Crime

All Fraud & Scams Deepfake Abuse Policing & Surveillance Cybercrime

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The Erosion of Cybersecurity Zero-Trust Principles Through Generative AI: A Survey on the Challenges and Future Directions

Generative artificial intelligence (AI) and persistent empirical gaps are reshaping the cyber threat landscape faster than Zero-Trust Architecture (ZTA) research can respond. We reviewed 10 recent ZTA surveys and 136 primary studies (2022–2024) and found that 98% provided only partial or no real-world validation, leaving several core controls largely untested. Our critique, therefore, proceeds on two axes: first, mainstream ZTA research is empirically under-powered and operationally unproven; second, generative-…

Journal of Cybersecurity and Privacy · Crime

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An Introduction to Machine Learning Methods for Fraud Detection

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hy…

Crime

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LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions

This paper presents a systematic review of research (2020–2025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, parti…

Crime

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Leveraging transfer learning with deep learning for crime prediction

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep…

Crime

Evidence-backed problem

CYBERSECURITY CHALLENGES IN THE ERA OF AI

Artificial Intelligence (AI) and cyber security, the environment has been transformed. Using technology, more elaborate cyber-attacks can be carried out, and automated, predictive defensive systems can be implemented. The more traditional and archaic forms of cyber security are becoming increasingly ineffective in the face of cyber threats and malware powered by AI. Automated phishing attacks, deepfake identity fraud, and machine learning adversarial attacks and breaches are just a few of the threats posed by th…

Crime

Evidence-backed problem

AI-driven financial fraud: key risks and legal protections for financial institutions

Abstract Artificial intelligence (AI) has become integral to financial institutions operations. Implementing AI allowed significant improvement in service quality and enabled innovative customer solutions. At the same time, with all the advantages and positive aspects of using AI, it also creates additional risks, depending on who and for what it is used. In the hands of fraudsters, AI becomes a tool with which financial institutions and their clients are causing significant damage, and not only financial. At th…

Crime

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Financial fraud detection through the application of machine learning techniques: a literature review

Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham methods were applied, and 104 articles published between 2012 and 2023 were examined. These articles were selected based on predefined inclusion and exclusion criteria and were obtained from databases suc…

Crime

<b>NEURAL NETWORKS FOR REAL-TIME FINANCIAL FRAUD DETECTION</b>

The accelerating digitalization of financial services has transformed fraud into a systemic global threat, with annual losses estimated at $485.6 billion in 2023 alone — losses that conventional rule-based and statistical detection methods have proven structurally incapable of containing. This article presents a narrative literature review examining how neural network architectures are redefining real-time fraud detection in financial systems. The review covers the theoretical and epistemological foundations of…

Crime

XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities

The rapid expansion of Internet of Things (IoT) technologies in smart cities, healthcare, and industrial automation has intensified the need for cybersecurity frameworks capable of operating at scale and in real time under increasingly sophisticated threat conditions. Traditional security mechanisms and opaque AI-based models are no longer adequate for protecting interconnected urban infrastructures, especially as regulatory and societal expectations move toward transparency and accountability. Although prior su…

Crime

The development of cyber threats related to the use of AI

The rapid development of artificial intelligence (AI) means that its role in cyberspace is also growing, both in terms of threats and defence against them. AI supports the automation of anomaly detection, data analysis, and incident response, which enhances protection efficiency. However, cybercriminals use AI-based solutions to create sophisticated attack tools, such as advanced phishing schemes, deepfakes, and hard-to-detect malware. The author analyses the role of AI in generating cyber threats and evaluates…

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How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape

Generative AI (GenAI) is a powerful technology poised to reshape Trust & Safety. While misuse by attackers is a growing concern, its defensive capacity remains underexplored. This paper examines these effects through a qualitative study with 43 Trust & Safety experts across five domains: child safety, election integrity, hate and harassment, scams, and violent extremism. Our findings characterize a landscape in which GenAI empowers both attackers and defenders. GenAI dramatically increases the scale and speed of…

Crime

How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape

Human Detection of Voice-Cloned Speech Under GSM, VoLTE and VoIP Conditions

The rapid progress of generative speech synthesis and voice-cloning technologies has enabled the creation of highly natural synthetic voices that pose a serious threat to telecommunication security. While most prior studies evaluate human ability to detect audio deepfakes using high-quality, studio-grade recordings, little is known about how real-world telecommunication channels affect perceptual detection. This study investigates the influence of three transmission scenarios—GSM (AMR-NB), VoLTE (AMR-WB), and Vo…

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Human Detection of Voice-Cloned Speech Under GSM, VoLTE and VoIP Conditions

Human detection of AI-generated faces and voices is not domain-general

Recent technological advances have resulted in synthetic faces and voices being perceptually indistinguishable from real faces and voices in typical populations. Faces and voices possess rich personal and social information, meaning synthetic faces and voices, commonly known as "deepfakes" can be used for identity theft, financial fraud, and misinformation campaigns. It is currently unknown whether detection of real versus synthetic content is modality-specific, or whether it generalizes across sensory domains.…

Crime

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers

Although Foundation Models (FMs), such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models’ real-world interaction. To address this, FM providers introduced tool calling—triggering a proliferation of frameworks with distinct tool interfaces. In late 2024, Anthropic introduced the Model Context Protocol (MCP) to standardize this tool ecosystem. With SDK downloads surpassing twenty five million per week and 86% of enterprises using mo…

Crime

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers

Beyond Generative Intelligence: A Comprehensive Review of Emerging Artificial Intelligence Paradigms, Explainability Challenges, Ethical Risks, and Future Directions

The artificial intelligence landscape has undergone a profound transformation from narrow, task-specific automation to sophisticated, multi-paradigm systems capable of autonomous reasoning, emotional understanding, and creative generation. This systematic literature review synthesizes 141 peer-reviewed studies published between 2018 and 2026 to map the evolution of AI paradigms beyond the dominant Generative AI breakthrough. Following PRISMA guidelines, we analyzed 4,250 initial records across six major academic…

Crime

Beyond Generative Intelligence: A Comprehensive Review of Emerging Artificial Intelligence Paradigms, Explainability Challenges, Ethical Risks, and Future Directions