs and
shaping their generated text to mimic specific human personality profiles.

Furthermore, Huang et al. (2023) proposed PsychoBench, a framework for
evaluating personality traits, interpersonal relationships, motivational
tests, and emotional abilities to uncover complex psychological profiles
within LLMs and their potential integration into human society as empathetic
and personalized AI-driven solutions. Frisch and Giulianelli (2024)
demonstrated that LLM agents conditioned on personality profiles can mimic
human traits, with creative personas displaying more consistent behavior in
both interactive and non-interactive conditions; the research highlights the
importance of robust persona conditioning in shaping LLM behavior and
emphasizes the asymmetry in linguistic alignment between different persona
groups during interactions.

Zhang et al. (2024) presented PsySafe, a framework designed to evaluate and
improve the safety of multi-agent systems (MAS) by addressing the
psychological aspects of agent behavior. PsySafe incorporates dark personality
traits to assess and mitigate potential risks associated with agent behaviors
in MAS; in addition, it includes identifying vulnerabilities, evaluating
safety from psychological and behavioral perspectives, and implementing
effective defense strategies. The findings yielded by PsySafe reveal several
phenomena, including collective dangerous behaviors among agents, their self-
reflection on engaging in such behaviors, and the correlation between
psychological assessments and behavioral safety.

While LLMs offer promising applications in psychological profiling, their
language generation capabilities also raise concerns about potential misuse
for cyber attacks and malicious activities Yamin et al. (2021); Motlagh et al.
(2024); Gupta et al. (2023); Yao et al. (2024). Attack payloads and malware
creation involve LLMs generating malicious code or new strains of malware
through training on relevant data Beckerich et al. (2023); Wu et al. (2023).
Automated hacking and vulnerability scanning tasks can be performed by LLMs,
including generating code for automated hacking attacks, scanning software for
vulnerabilities, or developing exploits Wu et al. (2023); Xu et al. (2024).

In addition, LLMs can be used for social engineering and phishing purposes,
leveraging their ability to mimic human language patterns to create convincing
social engineering attacks, phishing emails, or disinformation campaigns
Schmitt and Flechais (2023). Adversaries could potentially manipulate LLM
outputs for malicious purposes using prompt injection techniques Liu et al.
(2023); Piet et al. (2023). LLMs can generate highly personalized and
persuasive phishing emails tailored to specific individuals within an
organization, bypassing traditional detection systems. Studies show these AI-
crafted attacks can be strikingly effective, with around 10% of recipients
entering credentials on fake login portals Bethany et al. (2024). The ability
of LLMs to mimic human language patterns and adapt to different contexts makes
them a powerful tool for deception and manipulation Prome et al. (2024).

The 2023 Report of Voice of SecOps provides a comprehensive analysis of
threats and stressors posed by LLMs, revealing that 51% of security
professionals are likely to leave their job within 2024.111Generative AI and
Cybersecurity: Bright Future or Business Battleground? Deep Instinct. (2023).
Voice of SecOps Reports. Retrieved from https://www.deepinstinct.com/voice-of-
secops-reports. Accessed on May 12, 2024. The study surveyed over 650 senior
security operations professionals in the U.S. to assess LLMs’ impact on the
cybersecurity industry. Findings indicate a 75% surge in attacks in 2022, with
85% attributing this increase to bad actors leveraging LLMs. Furthermore, 70%
of respondents believe LLMs positively influence employee productivity and
collaboration, while 63% perceive an enhancement in employee morale.
Ransomware emerges as the greatest threat to organizational data security,
with 46% of respondents acknowledging its severity and 62% indicating it as
the top C-suite concern, a notable increase from 44% in 2022; the pressure to
combat ransomware has prompted organizations to revise their data security
strategies, with 47% now possessing a policy to pay the ransom, compared to
34% in the previous year. Moreover, the report reveals a 55% increase in
stress levels among security professionals, primarily attributed to staffing
and resource constraints, cited by 42% of respondents.

##  4 Psycholinguistic Features

Psycholinguistic features encompass a wide range of linguistic attributes and
psychological constructs that reflect cognitive and emotional aspects of
language use. Integrating psycholinguistic features into cybersecurity
frameworks enhances the granularity of threat profiling techniques and enables
a deeper understanding of cybercriminals’ mental states and feelings Jiang et
al. (2018); Deb et al. (2018); Uyheng et al. (2022); Krylova-Grek (2019); Xu
and Rajivan (2023). Psycholinguistic features include sentiment analysis,
linguistic complexity measures, lexical diversity metrics, and stylistic
characteristics. Through advanced text analysis algorithms and machine
learning algorithms, these features can be leveraged to identify anomalous
patterns indicative of malicious intent.

One of the powerful tools in psycholinguistic analysis is the Linguistic
Inquiry and Word Count (LIWC) dictionary Boyd et al. (2022). In the context of
cyber attacks, LIWC has been used to detect deception in phishing emails by
analyzing the psycholinguistic features that attackers employ to deceive end-
users Xu and Rajivan (2023). Research shows that phishers often use language
conveying certainty (e.g. always, never), time pressure and work-related words
to increase vulnerability of targets. Conversely, reward-related words like
money or cash tend to decrease vulnerability as they are associated with
scams. Beyond phishing, LIWC has been applied to study online predator
behavior, analyze developer personalities, model social media rumors, and
understand user reactions in crowdsourcing Rogers et al. (2006); Tausczik and
Pennebaker (2010); Shappie et al. (2020); Kranenbarg et al. (2023); Budimir et
al. (2021).

Building on the potential of LIWC for psycholinguistic analysis in
cybersecurity, researchers explore its applications to understand attacker
behavior and victim vulnerabilities. More precisely, Guo et al. (2023) focused
on analyzing the vulnerability factors of potential victims to cybergrooming
using LIWC to quantify and understand the social-psychological traits that may
make individuals more susceptible to online grooming; they reveal significant
correlations between specific vulnerability dimensions and the likelihood of
being targeted as a victim of cybergrooming. Interestingly, the research
observed negative correlations between victims and certain family and
community-related traits, challenging conventional beliefs about the key
factors contributing to vulnerability in online contexts. Tan et al. (2019)
utilize LIWC and demonstrate that malicious insiders exhibit specific
linguistic patterns in their written communications, including increased use
of self-focused words, negative language, and cognitive process-related words
compared to other team members; as insiders become more detached from the
team, language similarity decreases over time.

In a different angle, psycholinguistic features were utilized to examine the
manipulative aspects of cybercrimes. More specifically, Krylova-Grek (2019)
investigated the psycholinguistic dimensions of social engineering within
cybersecurity, employing activity theory to dissect the methods and techniques
utilized by malicious actors. This research reveals the sophisticated tactics
employed by social engineers to manipulate emotions, impede critical thinking,
and exploit moral values to influence user behavior and extract sensitive
information. Parapar et al. (2014) proposed a machine learning model for
detecting sexual predation in chatrooms using psycholinguistic, content-based,
and chat-based features, and show distinct characteristics that differentiate
predators from non-predators. Particularly, Rogers et al. (2006) investigated
the psychological traits and behaviors of individuals involved in self-
reported criminal computer activities, emphasizing the role of extraversion in
predicting such behavior and challenging stereotypes by shedding light on the
complexities of personality factors in criminal/deviant computer behavior
through the use of Likert-scale questionnaires and psychometric instruments.

Furthermore, Chatterjee and Basu (2021) conducted a study on phishing
influence detection using a novel computational psycholinguistic analysis
approach to identify influential sentences that could potentially lead to
security breaches and hacking in online transactions and social media
interactions, developing a language and domain-independent computational model
based on Cialdini’s principles of persuasion.222The 6 Principles of
Persuasion: Tips from the leading expert on social influence, Douglas T.
Kenrick. Posted Dec. 8, 2012. Retrieved from
https://www.psychologytoday.com/ca/blog/sex-murder-and-the-meaning-of-
life/201212/the-6-principles-of-persuasion. Accessed May 20, 2024. Kranenbarg
et al. (2023) indicated that cyber offenders displayed similarities to the
community sample on certain traits but exhibited differences from offline
offenders, particularly in conscientiousness and openness to experience.
Notably, cyber offenders showed lower scores on honesty-humility compared to
the community sample, suggesting potential implications for intervention
strategies targeting specific personality traits in this population.

Budimir et al. (2021) emphasized the importance of understanding
psycholinguistic features and psychology in cybersecurity to develop effective
strategies and interventions. They explore the emotional responses triggered
by cybersecurity breaches, focusing on the hacking of smart security cameras.
The study identifies a 3-dimensional structure of emotional reactions,
highlighting negative affectivity, proactive versus fight/flight action
tendencies, and emotional intensity and valence. Personality characteristics,
such as the Big Five traits and resilient/overcontrolled/undercontrolled
types, were found to relate to these emotional dimensions.

Recently, the application of sentiment analysis techniques has paved the way
for building psychological profiles and detecting and understanding cyber
threats. Sapienza et al. (2017) utilized sentiment analysis to identify
discussions around exploits, vulnerabilities, and attack planning on dark web
forums even before these threats manifest in the real world, and to provide
early warnings through the observation of changes in sentiment and semantic
context. Deb et al. (2018) proposed approaches to predict cyber-events by
leveraging sentiment analysis on hacker forums and social media to analyze the
sentiment expressed in online discussions and detect signals that may precede
cyber attacks. Jiang et al. (2018) built user psychological profiles based on
the sentiment analysis of their network browsing and email content, and
demonstrate that this approach can proactively and accurately detect malicious
insiders with extreme or negative emotional tendencies.

Building upon recent studies and advancements, Uyheng et al. (2022) developed
a machine learning model called TrollHunter and collected a dataset of online
trolling messages and found that troll messages exhibit more abusive language,
lower cognitive complexity, and greater targeting of named entities and
identities; the model achieved an 89% accuracy rate and F1 score in
identifying trolling behavior.

##  5 Discussion

The integration of psychological profiling into cybersecurity practices offers
a multifaceted approach to understanding and mitigating cyber threats. LLMs
and psycholinguistic features provide deeper understanding into the behaviors,
motivations, and emotional states of cybercriminals. This discussion section
explores the potential benefits, and challenges of these techniques, drawing
from the research findings presented earlier.

###  5.1 Benefits of Psychological Profiling in Cybersecurity

Psychological profiling in cybersecurity holds significant promise.
Identifying psychological traits and patterns in cybercriminal behavior
enables security professionals to anticipate and preemptively counteract
potential threats. For instance, understanding the personality traits and
motivations of different types of hackers (e.g., White Hat, Black Hat, Grey
Hat) allows for more tailored security measures and interventions Hani et al.
(2024); Gaia et al. (2020). The use of LLMs enhances this profiling by
analyzing large volumes of text data, identifying linguistic patterns that may
indicate malicious intent.

Psycholinguistic features, such as those derived from the LIWC dictionary,
provide additional granularity. These features help in detecting subtle cues
in language that might indicate deception, stress, or malicious intent. For
example, certain linguistic markers can distinguish phishing emails from
legitimate communications, thereby improving the accuracy of threat detection
systems Xu and Rajivan (2023); Rogers et al. (2006).

Moreover, the incorporation of psychological profiling can aid in the
development of more personalized cybersecurity training programs.
Understanding the psychological traits that make individuals more susceptible
to cyber attacks allows organizations to design targeted awareness campaigns
and training modules that address specific vulnerabilities.

###  5.2 Challenges and Limitations

Despite the promising applications, several challenges and limitations need to
be addressed. One major challenge is the accuracy and reliability of
psychological profiling techniques.

While LLMs and psycholinguistic tools provide valuable insights, they come
with inherent limitations. Implementing and maintaining these advanced
profiling systems require a workforce equipped with specialized skills in
artificial intelligence, cybersecurity, and psychological analysis. There is
often a shortage of professionals with the necessary expertise to develop,
deploy, and refine these tools. Addressing this skill gap is crucial for the
effective utilization of psychological profiling in cybersecurity.

The effectiveness of LLMs largely depends on the quality and diversity of the
data they are trained on. Inaccurate models can result from poor-quality data,
such as poisoned or contaminated datasets, or from non-representative data.
Moreover, acquiring diverse and representative datasets is particularly
challenging in the field of cybersecurity, where data sensitivity and
proprietary information are significant concerns.

Additionally, the use of these tools can lead to false positives and
negatives, causing either unnecessary alarms or undetected threats. Thus,
ensuring the robustness and validity of these models is vital for their
successful deployment in real-world scenarios Xu and Rajivan (2023); Hani et
al. (2024).

Another challenge lies in the dynamic and evolving nature of cybercriminal
behavior. Cybercriminals continually adapt their tactics to evade detection,
which means that profiling techniques must also evolve. Continuous updates and
refinements to the models and algorithms are necessary to keep pace with these
changes.

The ethical implications of psychological profiling in cybersecurity cannot be
overlooked. The use of personal data to create psychological profiles raises
significant privacy concerns. It is essential to balance the benefits of
enhanced security with the protection of individual privacy rights.
Transparent policies and stringent data protection measures must be in place
to ensure that the use of psychological profiling does not infringe on
personal freedoms.

##  6 Ethical Considerations

Ethical considerations are paramount when employing psychological profiling in
cybersecurity. The potential for misuse of these technologies for
surveillance, manipulation, or discrimination is a serious concern. For
example, the ability of LLMs to generate persuasive phishing emails tailored
to specific individuals poses a significant threat if used maliciously
Liyanage and Ranaweera (2023).

To mitigate these risks, it is crucial to establish ethical guidelines and
regulatory frameworks that govern the use of psychological profiling tools.
These guidelines should emphasize the importance of informed consent, data
minimization, and transparency in the use of personal data. Additionally,
there should be mechanisms for accountability and oversight to ensure that
these technologies are used responsibly and ethically McStay (2020); Fleming
(2021).

##  7 Future Directions

Future research should focus on improving the robustness of psychological
profiling techniques. This includes developing more sophisticated models that
can adapt to the evolving tactics of cybercriminals and integrating multimodal
data sources (e.g., text, behavioral data, biometric data) to create more
comprehensive profiles.

Another promising direction is the exploration of collaborative approaches
that combine human expertise with machine intelligence. Human analysts and AI
systems can collaborate to achieve more effective and nuanced threat detection
and mitigation strategies.

Finally, ongoing efforts to address the ethical and privacy concerns
associated with psychological profiling are essential. This includes
developing new methods for anonymizing and protecting personal data while
still enabling meaningful analysis, as well as fostering a culture of ethical
awareness and responsibility among cybersecurity professionals.

##  8 Conclusion

The integration of psychological profiling, LLMs, and psycholinguistic
features into cybersecurity practices represents a significant advancement in
the field. These techniques offer the potential to enhance threat detection
and mitigation strategies by providing deeper understanding into the behaviors
and motivations of cybercriminals. However, realizing this potential requires
addressing the challenges and ethical considerations associated with these
technologies. By doing so, we can create more robust and responsible
cybersecurity frameworks that protect both organizations and individuals from
evolving cyber threats.

## Acknowledgments

The authors thank all Greprovad members for helpful discussions and comments
on early drafts.

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