Conference Paper

Olha Mykhaylova , Taras Fedynyshyn , Volodymyr Sokolov , Roman Kyrychok

The research problem addressed in the paper centers around the difficulty of identifying Persons of Interest (POIs) in law enforcement activity due to the vast amount of data stored on mobile devices. Given the complexity and volume of mobile forensic data, traditional analysis methods are often insufficient. The paper proposes leveraging Artificial Intelligence (AI) techniques, including machine learning and natural language processing, to improve the efficiency and effectiveness of data analysis in mobile forensics. This approach aims to overcome the limitations of manual data examination and enhance the identification process of POIs in a forensically sound manner. The main objective of the study is to explore and demonstrate the effectiveness of Artificial Intelligence techniques in improving the identification of POIs from mobile forensic data. The study proposes AI-driven approaches, particularly machine learning, and natural language processing, which can significantly enhance the efficiency, accuracy, and depth of analysis in mobile forensics, thereby addressing the challenges of handling vast amounts of data and the complexity of modern digital evidence. The study employs a quantitative research design, utilizing AI algorithms to process mobile forensic data from simulated environments. The study particularly demonstrates how deep learning can be utilized for searching POIs in WhatsApp messenger data. The result of the experiment shows that using AI for face recognition may throw false positive results, which means humans can’t be replaced in the stage of AI evolution. Also, results emphasize that using AI is helpful in mobile forensics data analysis and followed 88% of successful face recognition. The findings underscore the transformative potential of AI in mobile forensics, highlighting its capacity to enhance investigative accuracy and efficiency. This advancement could lead to more effective law enforcement and judicial processes by enabling quicker identification of POIs with higher precision. Moreover, the research underscores the importance of addressing ethical and privacy concerns in the application of AI technologies in forensic investigations, suggesting a balanced approach to leverage AI benefits while safeguarding individual rights.

AI; Artificial intelligence; deep learning; mobile forensics; person of interest; POI

Neural Network; Wireless Sensor Network; Quality of Service

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2024 Cybersecurity Providing in Information and Telecommunication Systems (CPITS)

28 February 2024 Kyiv, Ukraine

First Online: 20 March 2024

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• ISSN: 1613-0073

• EID: 2-s2.0-85189143222

• URN: urn:nbn:de:0074-3654-7

• DBLP: conf/cpits/MykhaylovaFSK24

• KUBG: 48576

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Mykhaylova, O. Fedynyshyn, T., Sokolov, V., & Kyrychok, R. (2024). Person-of-Interest Detection on Mobile Forensics Data—AI-Driven Roadmap. In Workshop on Cybersecurity Providing in Information and Telecommunication Systems (CPITS) (Vol. 3654, pp. 239–251).

O. Mykhaylova, T. Fedynyshyn, V. Sokolov, and R. Kyrychok, “Person-of-Interest Detection on Mobile Forensics Data—AI-Driven Roadmap,” Workshop on Cybersecurity Providing in Information and Telecommunication Systems (CPITS), vol. 3654, pp. 239–251, 2024.

O. Mykhaylova, et al., Person-of-Interest Detection on Mobile Forensics Data — AI-Driven Roadmap, in: Cybersecurity Providing in Information and Telecommunication Systems, vol. 3654 (2024) 239–251.