Crime · Cybercrime

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A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large data…

IEEE Access · Crime

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Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms

Abstract The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity has driven a transformational shift, significantly enhancing the ability to detect, respond to, and mitigate complex cyber threats. Traditional defense mechanisms are increasingly inadequate against sophisticated attacks, necessitating the adoption of AI-driven security solutions. This review paper presents a novel, in-depth analysis of state-of-the-art AI and ML techniques applied to intrusion detection, malwar…

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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-…

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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…

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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…

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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…

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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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