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IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine Learning

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Abstract

The internet utilization has been developing quickly, mostly in previous eras. Though, by way of the internet develops an important section of the daily life routine, cybercrime is similarly on the grow. The prices of cybercrime will approximately five lakh crores dollars per year through 2022 according to cyber security endeavours details in 2021. The cyber attackers exploit some internet resources as a principal way of transformation through a victim system; therefore intruders generate benefit based on economic, promotional and many more through developing the susceptibilities over devices. The computing cybercrime threats and providing security procedures through physical schemes utilizing previous methodological techniques and moreover examinations have unsuccessful many times to govern cybercrime threats. The previous literature in field of cybercrime threats agonizes from absence of evaluation schemes to guess the cybercrimes, mainly on unstructured information. Hence, an Improved Grey-wolf Optimization based Classification Model (IGO-CM) is developed with the help of chaos system and information entropy utilizing machine learning schemes for cybercrime data analysis to compute the rate of cybercrime by classifying the cybercrime data. The protection examinations by means of the relationship of data analysis methodologies provide services to examine and classify crime data in unstructured form taking from India. The implementation of IGO-CM is performed on MATLAB 2021a tool for unstructured cybercrime data and the outcomes describe the superior performance of IGO-CM depending on accuracy, F-Measure, standard deviation, purity index, intra-cluster distance, root mean square error, and time complexity against a popular classification scheme K-Means, and some optimization schemes like ACO, ALO, PSO and GO.

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References

  1. Sonntag, D., & Profitlich, H.-J. (2019). An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation. Artificial Intelligence in Medicine, 93, 13–28. https://doi.org/10.1016/j.artmed.2018.08.003

    Article  Google Scholar 

  2. Prabakaran, S. & Mitra S. (2018). Survey of analysis of crime detection techniques using data mining and machine learning. In Journal of physics: conference series (pp. 1–12).

  3. Gil, D., Johnsson, M., Mora, H., & Szymanski, J. (2019). Review of the complexity of managing big data of the internet of things. Hindawi Complexity. https://doi.org/10.1155/2019/4592902

    Article  Google Scholar 

  4. Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for internet of things data analysis: A survey. Digital Communications and Networks, 4, 161–175. https://doi.org/10.1016/j.dcan.2017.10.002

    Article  Google Scholar 

  5. Hea, B., Guana, Y., & Dai, R. (2019). Classifying medical relations in clinical text via convolutional neural networks. Artificial Intelligence in Medicine, 93, 43–49. https://doi.org/10.1016/j.artmed.2018.05.001

    Article  Google Scholar 

  6. Chen, C.-Y., & Huang, J.-J. (2019). Double deep autoencoder for heterogeneous distributed clustering. Information, 10(144), 1–15. https://doi.org/10.3390/info10040144

    Article  Google Scholar 

  7. Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., Saha, B., Curino, C., O’Malley, O., Radia, S., Reed, B., & Baldeschwieler, E. (2019). Apache Hadoop YARN: Yet another resource negotiator. In SoCC, ACM, Santa Clara, California, USA (pp. 1–16).

  8. Giordano, D., Mellia, M., & Cerquitelli, T. (2021). K-MDTSC: K-multi-dimensional time-series clustering algorithm. Electronics, 10(1166), 1–21. https://doi.org/10.3390/electronics10101166

    Article  Google Scholar 

  9. Khare, A., Gupta, R., & Shukla, P. K. (2020). A grey wolf optimization algorithm (GWOA) for node capture attack to enhance the security of wireless sensor network. International Journal of Scientific & Technology Research, 9(3), 206–209.

    Google Scholar 

  10. Vinmalar, F. L., & Kombaiya, A. K. (2020). An improved dragonfly optimization algorithm based feature selection in high dimensional gene expression analysis for lung cancer recognition. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(8), 896–908.

    Article  Google Scholar 

  11. Chantar, H., Tubishat, M., Essgaer, M., & Mirjalili, S. (2021). Hybrid binary dragonfly algorithm with simulated annealing for feature selection. SN Computer Science, 2(295), 1–11.

    Google Scholar 

  12. Herrera, V. M., Khoshgoftaar, T. M., Villanustre, F. & Furht, B. (2019). Random forest implementation and optimization for Big Data analytics on LexisNexis’s high performance computing cluster platform. Journal of Big Data, 6(68), 1–36.

  13. Kiruthika, S., Kalaimani, E. V. R. M., Sudha, R., & Suganthi, K. (2021). ACO Feature selection and Novel Black Widow meta-heuristic Learning rate optimized CNN for Early diagnosis of Parkinson`s disease. Turkish Journal of Computer and Mathematics Education, 12(7), 809–817.

    Google Scholar 

  14. Dai, H. N., Wong, R. C. W., Wang, H., Zheng, Z. & Vasilakos, A. V. (2019). Big data analytics for large scale wireless networks: challenges and opportunities. ACM, 52(5), 1–46.

  15. Bhaskaran, S., Marappan, R., & Santhi, B. (2021). Design and analysis of a cluster-based intelligent hybrid recommendation system for e-learning applications. Mathematics, 9(107), 1–21. https://doi.org/10.3390/math9020197

    Article  Google Scholar 

  16. Gill, S. S., & Buyya, R. (2019). Bio-inspired algorithms for big data analytics: A survey, taxonomy and open challenges. Big Data Analytics for Intelligent Healthcare Management. https://doi.org/10.1016/B978-0-12-818146-1.00001-5

    Article  Google Scholar 

  17. Cui, X., Li, Y., Fan, J., Wang, T., & Zheng, Y. (2020). A hybrid improved dragonfly algorithm for feature selection. IEEE Access, 8, 155619–155629.

    Article  Google Scholar 

  18. Nadeem, F., Alghazzawi, D., Mashat, A., Faqeeh, K., & Almalaise, A. (2019). Using machine learning ensemble methods to predict execution time of e-science workflows in heterogeneous distributed systems. IEEE Access, 7, 25138–25149. https://doi.org/10.1109/ACCESS.2019.2899985

    Article  Google Scholar 

  19. Soomro, T. R., & Mumtaz, H. (2019). Social media-related cybercrimes and techniques for their prevention. Applied Computer Systems, 24, 9–17.

    Article  Google Scholar 

  20. Dooshima, M. P., Chidozie, E. N., Ademola, B. J., Sekoni, O. O., & Adebayo, I. P. (2018). A predictive model for the risk of mental illness in Nigeria using data mining. International Journal of Immonology, 6(1), 5–16. https://doi.org/10.11648/j.iji.20180601.12

    Article  Google Scholar 

  21. Ganesan, M., & Mayilvahanan, P. (2017). Cyber crime analysis in social media using data mining technique. International Journal of Pure and Applied Mathematics, 116, 413–424.

    Google Scholar 

  22. Ben-Nun, T., & Hoefler, T. (2019) Demystifying parallel and distributed deep learning: An in-depth concurrency analysi, pp. 1–47.

  23. Çagrı, B., Saglam, A., & R. B., Li, S. (2018). Automatic detection of cyber security related accounts on online social networks: twitter as an example. In Proceedings of the 9th international conference on social media and society, Copenhagen, Denmark (pp. 236–240).

  24. Hong, R., & Chandra, A. (2019). DLion: decentralized distributed deep learning in micro-clouds (pp. 1–9).

  25. Khan, M. A., Pradhan, S. K., & Fatima, H. (2017). Applying data mining techniques in cyber crimes. In Proceedings of 2nd international conference on anti-cyber crimes (ICACC), Abha, Saudi Arabia (pp. 213–216).

  26. Demertzis, K., Rantos, K., & Drosatos, G. (2020). A dynamic intelligent policies analysis mechanism for personal data processing in the IoT ecosystem. Big Data and Cognitive Computing, 4(9), 1–16. https://doi.org/10.3390/bdcc4020009

    Article  Google Scholar 

  27. Mitta, S. (2019). Cognitive computing architectures for machine (Deep) learning at scale. In Proceedings (pp. Vol. 1(186)). MDPI.

  28. Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A., & Qureshi, B. (2020). An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors, 20(6076), 1–23. https://doi.org/10.3390/s20216076

    Article  Google Scholar 

  29. Lamy, J.-B., Sekar, B., Guezenneca, G., Bouauda, J., & Séroussi, B. (2019). Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach. Artificial Intelligence in Medicine, 94, 42–53. https://doi.org/10.1016/j.artmed.2019.01.001

    Article  Google Scholar 

  30. Sarnovsky, M., & Olejnik, M. (2019). Improvement in the efficiency of a distributed multi-label text classification algorithm using infrastructure and task-related data. Informatics, 6(12), 1–15. https://doi.org/10.3390/informatics6010012

    Article  Google Scholar 

  31. Tama, B. A., & Lim, S. (2020). A comparative performance evaluation of classification algorithms for clinical decision support systems. Mathematics, 8(1814), 1–25. https://doi.org/10.3390/math8101814

    Article  Google Scholar 

  32. Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204–214. https://doi.org/10.1016/j.artmed.2019.01.006

    Article  Google Scholar 

  33. Ravikumar, K., & Kannan, A. R. (2018). Spatial Data Mining for Prediction of natural events and disaster management based on fuzzy logic using hybrid PSO. Taga Journal, 14, 858–878.

    Google Scholar 

  34. Khare, A., Shukla, P., & Silakari, S. (2014). Secure and fast chaos based encryption system using digital logic circuit. International Journal Computer Network and Information Security, 6, 25–33.

    Article  Google Scholar 

  35. Khare, A., Shukla, P. K., Rizvi, M. A., & Stalin, S. (2016). An intelligent and fast chaotic encryption using digital logic circuits for Ad-Hoc and ubiquitous computing. Entropy, 18(201), 1–27.

    Google Scholar 

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Authors and Affiliations

  1. School of Information Technology, RGPV, Bhopal, 462033, India

    Swati Sharma & Varsha Sharma

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  1. Swati Sharma
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  2. Varsha Sharma
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Contributions

Conceptualization, methodology, S.S..; software, validation, formal analysis, V.S.; investigation, resources, data curation, writing—original draft preparation, S.S. and V.S.; writing—review and editing, visualization, supervision, V.S.; project administration, funding acquisition, S.S. and V.S. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Swati Sharma.

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Sharma, S., Sharma, V. IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine Learning. Wireless Pers Commun 134, 1261–1281 (2024). https://doi.org/10.1007/s11277-024-10952-4

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