Abstract: The escalating complexity of cybercrime poses challenges to traditional law enforcement methods. To combat this threat, data analytics has emerged as a promising approach. This paper explores the application of data analytics techniques to analyse the cybercrime underground economy. It examines data sources like publicly available information, dark web data, and cybersecurity incidents. Various methodologies, including machine learning, natural language processing, and anomaly detection, are discussed to identify trends and predict threats. Ethical considerations and successful case studies are also explored. The study highlights data analytics as a valuable tool in understanding cybercrime and devising proactive strategies for threat mitigation, emphasizing the need for collaboration among stakeholders.

Keywords: crimeware-as-a-service, crimeware, hacking, machine learning.

| DOI: 10.17148/IARJSET.2023.10779