Cybercrime Victimization Among Young Adult Males Aged 18--20: A Post-Pandemic Analysis of Converging Risk Factors

• URL: http://arxiv.org/abs/2607.02530v1
• Date: Sat, 30 May 2026 03:00:12 GMT
• Title: Cybercrime Victimization Among Young Adult Males Aged 18--20: A Post-Pandemic Analysis of Converging Risk Factors
• Abstract summary: From 2022 to 2024, individuals aged 20--29 submitted 191,787 complaints to the FBI Internet Crime Complaint Center.<n>This study presents an original risk factor analysis and theoretical synthesis, representing the first integration of criminological, neurological, and behavioral evidence for this demographic sub-cohort.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cybercrime victimization among young adult males aged 18--20 has become an increasingly urgent public safety concern in the post-pandemic digital environment. From 2022 to 2024, individuals aged 20--29 submitted 191,787 complaints to the FBI Internet Crime Complaint Center (IC3), reporting combined losses of more than $1.28 billion. Although this population represents a substantial share of cybercrime victims, the 18--20 male sub-cohort remains insufficiently examined as a distinct demographic group within cybercrime victimization research. This study presents an original risk factor analysis and theoretical synthesis, representing the first integration of criminological, neurological, and behavioral evidence for this specific demographic sub-cohort. Drawing on FBI IC3 and FTC Consumer Sentinel Network data from 2022--2024 alongside European cybersecurity threat intelligence from ENISA, the study develops a unified risk profile centered on three intersecting vulnerability factors: a guardianship gap created by the transition into unsupervised digital independence, heightened behavioral exposure to online risk, and reduced impulse regulation associated with ongoing prefrontal cortex development. The findings show a 49.7% increase in reported losses among the 20--29 age group between 2023 and 2024 and identify financial sextortion, phishing, task scams, in-game currency fraud, and dark web grooming as major attack typologies exploiting this risk convergence. The study offers implications for targeted cybersecurity awareness campaigns, digital literacy education, and policy interventions designed for this high-risk demographic.

Related papers

• Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies [62.65225104423404]
  Self-evolving agent systems introduce a qualitatively new threat landscape in which adversarial influences become permanently encoded.<n>We present a systematic security and privacy analysis organized around the Module-Lifecycle Attack Surface matrix.<n>Our findings establish that self-evolution converts every known attack category from session-bounded to lineage-persistent.
  arXiv  Detail & Related papers  (2026-06-22T09:23:50Z)
• Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts [115.34779081791287]
  We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts.<n>Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.
  arXiv  Detail & Related papers  (2026-06-03T06:14:41Z)
• Global Cybercrime Damages: A Baseline for Frontier AI Risk Assessment [0.0]
  Current estimates of global cybercrime damages vary from tens of billions to tens of trillions of dollars, with little systematic evaluation of their reliability.<n>We establish a more rigorous baseline by surveying 27 existing estimates, critically evaluating their methodologies, and constructing a composite estimate from three independent sources.<n>We estimate total global cybercrime damages at approximately $500 billion USD annually (90% CI: $100 billion-$1 trillion)
  arXiv  Detail & Related papers  (2026-03-21T00:01:45Z)
• Sustaining Cyber Awareness: The Long-Term Impact of Continuous Phishing Training and Emotional Triggers [1.0399614883374282]
  Phishing constitutes more than 90% of successful cyberattacks globally.<n>This study examines how continuous cybersecurity training and emotional cues affect employee susceptibility to phishing.
  arXiv  Detail & Related papers  (2025-10-31T09:15:34Z)
• Cyber Slavery Infrastructures: A Socio-Technical Study of Forced Criminality in Transnational Cybercrime [0.12744523252873352]
  The rise of cyber slavery," a technologically facilitated variant of forced criminality, signifies a concerning convergence of human trafficking and digital exploitation.<n>This study adopts a hybrid qualitative-computational methodology, combining a systematic narrative review with case-level metadata extracted from real-world cyber trafficking incidents.
  arXiv  Detail & Related papers  (2025-10-08T20:38:23Z)
• To Change Or To Stick: Unveiling The Consistency Of Cyber Criminal Signatures Through Statistical Analysis [0.0]
  This study unveils the elusive presence of criminal signatures in cyberspace, validating for the first time their existence through statistical evidence. Our findings verify the existence of unique signatures associated with advanced cybercriminals, bridging a crucial gap in current understanding of human behavior in cyber-attacks.
  arXiv  Detail & Related papers  (2024-08-01T12:08:40Z)
• A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors [1.715270928578365]
  This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors. We propose a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95%) We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets.
  arXiv  Detail & Related papers  (2024-03-28T09:41:24Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review [79.49390241265337]
  Chalearn Face Anti-spoofing Attack Detection Challenge consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results.
  arXiv  Detail & Related papers  (2020-04-23T06:43:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.