Deep Learning Assisted Cyber Criminal Profiling

Biodoumoye George Bokolo; Lei Chen; Qingzhong Liu

doi:10.1109/BDAI59165.2023.10257003

Deep Learning Assisted Cyber Criminal Profiling

Biodoumoye George Bokolo
, Lei Chen
, Qingzhong Liu

Information Technology

Research output: Contribution to book or proceeding › Conference article › peer-review

2 Scopus citations

Abstract

This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.

Original language	English
Title of host publication	2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	226-231
Number of pages	6
ISBN (Electronic)	9798350303667
ISBN (Print)	9798350303667
DOIs	https://doi.org/10.1109/BDAI59165.2023.10257003
State	Published - Jul 7 2023
Event	6th International Conference on Big Data and Artificial Intelligence, BDAI 2023 - Haining, China Duration: Jul 7 2023 → Jul 9 2023

Publication series

Name	2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)

Conference

Conference	6th International Conference on Big Data and Artificial Intelligence, BDAI 2023
Country/Territory	China
City	Haining
Period	07/7/23 → 07/9/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Information Systems
Information Systems and Management

Keywords

Artificial intelligence
BERT
Distil-BERT
crime
criminal profiling
cyber crime
deep learning

Access to Document

10.1109/BDAI59165.2023.10257003

Cite this

Bokolo, B. G., Chen, L., & Liu, Q. (2023). Deep Learning Assisted Cyber Criminal Profiling. In 2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023 (pp. 226-231). (2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BDAI59165.2023.10257003

@inproceedings{e72900242a794b6c8ee1ef4969d8be8a,

title = "Deep Learning Assisted Cyber Criminal Profiling",

abstract = "This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.",

keywords = "Artificial intelligence, BERT, Distil-BERT, crime, criminal profiling, cyber crime, deep learning",

author = "Bokolo, \{Biodoumoye George\} and Lei Chen and Qingzhong Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023 ; Conference date: 07-07-2023 Through 09-07-2023",

year = "2023",

month = jul,

day = "7",

doi = "10.1109/BDAI59165.2023.10257003",

language = "English",

isbn = "9798350303667",

series = "2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "226--231",

booktitle = "2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023",

address = "United States",

}

Bokolo, BG, Chen, L & Liu, Q 2023, Deep Learning Assisted Cyber Criminal Profiling. in 2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023. 2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI), Institute of Electrical and Electronics Engineers Inc., pp. 226-231, 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023, Haining, China, 07/7/23. https://doi.org/10.1109/BDAI59165.2023.10257003

Deep Learning Assisted Cyber Criminal Profiling. / Bokolo, Biodoumoye George; Chen, Lei; Liu, Qingzhong.
2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 226-231 (2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Deep Learning Assisted Cyber Criminal Profiling

AU - Bokolo, Biodoumoye George

AU - Chen, Lei

AU - Liu, Qingzhong

PY - 2023/7/7

Y1 - 2023/7/7

N2 - This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.

AB - This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.

KW - Artificial intelligence

KW - BERT

KW - Distil-BERT

KW - crime

KW - criminal profiling

KW - cyber crime

KW - deep learning

UR - https://www.scopus.com/pages/publications/85175621188

U2 - 10.1109/BDAI59165.2023.10257003

DO - 10.1109/BDAI59165.2023.10257003

M3 - Conference article

AN - SCOPUS:85175621188

SN - 9798350303667

T3 - 2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)

SP - 226

EP - 231

BT - 2023 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Big Data and Artificial Intelligence, BDAI 2023

Y2 - 7 July 2023 through 9 July 2023

ER -

Deep Learning Assisted Cyber Criminal Profiling

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

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