Data Behavioral Pattern Analysis and Noise Classification in Mobile Forensic


  • Preeti Dudhe Assistant Professor Department of Information technology Prof Ram Meghe Institute of technology & Research, badnera
  • S. R. Gupta Assistant Professor Department of Computer science and engineering Prof Ram Meghe Institute of technology & Research, badnera


Data analysis of forensic evidence, criminal profiling, analysis of behavioral evidence


Using data gathered from mobile devices and suspicious pattern detection algorithms, Mobile Forensic Data Analysis seeks to identify criminals. When criminal activity is associated with robotic processes like malware distribution, it is easy to foresee. When human behavior is involved, as in traditional crimes, prediction and detection become more alluring. Cyberbullying and small-scale drug sales, both of which rely heavily on mobile devices, are the focus of the current study, which proposes a combined criminal profiling and suspicious pattern identification methodology. Our evidence-based method improves accuracy and decreases false positives on a dataset by linking several observed patterns. After evaluating and displaying the results of tests conducted on a dataset that includes both benign and malicious traffic, the scenarios are re-run on a real dataset for further testing and verification.


Download data is not yet available.


Okmi, M.; Por, L.Y.; Ang, T.F.; Ku, C.S. Mobile Phone Data: A Survey of Techniques, Features, and Applications. Sensors 2023, 23, 908.

Lanza, G.; Pucci, P.; Carboni, L.; Vendemmia, B. Impacts of the Covid-19 pandemic in inner areas. Remote work and near-home tourism through mobile phone data in Piacenza Apennine. TEMA 2022, 2, 73–89.

Zhang, A.; Bradford, B.; Morgan, R.M.; Nakhaeizadeh, S. Investigating the uses of mobile phone evidence in China criminal proceedings. Sci. Justice 2022, 62, 385–398.

Wu, J.; Abrar, S.M.; Awasthi, N.; Frias-Martinez, E.; Frias-Martinez, V. Enhancing short-term crime prediction with human mobility flows and deep learning architectures. EPJ Data Sci. 2022, 11, 53.

Xu, Y.; Li, J.; Xue, J.; Park, S.; Li, Q. Tourism geography through the lens of time use: A computational framework using fine-grained mobile phone data. Ann. Am. Assoc. Geogr. 2021, 111, 1420–1444.

Rummens, A.; Snaphaan, T.; Van de Weghe, N.; Van den Poel, D.; Pauwels, L.J.; Hardyns, W. Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime? ISPRS Int. J. Geo-Inf. 2021, 10, 369.

Casino, F.; Dasaklis, T.K.; Spathoulas, G.P.; Anagnostopoulos, M.; Ghosal, A.; Borocz, I.; Solanas, A.; Conti, M.; Patsakis, C. Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews. IEEE Access 2022, 10, 25464–25493.

Rizvi, S.; Scanlon, M.; McGibney, J.; Sheppard, J. Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions. IEEE Access 2022, 10, 110362–110384.

Pawlick, J.; Chen, J.; Zhu, Q. iSTRICT: An Interdependent Strategic Trust Mechanism for the Cloud-Enabled Internet of Controlled Things. IEEE Trans. Inf. Forensics Secur. 2022, 14, 1654–1669. Available online: (accessed on 1 February 2023).

Studiawan, H.; Salimi, R.N.; Ahmad, T. Forensic Analysis of Copy-Move Attack; Springer International Publishing: Cham, Switzerland, 2021.

Javed, A.R.; Ahmed, W.; Alazab, M.; Jalil, Z.; Kifayat, K.; Gadekallu, T.R. A Comprehensive Survey on Computer Forensics: State-of-the-Art, Tools, Techniques, Challenges, and Future Directions. IEEE Access 2022, 10, 11065–11089.

Heartfield, R.; Loukas, G.; Bezemskij, A.; Panaousis, E. Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning. IEEE Trans. Inf. Forensics Secur. 2021, 16, 1720–1735.

Ashraf, N.; Mehmood, D.; Obaidat, M.A.; Ahmed, G.; Akhunzada, A. Criminal Behavior Identification Using Machine Learning Techniques Social Media Forensics. Electronics 2022, 11, 3162.

Bibi, M.; Abbasi, W.A.; Aziz, W.; Khalil, S.; Uddin, M.; Iwendi, C.; Gadekallu, T.R. A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis. Pattern Recognit. Lett. 2022, 158, 80–86.

Ali, M.L.; Thakur, K.; Obaidat, M.A. A Hybrid Method for Keystroke Biometric User Identification. Electronics 2022, 11, 2782.

Gundluru, N.; Rajput, D.S.; Lakshmanna, K.; Kaluri, R.; Shorfuzzaman, M.; Uddin, M.; Rahman Khan, M.A. Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model. Comput. Intell. Neurosci. 2022, 2022, 8512469.

Nadia, T.; Obaidat, M.A.; Rawashdeh, M.; Bsoul, A.K.; Al Zamil, M.G. A Novel Feature-Selection Method for Human Activity Recognition in Videos. Electronics 2022, 11, 732.

Long, D.; Liu, L.; Xu, M.; Feng, J.; Chen, J.; He, L. Ambient population and surveillance cameras: The guardianship role in street robbers’ crime location choice. Cities 2021, 115, 103223.

Long, D.; Liu, L. Do Migrant and Native Robbers Target Different Places? ISPRS Int. J. Geo-Inf. 2021, 10, 771

Hu, X.; Chen, H.; Liu, S.; Jiang, H.; Chu, G.; Li, R. BTG: A Bridge to Graph machine learning in telecommunications fraud detection. Future Gener. Comput. Syst. 2022, 137, 274–287.

Xing, J.; Yu, M.; Wang, S.; Zhang, Y.; Ding, Y. Automated fraudulent phone call recognition through deep learning. Wirel. Commun. Mob. Comput. 2020, 2020, 8853468.

Chu, G.; Wang, J.; Qi, Q.; Sun, H.; Tao, S.; Yang, H.; Liao, J.; Han, Z. Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks. IEEE Trans. Dependable Secur. Comput. 2022, 1–13.

Ana Rodriguez, Kristinsdóttir María, Pekka Koskinen Pieter van der Meer, Thomas Müller. Machine Learning Techniques for Multi-criteria Decision Making in Decision Science. Kuwait Journal of Machine Learning, 2(4). Retrieved from

Dasari, S. ., Reddy, A. R. M. ., & Reddy , B. E. . (2023). KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 01–11.

Raghavendra, S., Dhabliya, D., Mondal, D., Omarov, B., Sankaran, K.S., Dhablia, A., Chaudhury, S., Shabaz, M. Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises (2023) IET Communications, 17 (13), pp. 1619-1625.




How to Cite

Dudhe, P. ., & Gupta, S. R. . (2023). Data Behavioral Pattern Analysis and Noise Classification in Mobile Forensic . International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 223–227. Retrieved from



Research Article