Towards a Comprehensive Ontology Based-Investigation for Digital Forensics Cybercrime
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Cyber physical attacks against information and computer systems are a tangible and dangerous threat that requires an effective response. In this paper, digital forensics cybercrime ontology is proposed to collect, examine, analyze, prepare, acquire and preserve evidence of computer crimes of digital forensics in cyberspace. The power of the proposed ontology is to determine the difficulties of association of the digital crime types and their collection evidences in digital forensics cases. Ontology development has consists three main steps, 1) domain, purpose and scope setting, 2) important terms acquisition, classes and class hierarchy conceptualization and 3) instances creation. Digital forensics and ontology are two normally unrelated topics. Ontology congruent to this paper is method that will help to better understanding and defining terms of digital forensics. Our proposed digital forensics cybercrime ontology resulting from the Protégé has a total of 180 classes, 179 subclasses and 84 instances regarding digital forensics crime cases.
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E. Casey, Digital Evidence and Computer Crime: Forensics Science, Computers and the Internet: Academic press, Third Edition, 2011.
M. Pollitt and A. Whitledge, "Exploring Big Haystacks," In Advances in Digital Forensics II: Springer, 2006, pp. 67-76.
T. R. Gruber, "Toward Principles for the Design of Ontologies used for Knowledge Sharing," International Journal of Human-computer Studies, vol. 43, pp. 907-928, 1995.
B. Tsoumas, S. Dritsas, and D. Gritzalis, "An Ontology-based Approach to Information Systems Security Management," In Computer Network Security: Springer, 2005, pp. 151-164.
A. Talib, R. Atan, R. Abdullah, and M. Azmi, "Security Ontology Driven Multi Agent System Architecture for Cloud Data Storage Security: Ontology Development," International Journal of Computer Science and Network Security, vol. 12, pp. 63-72, 2012.
D. L. McGuinness and F. Van Harmelen, "OWL Web Ontology Language Overview," W3C Recommendation, vol. 10, p. 2004, 2004.
Jabar, M., Khalefa, M., Abdullah, R., Abdullah, S., Meta-Analysis of Ontology Software Development Process, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 29-37.
A. Brinson, A. Robinson, and M. Rogers, "A Cyber Forensics Ontology: Creating a New Approach to Studying Cyber Forensics," Digital Investigation, vol. 3, pp. 37-43, 2006.
H. Park, S. Cho, and H.-C. Kwon, "Cyber Forensics Ontology for Cyber Criminal Investigation," In Forensics in Telecommunications, Information and Multimedia: Springer, 2009, pp. 160-165.
A. M. Hoss and D. L. Carver, "Weaving Ontologies to Support Digital Forensics Analysis," In Proceedings of the 2009 IEEE international conference on Intelligence and security informatics, 2009, pp. 203-205.
X. D. BoJin and Y. W. H. Li, "Forensics in Telecommunications, Information, and Multimedia," In Third International ICST Conference, E-Forensics 2010, Shanghai, China, Springer, 2011.
D. C. Harrill and R. P. Mislan, "A Small Scale Digital Device Forensics Ontology," Small Scale Digital Device Forensics Journal, vol. 1, p. 242, 2007.
D. Kahvedzic and T. Kechadi, "DIALOG: A Framework for Modeling, Analysis and Reuse of Digital Forensics Knowledge," Digital Investigation, vol. 6, pp. S23-S33, 2009.
Narayana, S., Saradhi Varma, G., Govardhan, A., Discovering Relevant Semantic Associations Based on User Specified Context, (2015) International Review on Computers and Software (IRECOS), 10 (8), pp. 805-813.
Nagarajan, G., Thyagharajan, K., Rule-Based Semantic Content Extraction in Image using Fuzzy Ontology, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 266-277.
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