Improving Cybercrime Detection and Digital Forensics Investigations with Artificial Intelligence

• URL: http://arxiv.org/abs/2510.14638v1
• Date: Thu, 16 Oct 2025 12:53:36 GMT
• Title: Improving Cybercrime Detection and Digital Forensics Investigations with Artificial Intelligence
• Authors: Silvia Lucia Sanna, Leonardo Regano, Davide Maiorca, Giorgio Giacinto,
• Abstract summary: We show how cybercrime analysis and df procedures can take advantage of AI.<n>On the other hand, cybercriminals can use these systems to improve their skills, bypass automatic detection, and develop advanced attack techniques.
• Score: 1.1666234644810893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: According to a recent EUROPOL report, cybercrime is still recurrent in Europe, and different activities and countermeasures must be taken to limit, prevent, detect, analyze, and fight it. Cybercrime must be prevented with specific measures, tools, and techniques, for example through automated network and malware analysis. Countermeasures against cybercrime can also be improved with proper \df analysis in order to extract data from digital devices trying to retrieve information on the cybercriminals. Indeed, results obtained through a proper \df analysis can be leveraged to train cybercrime detection systems to prevent the success of similar crimes. Nowadays, some systems have started to adopt Artificial Intelligence (AI) algorithms for cyberattack detection and \df analysis improvement. However, AI can be better applied as an additional instrument in these systems to improve the detection and in the \df analysis. For this reason, we highlight how cybercrime analysis and \df procedures can take advantage of AI. On the other hand, cybercriminals can use these systems to improve their skills, bypass automatic detection, and develop advanced attack techniques. The case study we presented highlights how it is possible to integrate the use of the three popular chatbots {\tt Gemini}, {\tt Copilot} and {\tt chatGPT} to develop a Python code to encode and decoded images with steganographic technique, even though their presence is not an indicator of crime, attack or maliciousness but used by a cybercriminal as anti-forensics technique.

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