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Applied & Interdisciplinary Mathematics

Exploring the impact of how criminals interact with cyber-networks—a mathematical modeling approach

, , , , , , , , , & | (Reviewing editor:) show all
Article: 2295059 | Received 18 May 2023, Accepted 10 Dec 2023, Published online: 11 Jan 2024

References

  • Abdullah Al-Khater, W., Al-Maadeed, S., Ali Ahmed, A., Safaa Sadiq, A., & Khurram Khan, M. (2020). Comprehensive review of cybercrime detection techniques. Institute of Electrical and Electronics Engineers Access, 8, 137293–137311. https://doi.org/10.1109/ACCESS.2020.3011259
  • Arshey, M., & Angel Viji, K. S. (2021). Thwarting cyber crime and phishing attacks with machine learning: A study. Proceedings of the 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 1, 353–357. IEEE.
  • Artico, I., Smolyarenko, I., Vinciotti, V., & Wit, E. C. (2020). How rare are power-law networks really? Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2241). https://doi.org/10.1098/rspa.2019.0742
  • Aschi, M., Bonura, S., Masi, N., Messina, D., & Profeta, D. (2022). Cybersecurity and fraud detection in financial transactions. In D. Kyriazis & J. Soldatos (Eds.), Big data and artificial intelligence in digital finance: Increasing personalization and trust in digital finance using big data and AI (pp. 269–278). Springer.
  • Bailey, L., Harinam, V., & Ariel, B. (2020). Victims, offenders and victim-offender overlaps of knife crime: A social network analysis approach using police records. PLoS One, 15(12), e0242621. https://doi.org/10.1371/journal.pone.0242621
  • Banerjee, S., Van Hentenryck, P., & Cebrian, M. (2015). Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities. Palgrave Communications, 1(15022), https://doi.org/10.1057/palcomms.2015.22
  • Berlusconi, G. (2017). Social Network Analysis and Crime Prevention. In B. LeClerc & E. Savona (Eds.), Crime Prevention in the 21st Century (pp. 129–141). Springer. https://doi.org/10.1007/978-3-319-27793-6_10
  • Bertozzi, A. L., Johnson, S. D., & Ward, M. J. (2016). Mathematical modelling of crime and security: Special issue of EJAM. European Journal of Applied Mathematics, 27(3), 311–316. https://doi.org/10.1017/S0956792516000176
  • Bilen, A., & Bedri Özer, A. (2021). Cyber-attack method and perpetrator prediction using machine learning algorithms. PeerJ Computer Science, 7(e475), e475. https://doi.org/10.7717/peerj-cs.475
  • Bossler, A. M., & Berenblum, T. (2019). Introduction: New directions in cybercrime research. Journal of Crime & Justice, 42(5), 495–499. https://doi.org/10.1080/0735648X.2019.1692426
  • Brantingham, P. L., Glasser, U., Kinney, B., Singh, K., & Vajihollahi, M. (2005). A computational model for simulating spatial aspects of crime in urban environments. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, Hawaii, USA (Vol. 4, pp. 3667–3674). IEEE.
  • Bright, D., Brewer, R., & Morselli, C. (2022). Reprint of: Using social network analysis to study crime: Navigating the challenges of criminal justice records. Social Networks, 69, 235–250. https://doi.org/10.1016/j.socnet.2022.01.008
  • Burcher, M., & Whelan, C. (2018). Social network analysis as a tool for criminal intelligence: Understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278–294. https://doi.org/10.1007/s12117-017-9313-8
  • Chikore, T., Nyabadza, F., & Jane White, K. A. (2023). Exploring the impact of nonlinearities in police recruitment and criminal capture rates: A population dynamics approach. Mathematics, 11(7), 1669. https://doi.org/10.3390/math11071669
  • Chuanying, L. (2018). Understanding cybercrime from the perspective of cybersecurity. Retrieved December 27, 2021.
  • Clark, R. M., Cox, S. J. D., & Laslett, G. M. (1999). Generalizations of power-law distributions applicable to sampled fault-trace lengths: Model choice, parameter estimation and caveats. Geophysical Journal International, 136(2), 357–372. https://doi.org/10.1046/j.1365-246X.1999.00728.x
  • Clarke, J., White, K. A. J., & Turner, K. (2013). Approximating optimal controls for networks when there are combinations of population-level and targeted measures available: Chlamydia infection as a case-study. Bulletin of Mathematical Biology, 2013(75), 1747–1777. https://doi.org/10.1007/s11538-013-9867-9
  • Coclite, M., Garavello, G. M. A., & Spinolo, L. V. (2017). A mathematical model for piracy control through police response. Nonlinear Differential Equations and Applications NoDea, 24(4), https://doi.org/10.1007/s00030-017-0471-9
  • Comissiong, D. M. G., Sooknanan, J., & Bhatt, B. (2012). Life and death in a gang-a mathematical model of gang membership. Journal of Mathematics Research, 4(4), 10–28. https://doi.org/10.5539/jmr.v4n4p10
  • Cui, A.-X., Zhang, Z.-K., Tang, M., Ming Hui, P., Fu, Y., & Hayasaka, S. (2012, 12). Emergence of scale-free close-knit friendship structure in online social networks. PLOS ONE, 7(12), 1–13. https://doi.org/10.1371/journal.pone.0050702
  • Dash, B., Farheen Ansari, M., Sharma, P., & Ali, A. (2022). Threats and opportunities with ai-based cyber security intrusion detection: A review. International Journal of Software Engineering & Applications (IJSEA), 13(5), 13–21. https://doi.org/10.5121/ijsea.2022.13502
  • Drury, B., Drury, S. M., Arafatur Rahman, M., & Ullah, I. (2022). A social network of crime: A review of the use of social networks for crime and the detection of crime. Online Social Networks and Media, 30(100211), 100211. https://doi.org/10.1016/j.osnem.2022.100211
  • Folds, C. L. (2022). How Hackers and Malicious Actors are Using Artificial Intelligence to Commit Cybercrimes in the Banking Industry [ PhD thesis]. Colorado Technical University.
  • Fronczak, P. (2018). Scale-free nature of social networks. Springer New York.
  • Furnell, S. (2002). Cyber crime: Vandalizing the information society. Addison Wesley.
  • Goni, I., & Mohammad, M. (2020). Machine learning approach to mobile forensics framework for cyber crime detection in Nigeria. Journal of Computer Science Research, 2(4), 1–6. https://doi.org/10.30564/jcsr.v2i4.2147
  • Holling, C. S. (1959). Some characteristics of simple types of predation and parasitism. Canadian Entomologist, 9(7), 385–398. https://doi.org/10.4039/Ent91385-7
  • Internet Society. (2015). Global internet report 2015: Mobile evolution and development of the internet. Retrieved March 7, 2022.
  • Jane White, K. A., Campillo-Funollet, E., Nyabadza, F., Cusseddu, D., Kasumo, C., Imbusi, N. M., Juma, V. O., Meir, A. J., & Marijani, T. (2021). Towards understanding crime dynamics in a heterogeneous environment: A mathematical approach. Journal of Interdisciplinary Mathematics, 24(8), 2139–2159. https://doi.org/10.1080/09720502.2020.1860292
  • Jia, P., Liu, J., Fang, Y., Liu, L., & Liu, L. (2018). Modeling and analyzing malware propagation in social networks with heterogeneous infection rates. Physica A: Statistical Mechanics and Its Applications, 507, 240–254. https://doi.org/10.1016/j.physa.2018.05.047
  • Johnson, J. A., & David Reitzel, J. (2011). Social network analysis in an operational environment: Defining the utility of a network approach for crime analysis using the Richmond City Police Department as a case study. In International Police Executive Symposium.
  • Kshetri, N. (2013). Cybercrime and cybersecurity in the global south. Springer.
  • Kshetri, N. (2019). Cybercrime and cybersecurity in Africa. Journal of Global Information Technology Management, 22(2), 77–81. https://doi.org/10.1080/1097198X.2019.1603527
  • Kumar Saini, D. (2011). A mathematical model for the effect of malicious object on computer network immune system. Elsevier: Applied Mathematical Modelling, 35(8), 3777–3787. https://doi.org/10.1016/j.apm.2011.02.025
  • Lacey, A. A., & Tsardakas, M. N. (2016). A mathematical model of serious and minor criminal activity. European Journal of Applied Mathematics, 27(3), 403–421. https://doi.org/10.1017/S0956792516000139
  • Lebogang, V., Tabona, O., & Maupong, T. (2022). Evaluating cybersecurity strategies in Africa. In M. Dawson, O. Tabona, & T. Maupong (Eds.), Cybersecurity capabilities in developing nations and its impact on global security (pp. 1–19). IGI Global.
  • Lee, D.-S., & Zhu, M. (2021). Epidemic spreading in a social network with facial masks wearing individuals. IEEE Transactions on Computational Social Systems, 8(6), 1393–1406. https://doi.org/10.1109/TCSS.2021.3081148
  • Liu, B., Teng, Z., & Chen, L. (2006). Analysis of a predator–prey model with Holling II functional response concerning impulsive control strategy. Journal of Computational and Applied Mathematics, 193(1), 347–362. https://doi.org/10.1016/j.cam.2005.06.023
  • Mahor, V., Rawat, R., Telang, S., Garg, B., Mukhopadhyay, D., & Palimkar, P. (2021). Machine learning based detection of cyber crime hub analysis using twitter data. Proceedings of the 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia (pp. 1–5). IEEE.
  • Manasevich, R., Hung Phan, Q., & Souplet, P. (2013). Global existence of solutions for a chemotaxis-type system arising in crime modelling. European Journal of Applied Mathematics, 24(2), 273–296. https://doi.org/10.1017/S095679251200040X
  • Mangilal Chayal, N., & Patel, N. P. (2021). Review of machine learning and data mining methods to predict different cyberattacks. Data Science and Intelligent Applications: Proceedings of ICDSIA 2020, GIT, Gujarat, India (pp. 43–51).
  • McMillon, D., Simon, C. P., Morenoff, J., & Perc, M. (2014). Modeling the underlying dynamics of the spread of crime. PLoS ONE, 9(4), 1–22. https://doi.org/10.1371/journal.pone.0088923
  • Mijwil, M., & Aljanabi, M. (2023). Towards artificial intelligence-based cybersecurity: The practices and ChatGPT generated ways to combat cybercrime. Iraqi Journal for Computer Science and Mathematics, 4(1), 65–70.
  • Mithoo, P., & Kumar, M. (2023). Social network analysis for crime rate detection using spizella swarm optimization based bilstm classifier. Knowledge-Based Systems, 269(110450), 110450. https://doi.org/10.1016/j.knosys.2023.110450
  • Mohammed Shamiulla, A. (2019). Role of artificial intelligence in cyber security. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4628–4630. https://doi.org/10.35940/ijitee.A6115.119119
  • Mwangi, T., Asava, T., & Akerele, I. (2022). Cybersecurity threats in Africa. In D. Kuwali (Ed.), The Palgrave handbook of sustainable peace and security in Africa (pp. 159–180). Springer.
  • Nathan, O. M., & Jackob, K. O. (2019). Stability analysis in a mathematical model of corruption in Kenya. Asian Research Journal of Mathematics, 15(4), 1–15. https://doi.org/10.9734/arjom/2019/v15i430164
  • Nguyen, B. (2017). Modelling cyber vulnerability using epidemic models. Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), Madrid, Spain (pp. 232–239).
  • Omar, N., Bin Mohamed, I., Mohd Sanusi, Z., & Yogi Prabowo, H. (2014). Understanding social network analysis (SNA) in fraud detection. Proceedings of the International Congress on Interdisciplinary Behaviour and Social Sciences, Bali, Indonesia (pp. 543–548).
  • Parti, K., Dearden, T. E., & Choi, S. (2023). Understanding the use of artificial intelligence in cybercrime. International Journal of Cybersecurity Intelligence & Cybercrime, 6(2). https://doi.org/10.52306/2578-3289.1170
  • Perkins, R. C., Ouellet, M., Howell, C. J., & Maimon, D. (2023). The illicit ecosystem of hacking: A longitudinal network analysis of website defacement groups. Social Science Computer Review, 41(2), 390–409. https://doi.org/10.1177/08944393221097881
  • Pitcher, A. B. (2010). Adding police to a mathematical model of burglary. European Journal of Applied Mathematics, 21(4–5), 401–419. https://doi.org/10.1017/S0956792510000112
  • Pourhabibi, T., Ong, K.-L., Kam, B. H., & Ling Boo, Y. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133(113303), 113303. https://doi.org/10.1016/j.dss.2020.113303
  • Richard Mphatheni, M., & Maluleke, W. (2022). Cybersecurity as a response to combating cybercrime: Demystifying the prevailing threats and offering recommendations to the African regions. International Journal of Research in Business and Social Science, 11(4), 384–396. https://doi.org/10.20525/ijrbs.v11i4.1714
  • Sabillon, R., Cano, J., Cavaller, V., & Serra, J. (2016). Cybercrime and cybercriminals: A comprehensive study. International Journal of Computer Networks and Communications Security, 4(6), 165–176.
  • Saeed, M., & Osakwe, S. (2021). Are African countries doing enough to ensure cybersecurity and internet safety?
  • Sayaji, R. W. (2013). Mathematical modelling: A study of corruption in the society. International Journal of Scientific & Engineering Research, 4(7), 2303–2318.
  • Serafino, M., Cimini, G., Maritan, A., Rinaldo, A., Suweis, S., Banavar, J. R., & Caldarelli, G. (2021). True scale-free networks hidden by finite size effects. Proceedings of the National Academy of Sciences, 118(2), 2021. https://doi.org/10.1073/pnas.2013825118
  • Shankar Rao, Y., Kumar Rauta, A., Saini, H., & Charana Panda, T. (2017). Mathematical model for cyber attack in computer network. International Journal of Business Data Communications and Networking (IJBDCN), 13(1), 58–65. https://doi.org/10.4018/IJBDCN.2017010105
  • Short, M. B., Bertozzi, A. L., & Jeffrey Brantingham, P. (2010). Nonlinear patterns in urban crime: Hotspots, bifurcations, and suppression. SIAM Journal on Applied Dynamical Systems, 9(2), 462–483. https://doi.org/10.1137/090759069
  • Short, M. B., Brantingham, P. J., Bertozzi, A. L., & Tita, G. E. (2010). Dissipation and displacement of hotspots in reaction-diffusion models of crime. Proceedings of the National Academy of Sciences of the United States of America, 107(9), 3961–3965. https://doi.org/10.1073/pnas.0910921107
  • Short, M. B., Jeffrey Brantingham, P., Bertozzi, A. L., & Tita, G. E. (2010). Dissipation and displacement of hotspots in reaction-diffusion models of crime. Proceedings of the National Academy of Sciences, 107(9), 3961–3965. https://doi.org/10.1073/pnas.0910921107
  • Sooknanan, J., Bhatt, B., & Comissiong, D. M. (2013). Another way of thinking: A review of mathematical models of crime. Math Today, 131, 131–133.
  • Sooknanan, J., Bhatt, B., & Comissiong, D. M. G. (2012). Criminals treated as predators to be harvested: A two prey one predator model with group defense, prey migration and switching. Journal of Mathematics Research, 4(4), https://doi.org/10.5539/jmr.v4n4p92
  • Sooknanan, J., Bhatt, B., & Comissiong, D. M. G. (2013a). Catching a gang - a mathematical model of the spread of gangs in a population treated as an infectious disease. International Journal of Pure and Applied Mathematics, 83(1), 25–43. https://doi.org/10.12732/ijpam.v83i1.4
  • Sooknanan, J., Bhatt, B., & Comissiong, D. M. G. (2013b). CATCHING A GANG -- A MATHEMATICAL MODEL OF THE SPREAD OF GANGS IN A POPULATION TREATED AS AN INFECTIOUS DISEASE. International Journal of Pure and Applied Mathematics, 83(1), 25–43. https://doi.org/10.12732/ijpam.v83i1.4
  • Sooknanan, J., Bhatt, B., & Comissiong, D. M. G. (2016). A modified predator–prey model for the interaction of police and gangs. Royal Society Open Science, 3(9), 160083. https://doi.org/10.1098/rsos.160083
  • Tcherni, M., Davies, A., Lopes, G., & Lizotte, A. (2016). The dark figure of online property crime: Is cyberspace hiding a crime wave? Justice Quarterly, 33(5), 890–911. https://doi.org/10.1080/07418825.2014.994658
  • Tewa, J. J., Djeumen, V. Y., & Bowong, S. (2012). Predator–prey model with Holling response function of type II and SIS infectious disease. Applied Mathematical Modelling, 37(7), 4825–4841. https://doi.org/10.1016/j.apm.2012.10.003
  • Tita, G. E., & Boessen, A. (2012). Social networks and the ecology of crime: Using social network data to understand the spatial distribution of crime. In D. Gadd, S. Karstedt, & S. F. Messner (Eds.), The SAGE handbook of criminological research methods (pp. 128–142). Sage.
  • Uchenna Chinedu, P., Nwankwo, W., Masajuwa, F. U., & Imoisi, S. (2021). Cybercrime detection and prevention efforts in the last decade: An overview of the possibilities of machine learning models. Review of International Geographical Education Online, 11(7).
  • United Nations Department of Economic and Social Affairs. (2015). United nations sustainable development goals. Retrieved August 17, 2022.
  • United Nations Office on Drugs and Crime. (2013) . Global study on homicide 2013: Trends, contexts, data. UNODC.
  • Walker, S. (2019). Cyber-insecurities? A guide to the UN cybercrime debate.
  • Wang, X., An, Q., He, Z., & Fang, W. (2021). A literature review of social network analysis in epidemic prevention and control. Complexity, 2021, 1–20. https://doi.org/10.1155/2021/3816221
  • Wikstrom, P. H. (2019). Situational action theory: A general, dynamic and mechanism-based theory of crime and its causes. Springer International Publishing.
  • William Johnsen, J., & Franke, K. (2020). Identifying proficient cybercriminals through text and network analysis. Proceedings of the 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), Arlington, VA, USA (pp. 1–7). IEEE.
  • Zhang, Z., Wang, H., Wang, C., & Fang, H. (2015). Modeling epidemics spreading on social contact networks. IEEE Transactions on Emerging Topics in Computing, 3(3), 410–419. https://doi.org/10.1109/TETC.2015.2398353
  • Zhou, B., Meng, X., & Eugene Stanley, H. (2020). Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks. Proceedings of the National Academy of Sciences, 117(26), 14812–14818. https://doi.org/10.1073/pnas.1918901117