Abstract
Nowadays, Online Social Networks (OSNs) has created a breeding ground for criminals to engage in cyber–crime activities, and the legal enforcement agencies (LEAs) are facing significant challenges since there is no consistent and generalized framework built specifically to analyse users’ misbehaviour and their social activity on these platforms. Data exchanged over these platforms represent an important source of information, even their characteristics such as unstructured nature, high volumes, velocity, and data inter–connectivity, become an obstacle for LEAs to analyse these data using traditional methods in order to provide it to the legal domain. Although numerous researches have been carried out on digital forensics, little focus has been employed on developing appropriate tools to exhaustively meet all the requirements of crime investigation targeting data integration, information sharing, collection and preservation of digital evidences. To bridge this gap, in our preliminary work we presented a generic digital evidence framework, called CISMO as a semantic tool that is able to support LEAs in detecting and preventing different type of crimes happening on OSNs. This paper gives details of the knowledge extraction layer of the framework. Specially, we mainly focus on analyses criminal social graph structures proving the effectiveness of CISMO in a case study with real criminal dataset. Experimental results reveal that applying appropriate Social Network Analyses (SNA), CISMO framework should be able to query and discover the criminal networks, empowering the criminal investigator to see the connections between people.
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References
Digital 2020. https://wearesocial.com/digital-2020
Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., Boyd, D.: The Arab spring| the revolutions were tweeted: ınformation flows during the Tunisian and Egyptian revolutions. Int. J. Commun. 5, 2011 (2011)
Eaton, R.: Digital Terrorism and Hate. Simon Wiesenthal Centre (2014). Accessed 18 Mar 2020. http://www.wiesenthal.com/site/apps/nlnet/content.aspx?c=lsKWLbPJLnF&b=8776547&ct=13928897
Janze, C.: Are cryptocurrencies criminals’ best friends? Examining the coevolution of Bitcoin and darknet markets. In: Proceedings of the Americas Conference on Information Systems (AMCIS), Boston, MA, p. 10 (2017)
Décary-Hétu, D., Dupont, B.: The social network of hackers. Global Crime 13(3), 160–175 (2012)
Marcus, S.M., Moy, M., Coffman, T.: Social network analysis. In: Cook, D.J., Holder, L.B. (eds.) Mining Graph Data. Wiley, New York (2007)
Weimann, G.: Going dark: terrorism on the dark web. Stud. Conflict Terrorism 39(3), 195–206 (2016)
Bradbury, D.: Unveiling the dark web. Netw. Secur. 4, 14–17 (2014)
Edwards, M.J., Rashid, A., Rayson, P.: A service-independent model for linking online user profile information. In: Proceedings of the 2014 IEEE Joint Intelligence and Security Informatics Conference, The Hague, The Netherlands, 24–26 September 2014, pp. 280–283 (2014)
Travers, J., Milgram, S.: An experimental study of the small world problem. Sociometry 32, 425–443 (1969)
Wilson, C., Sala, A., Puttaswamy, K.P.N., Zhao, B.Y.: Beyond social graphs. ACM Trans. Web 6(4), 1–31 (2012)
Bonacich, P.: Technique for analyzing overlapping memberships. Sociol. Methodol. 4, 176–185 (1972)
Freeman, L.C., Roeder, D., Mulholland, R.R.: Centrality in social networks: II. Experimental results. Soc. Netw. 2(2):119–141 (1979)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Yolum, Güngör, T., Gürgen, F., Özturan, C. (eds.) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol. 3733, pp. 284–293. Springer, Heidelberg. https://doi.org/10.1007/11569596_31
Rosvall, M., Bergstrom, C.T.: Maps of information flow reveal community structure in complex networks. PNAS 105, 1118 (2008)
Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(5), 36–41 (2007)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. (10), P10008 (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Peel, L., Larremore, D.B., Clauset, A.: The ground truth about metadata and community detection in networks. Sci. Adv. 3, e1602548 (2017)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)
Elezaj, O., Yildirim, S., Ahmed, J., Kalemi, E., Brichfeldt, B., Haubold, C.: Crime Intelligence from Social Media Using CISMO. In: Yang, X.S., Sherratt, R.S., Dey, N., Joshi, A. (eds.) Proceedings of Fifth International Congress on Information and Communication Technology. ICICT 2020. Advances in Intelligent Systems and Computing, vol. 1183, pp. 441–460. Springer, Singapore. https://doi.org/10.1007/978-981-15-5856-6_44
Newman, M.E.J.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
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Elezaj, O., Yayilgan, S.Y., Kalemi, E. (2021). Criminal Network Community Detection in Social Media Forensics. In: Yildirim Yayilgan, S., Bajwa, I.S., Sanfilippo, F. (eds) Intelligent Technologies and Applications. INTAP 2020. Communications in Computer and Information Science, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-71711-7_31
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