Abstract
Criminal investigation plays a vital role nowadays where the law enforcement agencies (LEAs) carry out this critical mission thoroughly and competently. However, such complicated mission involves a broad spectrum of tasks including collecting evidences from various data sources, analyzing them, and eventually identifying the criminals. Particularly, data may be collected by LEAs from telecommunication companies, online money transfer agencies, social media networks, video surveillance systems, bank transactions, and airways companies. LEAs confront various challenges from different fronts regarding criminal investigation. Thus, handling such big and heterogeneous data coming from different sources and recognizing potential suspects in a real-time is becoming a major challenge for LEAs in criminal investigation. In this paper, we propose an end-to-end Intelligent framework, called as ICAD, to help LEAs in Criminal Analytics and Detection. Mainly, ICAD uses cutting-edge technologies (data science and big data tools) as well as ontological models and inference rules to automatically identify suspects and reduce the human intervention in the investigation process. Furthermore, ICAD consists of four phases. The data sources phase in which we take benefits of various data collection sources that are essential in the crime investigation process. The data acquisition phase where data are collected, preprocessed, and stored using data science tools. The model phase in which a criminal-based ontology is defined that semantically integrates and enriches real-time data into useful information. The last phase is the knowledge extraction where a set of inference and reasoning rules are defined and applied over the ontology to detect criminals according to their activities.
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References
Abba, E., Aibinu, A., Alhassan, J.: Development of multiple mobile networks call detailed records and its forensic analysis. Digit. Commun. Netw. 5(4), 256–265 (2019)
Abdallah, R., Benbernou, S., Taher, Y., Younas, M., Haque, R.: A smart video surveillance system for helping law enforcement agencies in detecting knife related crimes. In: Awan, I., Younas, M., Bentahar, J., Benbernou, S. (eds.) DBB 2022. LNNS, vol. 541, pp. 65–78. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16035-6_6
Abuhamoud, N., Geepalla, E.: A study of using big data and call detail records for criminal investigation. J. Pure Appl. Sci. 18(4) (2019)
Amjad, K., Malik, A.A., Mehta, S.: A technique and architectural design for criminal detection based on Lombroso theory using deep learning. Lahore Garrison Univ. Res. J. Comput. Sci. Inf. Technol. 4(3), 47–63 (2020)
Barhamgi, M., Masmoudi, A., Lara-Cabrera, R., Camacho, D.: Social networks data analysis with semantics: application to the radicalization problem. J. Ambient Intell. Human. Comput. 1–15 (2018)
Burney, S.M.A., Arifeen, Q.U., Mahmood, N., Bari, S.A.K.: Suspicious call detection using Bayesian network approach. WSEAS Trans. Inf. Sci. Appl. 37–49
Carnaz, G., Nogueira, V.B., Antunes, M.: A graph database representation of Portuguese criminal-related documents. In: Informatics, vol. 8, p. 37. MDPI (2021)
Das, P., Das, A.K., Nayak, J., Pelusi, D., Ding, W.: A graph based clustering approach for relation extraction from crime data. IEEE Access 7, 101269–101282 (2019)
Karpova, A., Savelev, A., Vilnin, A., Kuznetsov, S.: Method for detecting far-right extremist communities on social media. Soc. Sci. 11(5), 200 (2022)
Lara-Cabrera, R., Gonzalez-Pardo, A., Barhamgi, M., Camacho, D.: Extracting radicalisation behavioural patterns from social network data. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), pp. 6–10. IEEE (2017)
Mushtaq, N., Ali, K., Moetesum, M., Siddiqi, I.: Impact of demographics on automated criminal tendency detection from facial images. In: 2022 International Conference on Frontiers of Information Technology (FIT), pp. 88–93. IEEE (2022)
Schiliro, F., Beheshti, A., Moustafa, N.: A novel cognitive computing technique using convolutional networks for automating the criminal investigation process in policing. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1250, pp. 528–539. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55180-3_39
Zaman, M., et al.: Execution of coordinate based classifier system to predict specific criminal behavior using regional multi person pose estimator. Ph.D. thesis, Brac University (2021)
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Abdallah, R., Harb, H., Taher, Y., Benbernou, S., Haque, R. (2023). ICAD: An Intelligent Framework for Real-Time Criminal Analytics and Detection. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_24
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DOI: https://doi.org/10.1007/978-981-99-7254-8_24
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