DIGITAL FORENSICS:AN INVESTIGATION ON CYBER BULLYING ACTIVITIES USING MACHINE LEARNING ALGORITHM

Cyberbullying has become a growing concern in today’s society, with more and more people turning to the internet to harass and intimidate others. Digital forensics is an essential tool for investigating cyberbullying activities, as it allows for the collection and analysis of digital evidence. However, traditional digital forensics techniques can be time-consuming and require a significant amount of human effort. In this paper, we propose the use of machine learning algorithms to aid in the investigation of cyberbullying activities. By training these algorithms on a dataset of known cyberbullying incidents, we can create a predictive model that can automatically classify new instances of cyberbullying. This can significantly reduce the time and effort required for investigations, allowing for a more efficient response to cyberbullying incidents. The challenges associated with using machine learning for cyberbullying detection, including the need for high-quality training data and the potential for bias in the algorithms. We also explore the various types of digital evidence that can be used in cyberbullying investigations, such as social media posts, emails, and instant messages. We present a case study in which we apply our proposed approach to a real-world cyberbullying incident. Our results show that the machine learning algorithm was able to accurately identify the cyberbullying activity with a high level of precision, demonstrating the potential of this approach for improving the efficiency and effectiveness of cyberbullying investigations.