Investigating the Intersection of Cybercrime and Machine Learning: Strategies for Prevention and Detection

Cybercrime is a growing problem that has gained increased attention in recent years due to the growing use of technology and the internet. With the increasing use of machine learning in various fields, including cybersecurity, it is important to examine the intersection of these two domains. This review paper aims to provide an overview of the evolution of cybercrime and its use of machine learning techniques. It also identifies various types of cybercrime that utilize machine learning and the challenges and limitations of detecting and preventing these crimes. The paper also suggests strategies for prevention and detection, both technical and non-technical, that could be used to combat the growing problem of cybercrime. While non-technical strategies include user education, strong passwords, and cooperation between the public and private sectors, technical techniques employ machine learning-based threat detection systems, anomaly detection, and encryption. In conclusion, this research emphasizes the significance of comprehending the relationship between cybercrime and machine learning to create successful preventive and detection solutions.