Abstract
In this chapter, we introduce the concepts of digital forensics and experiential learning, and describe how we implement experiential learning in an undergraduate level digital forensic course and a graduate level digital forensic course. The students were given the option of working on either a digital forensic research topic or the Digital Forensic Research Workshop (DFRWS) IoT Forensic Challenge (2018–2019). We also report on the artifacts generated by the students working on both types of final projects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
According to the American Academy of Forensic Sciences (AAFS; see https://www.aafs.org/about-aafs/sections/, Digital and Multimedia Sciences is been recognized as one of the 11 sub-disciplines in forensic sciences (Anthropology, Criminalistics, Engineering Sciences, General, Jurisprudence, Odontology, Pathology/Biology, Psychiatry and Behavioral Science, Questioned Documents, and Toxicology are the other ten forensic science sub-disciplines).
- 2.
- 3.
- 4.
- 5.
- 6.
References
Ahsan, M.M., Wahab, A.W.A., Idris, M.Y.I., Khan, S., Bachura, E., Choo, K.-K.R.: Class: cloud log assuring soundness and secrecy scheme for cloud forensics. IEEE Trans. Sustain. Comput. (2018)
Barmpatsalou, K., Cruz, T., Monteiro, E., Simões, P.: Current and future trends in mobile device forensics: a survey. ACM Comput. Surv. 51(3), 46:1–46:31 (2018). https://doi.org/10.1145/3177847
Cloyd, T., Osborn, T., Ellingboe, B., Glisson, W.B., Choo, K.R.: Browser analysis of residual facebook data. In: 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering, Trustcom/Bigdatase 2018, New York, NY, USA, 1–3 August 2018, pp. 1440–1445 (2018). https://doi.org/10.1109/Trust-Com/BigDataSE.2018.00200
Coker, J.S., Heiser, E., Taylor, L., Book, C.: Impacts of experiential learning depth and breadth on student outcomes. J. Exp. Educ. 40(1), 5–23 (2017)
DOrazio, C., Ariffin, A., Choo, K.-K.R.: IoS anti-forensics: how can we securely conceal, delete and insert data? In: 2014 47th Hawaii International Conference on System Sciences, pp. 4838–4847 (2014)
D’Orazio, C.J., Choo, K.R.: Circumventing IoS security mechanisms for APT forensic investigations: a security taxonomy for cloud apps. Future Gener. Comput. Syst. 79, 247–261 (2018). https://doi.org/10.1016/j.future.2016.11.010
Eterovic-Soric, B., Choo, K.-K.R., Mubarak, S., Ashman, H.: Windows 7 antiforensics: a review and a novel approach. J. Forensic Sci. 62(4), 1054–1070 (2017)
Guo, H., Hou, J.: Review of the accreditation of digital forensics in China. Forensic Sci. Res. 3(3), 194–201 (2018)
Horst, L.V.D., Choo, K.R., Le-Khac, N.: Process memory investigation of the Bitcoin clients electrum and Bitcoin core. IEEE Access 5, 22385–22398 (2017). https://doi.org/10.1109/ACCESS.2017.2759766
Khan, S., Gani, A., Wahab, A.W.A., Bagiwa, M.A., Shiraz, M., Khan, S.U., Zomaya, A.Y.: Cloud log forensics: foundations, state of the art, and future directions. ACM Comput. Surv. 49(1), 7:1–7:42 (2016). https://doi.org/10.1145/2906149
Kolb, A.Y., Kolb, D.A.: Learning styles and learning spaces: enhancing experiential learning in higher education. Acad. Manag. Learn. Educ. 4(2), 193–212 (2005)
Kolb, A.Y., Kolb, D.A.: Experiential learning theory. In: Encyclopedia of the Sciences of Learning, pp. 1215–1219. Springer, Berlin (2012)
Konak, A., Clark, T.K., Nasereddin, M.: Using Kolb’s experiential learning cycle to improve student learning in virtual computer laboratories. Comput. Educ. 72, 11–22 (2014)
Leom, M.D., Choo, K.-K.R., Hunt, R.: Remote wiping and secure deletion on mobile devices: a review. J. Forensic Sci. 61(6), 1473–1492 (2016)
Lin, X.: File carving. In: Introductory Computer Forensics, pp. 211–233. Springer, Berlin (2018)
Mata, N., Beebe, N., Choo, K.R.: Are your neighbors Swingers or Kinksters? Feeld app forensic analysis. In: 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering, Trustcom/Bigdatase 2018, New York, NY, USA, 1–3 August 2018, pp. 1433–1439 (2018). https://doi.org/10.1109/Trust-Com/BigDataSE.2018.00199
My, A., Rights, C., Justice, S.: American bar association. Perspectives (2018)
Quick, D., Choo, K.-K.R.: Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit. Investig. 11(4), 273–294 (2014)
Quick, D., Choo, K.R.: Pervasive social networking forensics: intelligence and evidence from mobile device extracts. J. Netw. Comput. Appl. 86, 24–33 (2017). https://doi.org/10.1016/j.jnca.2016.11.018
Quick, D., Choo, K.R.: Big Digital Forensic Data - Volume 1: Data Reduction Framework and Selective Imaging. Springer, Berlin (2018). https://doi.org/10.1007/978-981-10-7763-0
Quick, D., Choo, K.R.: Big Digital Forensic Data - Volume 2: Quick Analysis for Evidence and Intelligence. Springer, Berlin (2018). https://doi.org/10.1007/978-981-13-0263-3
Quick, D., Choo, K.R.: IoT device forensics and data reduction. IEEE Access 6, 47566–47574 (2018). https://doi.org/10.1109/ACCESS.2018.2867466
Rondeau, C.M., Temple, M.A., Lopez, J.: Industrial IoT cross-layer forensic investigation. In: Wiley Interdisciplinary Reviews: Forensic Science, p. e1322 (2019)
Shetty, R., Grispos, G., Choo, K.R.: Are you dating danger? an interdisciplinary approach to evaluating the (in)security of android dating apps. IEEE Trans. Sustain. Comput. (2019). https://doi.org/10.1109/TSUSC.2017.2783858
Shi, K., Xu, M., Jin, H., Qiao, T., Yang, X., Zheng, N., Choo, K.R.: A novel file carving algorithm for national marine electronics association (NMEA) logs in GPS forensics. Digit. Investig. 23, 11–21 (2017). https://doi.org/10.1016/j.diin.2017.08.004
Smith, C., Dietrich, G., Choo, K.R.: Identification of forensic artifacts in VMWare virtualized computing. In: Security and Privacy in Communication Networks - Securecomm 2017 International Workshops, ATCS and Sepriot, Niagara Falls, Canada, 22–25 October 2017, Proceedings, pp. 85–103 (2017). https://doi.org/10.1007/978-3-319-78816-6_7
Stamm, M.C., Liu, K.J.R.: Anti-forensics of digital image compression. IEEE Trans. Inf. Forensics Secur. 6(3–2), 1050–1065 (2011). https://doi.org/10.1109/TIFS.2011.2119314
Volety, T., Saini, S., McGhin, T., Liu, C.Z., Choo, K.R.: Cracking Bitcoin wallets: i want what you have in the wallets. Future Gener. Comput. Syst. 91, 136–143 (2019). https://doi.org/10.1016/j.future.2018.08.029
Zhang, X., Hu, L., Song, S., Xie, Z., Meng, X., Zhao, K.: Windows volatile memory forensics based on correlation analysis. J. Netw. 9(3), 645–653 (2014)
Zhang, X., Breitinger, F., Baggili, I.: Rapid android parser for investigating DEX files (RAPID). Digit. Investig. 17, 28–39 (2016)
Zhang, X., Baggili, I., Breitinger, F.: Breaking into the vault: privacy, security and forensic analysis of android vault applications. Comput. Secur. 70, 516–531 (2017)
Zhang, X., Grannis, J., Baggili, I., Beebe, N.L.: Frameup: an incriminatory attack on Storj: a peer to peer blockchain enabled distributed storage system. Digit. Investig. (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zhang, X., Yuen, T.T., Choo, KK.R. (2020). Experiential Learning in Digital Forensics. In: Zhang, X., Choo, KK. (eds) Digital Forensic Education. Studies in Big Data, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-030-23547-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-23547-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23546-8
Online ISBN: 978-3-030-23547-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)