Medical Forensics Principles and Cyber Crime Forensic Investigation Model
DOI:
https://doi.org/10.48001/jowacs.2024.211-17Keywords:
Acquisition, Analysis, Chain of custody, Examination, Preservation, Principles, Seizure and isolationAbstract
The paper deals with medical principles of forensic and their proposed utilization in computer forensic. This paper explores how the medical/traditional forensic science law principles like individuality, exchange, progressive change, comparison analysis, facts do not lie are useful in cybercrime forensic investigation with forensic principles applicability cybercrime scenarios with case law. Lastly the research explores the Digital forensics methodology useful for the prosecution of digital criminals. The researchers have done extensive literature survey of the existing crime investigation forensic investigation frameworks proposed from 1995 to 2022 to identify the detailed activities in each process. Based on this collective information, their limitations and practical experience into the domain, the researchers proposed a robust digital forensic process model to obtain the digital evidence. The authors proposed the digital forensic investigation model with the algorithmic steps used in proposed phases of investigation model. Phase 1 starts the investigation process-readiness phase with procedure 1: digital forensic case management, procedure 2: investigation preparation (fir). Phase 2 seizer and isolation explore for communication shielding. Phase 3: data acquisition/duplication & deleted data recovery explore volatile data collection, non-volatile data, and evidence collection from network with widely used tools with respect to computing OS Infrastructure. The procedures explored in phase 2 are Procedure 3: DataAcquisition (), Procedure 4: Procedure EvidenceCollection () and Procedure 5 HashAlgorithms(). Phase 3 preservation and data security includes packaging, transportation and storage, Access Control and physical Safety. The procedure proposed in Phase3 are Procedure 6: Procedure DataTransfer(). Phase4 Identification, Examination create Procedure7 detailedIdentificationofData(). Phase 4 Digital forensic object Analysis(DFOA) explore analysis of data diagnosis and observe about What, Why, Who When, Where(5W, And How(1H) ? and identify the best deep learning algorithms for examination and analysis for building the investigation tool are identified. The researcher identifies three data bases namely evidence, attacker, and chain of custody for building the report using Procedure 9 Examinationandanalysis() and association of phases with access control, deep learning algorithms and chain of custody. The researcher proposed the design of the secure data structure design of chain of custody and identify the tools used in every phase of the crime investigation.
Downloads
References
Agarwal, S. (2013, December). Data mining: Data mining concepts and techniques. In 2013 International Conference on Machine Intelligence and Research Advancement (pp. 203-207). IEEE.
https://doi.org/10.1109/ICMIRA.2013.45.
Al-Garadi M. A., Varathan K. D., & Ravana S. D. (2016). Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Computers in Human Behavior, 63, 433-443. https://doi.org/10.1016/j.chb.2016.05.051.
Ali, M., Shiaeles, S., Clarke, N., & Kontogeorgis, D. (2019). A proactive malicious software identification approach for digital forensic examiners. Journal of Information Security and Applications, 47, 139-155. https://doi.org/10.1016/j.jisa.2019.04.013.
Bandu B. Meshram & Manish Kumar Singh. (2023). Research areas in cyber security and forensic and centre of excellence for forensic laboratory. Journal of Web Applications and Cyber Security, 1(1), 11-28. https://doi.org/10.48001/jowacs.2023.1111-28.
Barni, M., Nowroozi, E., & Tondi, B. (2018, June). Detection of adaptive histogram equalization robust against JPEG compression. In 2018 International Workshop on Biometrics and Forensics (IWBF) (pp. 1-8). IEEE. https://doi.org/10.1109/IWBF.2018.8401564.
Brown C. S. (2015). Investigating and prosecuting cyber crime: Forensic dependencies and barriers to justice. International Journal of Cyber Criminology, 9(1), 55.
https://cybercrimejournal.com/pdf/Brown2015vol9issue1.pdf.
Carroll, O. L., Brannon, S. K., & Song, T. (2008). Computer forensics: Digital forensic analysis methodology. US Att'ys Bull., 56, 1.
https://heinonline.org/HOL/Landing Page?handle=hein.journals/usab56&div=4&id=&page=.
Chhabra, G. S., Singh, V., & Singh, M. (2018). Hadoop‐based analytic framework for cyber forensics. International Journal of Communication Systems, 31(15), e3772.
https://doi.org/10.1002/dac.3772.
Durall, R., Keuper, M., Pfreundt, F. J., & Keuper, J. (2019). Unmasking deepfakes with simple features. arXiv preprint arXiv:1911.00686.
https://doi.org/10.48550/arXiv.1911.00686.
Ferreira, S., Antunes, M., & Correia, M. E. (2021). Exposing manipulated photos and videos in digital forensics analysis. Journal of Imaging, 7(7), 102.
https://doi.org/10.3390/jimaging7070102.
Gedam, M. N., & Meshram, B. B. (2021). Database private security jurisprudence: a case study using oracle. International Journal of Database Management Systems, 13(3), 1-21.
https://doi.org/10.5121/ijdms.2021.13301.
Hina, M., Ali, M., Javed, A. R., Srivastava, G., Gadekallu, T. R., & Jalil, Z. (2021, October). Email classification and forensics analysis using machine learning. In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCO M/UIC/ATC/IOP/SCI) (pp. 630-635). IEEE.
https://doi.org/10.1109/SWC50871.2021.00093.
Islam, M. M., Karmakar, G., Kamruzzaman, J., Murshed, M., Kahandawa, G., & Parvin, N. (2018, December). Detecting splicing and copy-move attacks in color images. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-7). IEEE.
https://doi.org/10.1109/DICTA.2018.8615874.
Jakobsson, M. (2017). Short paper: Addressing sophisticated email attacks. In Financial Cryptography and Data Security: 21st International Conference, FC 2017, Sliema, Malta, April 3-7, 2017, Revised Selected Papers 21 (pp. 310-317). Springer International Publishing.
https://doi.org/10.1007/978-3-319-70972-7_17.
Javed, A. R., Ahmed, W., Alazab, M., Jalil, Z., Kifayat, K., & Gadekallu, T. R. (2022). A comprehensive survey on computer forensics: State-of-the-art, tools, techniques, challenges, and future directions. IEEE Access, 10, 11065-11089. https://doi.org/10.1109/ACCESS.2022.3142508.
Kachavimath, A. V., Nazare, S. V., & Akki, S. S. (2020, March). Distributed denial of service attack detection using naive bayes and k-nearest neighbor for network forensics. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 711-717). IEEE.
https://doi.org/10.1109/ICIMIA48430.2020.9074929.
Lutui, R. (2016). A multidisciplinary digital forensic investigation process model. Business Horizons, 59(6), 593-604. https://doi.org/10.1016/j.bushor.2016.08.001.
Meshram B. B., & Singh M. K. (2023). Guarding the digital cyber realm of India: Navigating IPC 1860, bharatiya nyaya sanhita 2023 and ITA 2000 in the fight against cyber crime. IJRDO Journal of Law and Cyber Crime, 3(2), 1-15. http://13.234.104.160/index.php/lcc/article/view/5851.
Montasari, R., Carpenter, V., & Hill, R. (2019). A road map for digital forensics research: A novel approach for establishing the design science research process in digital forensics. International Journal of Electronic Security and Digital Forensics, 11(2), 194-224.
https://doi.org/10.1504/IJESDF.2019.098784.
Muda, A. K., Choo, Y. H., Abraham, A., & Srihari, S. N. (Eds.). (2014). Computational intelligence in digital forensics: Forensic investigation and applications. Springer International Publishing.
https://doi.org/10.1007/978-3-319-05885-6.
Overill, R. E., Silomon, J. A., Chow, K. P., & Tse, H. (2013, November). Quantification of digital Forensic hypotheses using probability theory. In 2013 8th International Workshop on Systematic Approaches to Digital Forensics Engineering (SADFE) (pp. 1-5). IEEE.
https://doi.org/10.1109/SADFE.2013.6911547.
Pandey A., & Saini J. R. (2014). Attacks & defense mechanisms for TCP/IP based protocols. International Journal of Engineering Innovations and Research, 3(1), 17.
https://www.researchgate.net/profile/Jatinderkumar-Saini/publication/2608 77113_Attacks_Defense_Mechanisms_for_TCP_IP_Based_Protocols/links/0deec5329544f94845000000/Attacks-Defense-Mechanisms-for-TCP-IP-Based-Protocols.pdf.
Pimenta Rodrigues, G. A., de Oliveira Albuquerque, R., Gomes de Deus, F. E., de Sousa Jr, R. T., de Oliveira Júnior, G. A., Garcia Villalba, L. J., & Kim, T. H. (2017). Cybersecurity and network forensics: Analysis of malicious traffic towards a honeynet with deep packet inspection. Applied Sciences, 7(10), 1082.
https://doi.org/10.3390/app7101082.
Roy, N. R., Khanna, A. K., & Aneja, L. (2016, April). Android phone forensic: Tools and techniques. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 605-610). IEEE. https://doi.org/10.1109/CCAA.2016.7813792.
Sabillon, R., Serra-Ruiz, J., Cavaller, V., & Cano, J. J. (2017). Digital forensic analysis of cybercrimes: Best practices and methodologies. International Journal of Information Security and Privacy (IJISP), 11(2), 25-37. https://doi.org/10.4018/IJISP.2017040103.
Shah, M. S. M. B., Saleem, S., & Zulqarnain, R. (2017). Protecting digital evidence integrity and preserving chain of custody. Journal of Digital Forensics, Security and Law, 12(2), 12.
https://doi.org/10.15394/jdfsl.2017.1478.
Triantafyllidis, A. K., & Tsanas, A. (2019). Applications of machine learning in real-life digital health interventions: Review of the literature. Journal of Medical Internet Research, 21(4), e12286.
https://doi.org/10.2196/12286.
Usman, N., Usman, S., Khan, F., Jan, M. A., Sajid, A., Alazab, M., & Watters, P. (2021). Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics. Future Generation Computer Systems, 118, 124-141.
https://doi.org/10.1016/j.future.2021.01.004.
Yudhana, A., Riadi, I., & Ridho, F. (2018). DDoS classification using neural network and naive bayes methods for network forensics. International Journal of Advanced Computer Science and Applications, 9(11). https://doi.org/10.14569/IJACSA.2018.091125.
Downloads
