Implementation of Machine Learning Techniques for Analyzing Crime and Dark Web Data

Abstract

The dark web is a virtually untraceable hidden layer of the internet that is frequently used to store and access secret data. However, a number of situations have been documented in which this platform has been used to covertly undertake illicit and unlawful operations. Traditional crime-solving procedures are inadequate to meet the demands of the current crime environment. Machine learning can be used to detect criminal patterns. Past crime records, social media sentiment analysis, meteorological data, and other sources of data can be used to feed this machine learning technique. Using machine learning, there are five phases to predicting crime. These are data gathering, data classification, pattern recognition, event prediction, and visualization. Using crime prediction technologies, law enforcement agencies can make better use of their limited resources. In this chapter, the authors show the importance of learning the principles of various policies on the dark web and cyber crimes, guiding new researchers through cutting-edge methodologies.