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  • P-ISSN1738-3110
  • E-ISSN2093-7717
  • SCOPUS, ESCI

A Blocking Distribution Channels to Prevent Illegal Leakage in Supply Chain using Digital Forensic

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2022, v.20 no.7, pp.107-117
https://doi.org/https://doi.org/10.15722/jds.20.07.202207.107
HWANG, Jin-Hee

Abstract

Purpose: The scope of forensic investigations serves to identify malicious activities, including leakage of crucial corporate information. The investigations also identify security lapses in available networks. The purpose of the present study is to explore how to block distribution channels to protect illegal leakage in supply chain through digital forensic method. Research design, data and methodology: The present study conducted the qualitative textual analysis and its data collection process entails five steps: identifying and collecting data, determining coding categories, coding the content, checking validity and reliability, and analyzing and presenting the results. This methodology is a significant research method due to its high quality of previous resources. Results: Applying previous literature analysis to the results of this study, the author figured out that there are four solutions as an evidences to block distribution channels, preventing illegal leakage regarding company information. The following subtitles show clear solutions: (1) Communicate with Stakeholders, (2) Preventing and addressing illegal leakage, (3) Victims of Data Breach, (4) Focusing Solely on Technical Teams. Conclusion: There are difficult scenarios that continue to introduce difficult questions surrounding engagement with digital evidence. Consequently, it is important to enhance data handling to provide answers for organizations that suffer due to illegal leakages of sensitive information.

keywords
Digital Forensic, Supply Chain Management, Distribution Channel, Qualitative Approach

Reference

1.

Adams, R. B., Hobbs, V., & Mann, G. (2013). The advanced data acquisition model (ADAM): A process model for digital forensic practice. JDFSL: The Journal of Digital Forensics, Security and Law, 8(4), 25-48.

2.

Ali, M. D., & Kaur, D. (2020). Byod cyber forensic eco-system. International Journal of Advanced Research in Engineering and Technology, 11(9), 417-437.

3.

Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2016). A survey on data leakage prevention systems. Journal of Network and Computer Applications, 62(February), 137-152.

4.

Assarroudi, A., Heshmati Nabavi, F., Armat, M. R., Ebadi, A., &Vaismoradi, M. (2018). Directed qualitative content analysis:the description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42-55.

5.

Baig, Z. A., Szewczyk, P., Valli, C., Rabadia, P., Hannay, P., Chernyshev, M., & Peacock, M. (2017). Future challenges for smart cities: Cyber-security and digital forensics. Digital Investigation, 22(September), 3-13.

6.

Bollam, N., & Malsoru, M. V. (2011). Review on Data Leakage Detection. International Journal of Engineering Research and Applications, 1(3), 1088-1091.

7.

Bulbul, H. I., Yavuzcan, H. G., & Ozel, M. (2013). Digital forensics:an analytical crime scene procedure model (ACSPM). Forensic science international, 233(1-3), 244-256.

8.

Caviglione, L., Wendzel, S., & Mazurczyk, W. (2017). The future of digital forensics: Challenges and the road ahead. IEEE Security & Privacy, 15(6), 12-17.

9.

Chen, H., Huang, X., & Li, Z. (2022). A content analysis of Chinese news coverage on COVID-19 and tourism. Current Issues in Tourism, 25(2), 198-205.

10.

Cheng, L., Liu, F., & Yao, D. (2017). Enterprise data breach: causes, challenges, prevention, and future directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(5), 1-14.

11.

Collie, J. (2018). A strategic model for forensic readiness. Athens Journal of Sciences, 5(2), 167-182.

12.

Daryabar, F., Dehghantanha, A., Udzir, N. I., bin Shamsuddin, S., & Norouzizadeh, F. (2013). A survey about impacts of cloud computing on digital forensics. International Journal of Cyber-Security and Digital Forensics, 2(2), 77-95.

13.

Dezfoli, F. N., Dehghantanha, A., Mahmoud, R., Sani, N. F. B. M., & Daryabar, F. (2013). Digital forensic trends and future. International Journal of Cyber-Security and Digital Forensics, 2(2), 48-77.

14.

Esposito, C., Castiglione, A., Martini, B., & Choo, K. K. R. (2016). Cloud manufacturing: security, privacy, and forensic concerns. IEEE Cloud Computing, 3(4), 16-22.

15.

Gaur, A., & Kumar, M. (2018). A systematic approach to conducting review studies: An assessment of content analysis in 25 years of IB research. Journal of World Business, 53(2), 280-289.

16.

Guevara, C., Santos, M., & Lopez, V. (2017). Data leakage detection algorithm based on task sequences and probabilities. Knowledge-Based Systems, 120(March), 236-246.

17.

Hong, J. H. (2021). A Global Strategy of a Company that Uses Culture Content as its Core Business. The Journal of Industrial Distribution & Business, 12(6), 37-46.

18.

Jain, M., & Lenka, S. K. (2016). A review on data leakage prevention using image steganography. International Journal of Computer Science Engineering, 5(2), 56-59.

19.

Kang, E. (2021). Qualitative Content Approach: Impact of Organizational Climate on Employee Capability. East Asian Journal of Business Economics, 9(4), 57-67.

20.

Karie, N. M., & Venter, H. S. (2015). Taxonomy of challenges for digital forensics. Journal of forensic sciences, 60(4), 885-893.

21.

Katz, G., Elovici, Y., & Shapira, B. (2014). CoBAn: A contextbased model for data leakage prevention. Information sciences, 262(March), 137-158.

22.

Kebande, V. R., & Venter, H. S. (2018). Novel digital forensic readiness technique in the cloud environment. Australian Journal of Forensic Sciences, 50(5), 552-591.

23.

Khan, S., Gani, A., Wahab, A. W. A., Shiraz, M., & Ahmad. I. (2016). Network forensics: Review, taxonomy, and open challenges. Journal of Network and Computer Applications, 66(May), 214-235.

24.

Kim, J., Lee, C., & Chang, H. (2020). The Development of a Security Evaluation Model Focused on Information Leakage Protection for Sustainable Growth. Sustainability, 12(24), 10639.

25.

Krishnan, S., & Shashidhar, N. (2021). Interplay of Digital Forensics in eDiscovery. International Journal of Computer Science and Security, 15(2), 19-44.

26.

Kruse II, W. G., & Heiser, J. G. (2001). Computer forensics:incident response essentials. London, United Kingdom:Pearson Education.

27.

Lee, J. H. (2021). Effect of Sports Psychology on Enhancing Consumer Purchase Intention for Retailers of Sports Shops:Literature Content Analysis. Journal of Distribution Science, 19(4), 5-13.

28.

Liu, S., & Kuhn, R. (2010). Data loss prevention. IT professional, 12(2), 10-13.

29.

Losavio, M. M., Chow, K. P., Koltay, A., & James, J. (2018). The Internet of Things and the Smart City: Legal challenges with digital forensics, privacy, and security. Security and Privacy, 1(3), 1-11.

30.

Monteith, S., Bauer, M., Alda, M., Geddes, J., Whybrow, P. C., &Glenn, T. (2021). Increasing Cybercrime Since the Pandemic:Concerns for Psychiatry. Current Psychiatry Reports, 23(4), 1-9.

31.

Nelson, B., Phillips, A., & Steuart, C. (2014). Guide to computer forensics and investigations. Boston, MA: Cengage Learning.

32.

Okereafor, K., & Djehaiche, R. (2020). A Review of Application Challenges of Digital Forensics. International Journal of Simulation Systems Science and Technology, 21(2), 351-357.

33.

Park, S., Kim, Y., Park, G., Na, O., & Chang, H. (2018). Research on digital forensic readiness design in a cloud computing-based smart work environment. Sustainability, 10(4), 1203.

34.

Patrucco, A. S., Luzzini, D., & Ronchi, S. (2017). Research perspectives on public procurement: Content analysis of 14years of publications in the journal of public procurement. Journal of Public Procurement, 17(2), 229-269.

35.

Pichan, A., Lazarescu, M., & Soh, S. T. (2015). Cloud forensics:Technical challenges, solutions and comparative analysis. Digital investigation, 13(June), 38-57.

36.

Poyraz, O. I., Canan, M., McShane, M., Pinto, C. A., & Cotter, T. S. (2020). Cyber assets at risk: monetary impact of US personally identifiable information mega data breaches. The Geneva Papers on Risk and Insurance-Issues and Practice, 45(4), 616-638.

37.

Quick, D., & Choo, K. K. R. (2016). Big forensic data reduction:digital forensic images and electronic evidence. Cluster Computing, 19(2), 723-740.

38.

Ribeiro, L. E. (2019). High-profile data breaches: Designing the right data protection architecture based on the law, ethics and trust. Applied Marketing Analytics, 5(2), 146-158.

39.

Shabtai, A., Elovici, Y., & Rokach, L. (2012). Data leakage detection/prevention solutions. In A Survey of Data Leakage Detection and Prevention Solutions (pp. 17-37). Boston, MA:Springer.

40.

Simou, S., Kalloniatis, C., Kavakli, E., & Gritzalis, S. (2014). Cloud forensics: identifying the major issues and challenges. In International conference on advanced information systems engineering (pp. 271-284). Cham, Switzerland: Springer.

41.

Skalak, S. L., Golden, T. W., Clayton, M. M., & Pill, J. S. (2011). A guide to forensic accounting investigation. Hoboken, NJ :John Wiley & Sons.

42.

Sung, I. (2021). Interdisciplinary Literaure Analysis between Cosmetic Container Design and Customer Purchasing Intention. The Journal of Industrial Distribution & Business, 12(3), 21-29.

43.

Tabrizchi, H., & Rafsanjani, M. K. (2020). A survey on security challenges in cloud computing: issues, threats, and solutions. The journal of supercomputing, 76(12), 9493-9532.

44.

Vavilis, S., Petković, M., & Zannone, N. (2016). A severity-based quantification of data leakages in database systems. Journal of Computer Security, 24(3), 321-345.

45.

Wang, H., Wang, W., Sun, H., Cui, Z., Rahnamayan, S., & Zeng, S. (2017). A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 21(15), 4297-4307.

46.

Woo, E. J., & Kang, E. (2021). The effect of environmental factors on customer's environmental protection pattern: An empirical text analysis in the literature. International Journal of Environmental Sciences, 7(1), 1-15.

47.

Yuan, J., & Yu, S. (2015). Public integrity auditing for dynamic data sharing with multiuser modification. IEEE Transactions on Information Forensics and Security, 10(8), 1717-1726.

48.

Zawoad, S., & Hasan, R. (2016). Trustworthy digital forensics in the cloud. Computer, 49(3), 78-81.

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