SMART Model: A Robust Approach for Cyber Criminal Identification using Smartphone Data
Received: 20 June 2024 | Revised: 16 July 2024 and 31 July 2024 | Accepted: 18 August 2024 | Online: 2 December 2024
Corresponding author: K. Swetha
Abstract
The SMART (Smartphone Metadata Analysis for Recognizing Threats) model is a novel approach to the identification of prospective cyber criminals by analyzing smartphone data, with a particular emphasis on social media interactions, messages, and call logs. The SMART model, in contrast to conventional methods that depend on a wide variety of features, prioritizes critical parameters to ensure more precise and effective analysis. This model exhibits exceptional adaptability and robustness in a variety of data environments by employing sophisticated feature extraction and classification algorithms. This targeted approach not only improves the precision of threat identification but also offers a practicable solution for real-world cybersecurity applications, where data quality and consistency may vary.
Keywords:
Smartphone Data Analysis, SMART Model, Smartphone Applications, cyber scams, cyber attacksDownloads
References
A. Dimitriadis, E. Lontzetidis, B. Kulvatunyou, N. Ivezic, D. Gritzalis, and I. Mavridis, "Fronesis: Digital Forensics-Based Early Detection of Ongoing Cyber-Attacks," IEEE Access, vol. 11, pp. 728–743, 2023.
S. Nasiri, M. T. Sharabian, and M. Aajami, "Using Combined One-Time Password for Prevention of Phishing Attacks," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2328–2333, Dec. 2017.
J. Kumar and G. Ranganathan, "Malware Attack Detection in Large Scale Networks using the Ensemble Deep Restricted Boltzmann Machine," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11773–11778, Oct. 2023.
P. Bradford and N. Hu, "A layered approach to insider threat detection and proactive forensics," in Proceedings of the Twenty-First Annual Computer Security Applications Conference (Technology Blitz), 2005.
A. Orebaugh, "Proactive Forensics," Journal of Digital Forensic Practice, Mar. 2006.
J. Sachowski, Implementing Digital Forensic Readiness: From Reactive to Proactive Process, Second Edition, 2nd ed. Boca Raton, FL, USA: CRC Press, 2019.
B. D. Bryant and H. Saiedian, "A novel kill-chain framework for remote security log analysis with SIEM software," Computers & Security, vol. 67, pp. 198–210, Jun. 2017.
"MITRE ATT&CK®." https://attack.mitre.org/.
V. S. Harichandran, D. Walnycky, I. Baggili, and F. Breitinger, "CuFA: A more formal definition for digital forensic artifacts," Digital Investigation, vol. 18, pp. S125–S137, Aug. 2016.
A. Dimitriadis, "Leveraging digital forensics and information sharing into prevention, incident response, and investigation of cyber threats," Ph.D. dissertation, University of Macedonia, Thessaloniki, Greece, 2022.
B. L. Krishna, "Comparative Study of Fileless Ransomware," International Journal of Trend in Scientific Research and Development, vol. 4, no. 3, pp. 608–616, 2020.
H. Al-Mohannadi, Q. Mirza, A. Namanya, I. Awan, A. Cullen, and J. Disso, "Cyber-Attack Modeling Analysis Techniques: An Overview," in 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Vienna, Austria, Aug. 2016, pp. 69–76.
K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021.
A. Al-Marghilani, "Comprehensive Analysis of IoT Malware Evasion Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7495–7500, Aug. 2021.
K. Muppavaram, M. Sreenivasa Rao, K. Rekanar, and R. Sarath Babu, "How Safe Is Your Mobile App? Mobile App Attacks and Defense," in Proceedings of the Second International Conference on Computational Intelligence and Informatics, 2018, pp. 199–207.
"Home," UCO Community. https://www.unifiedcyberontology.org/.
"Pellet - Semantic Web Standards." https://www.w3.org/2001/sw/wiki/Pellet.
B. E. Strom et al., "Finding Cyber Threats with ATT&CKTM-Based Analytics," MITRE, Technical Report MTR170202, Jun. 2017. [Online]. Available: https://apps.dtic.mil/sti/trecms/pdf/AD1107945.pdf.
"ATT&CK Data & Tools | MITRE ATT&CK®." https://attack.mitre.org/resources/attack-data-and-tools/.
Downloads
How to Cite
License
Copyright (c) 2024 K. Swetha, K. Sivaraman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
