FORENSIC INVESTIGATION OF CALL DATA RECORD (CDR) USING STATISTICAL AND MAPPING TOOLS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.1665Keywords:
Call Data Record(CDR), Clustering, Cellular Network, Fraud Detection, Autofilter, Maptive, User Behaviour Analysis, Telecom, Logistic Regression, Mobility Analysis, Anomaly DetectionsAbstract [English]
As technology advances, it appears that the undesirable side effects do as well. It also results in increase in crime rates. It has been noted that many significant crimes are occurring with the use of only smartphones because they are convenient and simple to use due to their GUI characteristics. Police departments and many other law enforcement agencies have used CDR since many years till now to collect the evidence including both civil and criminal cases. People are naturally leaving their footprints behind them. Numerous studies on various aspects of CDR have been on-going for a decade. In this research, the researchers utilized the MS EXCEL and Maptive tools to simply and easily gather satellite- based imagery of a site's precise location in order to analyse CDR.
References
rd Eye | Home. (n.d.). http://www.3ets.in/cdr-analysis-tool-trap.php
Bianchi, F. M., Rizzi, A., Sadeghian, A., & Moiso, C. (2016). Identifying user habits through data mining on call data records. Engineering Applications of Artificial Intelligence, 54, 49–61. DOI: https://doi.org/10.1016/j.engappai.2016.05.007
CDR Analysis Software- Purple Radiance. (2022).
CDR BTS and Forensic Software. (2022). CDR Training | CDR Software | CDR Analysis | CDR Analysis Software | CDR Analysis Investigation | Mobile CDR Analysis.
Chen, G., Hoteit, S., Viana, A., Fiore, M., & Sarraute, C. (2016). Relevance of Context for the Temporal Completion of Call Detail Record.
Chen, N. C., Xie, W., Welsch, R. E., Larson, K., & Xie, J. (2017). Comprehensive Predictions of Tourists’ Next Visit Location Based on Call Detail Records Using Machine Learning and Deep Learning Methods. Proceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017, 1–6. DOI: https://doi.org/10.1109/BigDataCongress.2017.10
Dash, M., Koo, K. K., Gomes, J. B., Krishnaswamy, S. P., Rugeles, D., & Shi-Nash, A. (2015). Next place prediction by understanding mobility patterns. 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015, 469–474. DOI: https://doi.org/10.1109/PERCOMW.2015.7134083
Doyle, C., Herga, Z., Dipple, S., Szymanski, B. K., Korniss, G., & Mladenić, D. (2019). Predicting complex user behavior from CDR based social networks. Information Sciences, 500, 217–228. DOI: https://doi.org/10.1016/j.ins.2019.05.082
Khan, F. H., Ali, M. E., & Dev, H. (2015). A hierarchical approach for identifying user activity patterns from mobile phone call detail records. Proceedings of 2015 International Conference on Networking Systems and Security, NSysS 2015, February. DOI: https://doi.org/10.1109/NSysS.2015.7043535
Kujala, R., Aledavood, T., & Saramäki, J. (2016). Estimation and monitoring of city- ity travel times using call detail records. EPJ Data Science, 5(1). DOI: https://doi.org/10.1140/epjds/s13688-016-0067-3
Kumar, M., Hanumanthappa, M., & Kumar, T. V. S. (2017). Crime investigation and criminal network analysis using archive call detail records. 2016 8th International Conference on Advanced Computing, ICoAC 2016, 46–50. DOI: https://doi.org/10.1109/ICoAC.2017.7951743
Leng, Y. (2016). Urban computing using call detail records : mobility pattern mining, next-location prediction and location recommendation.
LIS CDR Analysis Tool | | Cell Id Finder | Avenging Security. (2022).
Liu, Q., Wu, S., Wang, L., & Tan, T. (2016). Predicting the next location: A recurrent model with spatial and temporal contexts. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 194–200. DOI: https://doi.org/10.1609/aaai.v30i1.9971
Sikder, R., Uddin, M. J., & Halder, S. (2017). An efficient approach of identifying tourist by call detail record analysis. IWCI 2016 - 2016 International Workshop on Computational Intelligence, May 2017, 136–141. DOI: https://doi.org/10.1109/IWCI.2016.7860354
Zhao, Zhan, Koutsopoulos, H. N., & Zhao, J. (2021). Identifying Hidden Visits from Sparse Call Detail Record Data. 1–17. DOI: https://doi.org/10.1177/27541231221124164
Zhao, Ziliang, Shaw, S. L., Xu, Y., Lu, F., Chen, J., & Yin, L. (2016). Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science, 30(9), 1738. DOI: https://doi.org/10.1080/13658816.2015.1137298
Zhang, Daqiang, Athanasios V. Vasilakos, and Haoyi Xiong. ”Predicting location using mobile phone calls.” ACM SIGCOMM Computer Communication Review 42, no. 4 (2012): 295-296. DOI: https://doi.org/10.1145/2377677.2377738
Palchykov V, Mitrovic M, Jo H-H, Saramaki J, Pan RK (2014) Inferring human mobility using communication patterns. Sci Rep 4:6174 DOI: https://doi.org/10.1038/srep06174
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Copyright (c) 2024 Gouri Rajendra Uplenchwar, Rahul Kailas Bharati, Dr. Shobha Kamalakar Bawiskar
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