Machine Learning and AI in Cyber Crime Detection
Chitrak Chakraborty, Sakalya Mitra · 2024
Globally, cybercrime is becoming a more serious menace to people, businesses, and governments. The growth of cyberthreats demands novel techniques to detect and mitigate in the field of cybersecurity. The sophistication of contemporary cyber threats is too great for conventional approaches to handle. Hence, it is quite critical at this stage of technological evolution to combine ML and AI to improve detection capabilities. This chapter provides an overview of a thorough investigation of the critical roles that artificial intelligence (AI) and machine learning (ML) play in strengthening cybercrime detection systems. This chapter’s primary goal is to evaluate the shortcomings of conventional cybercrime detection techniques and traditional methodologies, laying the groundwork for the necessity of embracing machine learning and artificial intelligence (AI) solutions. In addition, by exploring the various kinds of cybercrimes, an overview is laid out and categorization of cybercrimes is presented which essentially examine the effectiveness of machine learning (ML) models, such as supervised learning for anomaly detection, unsupervised learning for intrusion detection, and deep learning techniques like Convolutional and Recurrent neural networks. The difficulties that arise when implementing ML-based solutions, such as adversarial attacks, interpretability issues, and scalability issues, are closely examined. Solutions are provided for the same. Finally, ethical considerations, case studies, and other issues that are to be addressed in this context are other key areas. By promoting a proactive strategy for countering cyber threats in an era of growing digital complexity, this chapter seeks to advance knowledge of the mutually beneficial relationship between ML, AI, and cybersecurity.