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Robust Review on Privacy and Security Issues in Present Day Attacks of Data Mining

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

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Abstract

The Advancements in technology leads to various developments in both positive and negative way. In data mining domain privacy and security are main concerns as they have major impact on businesses. Hackers compromise the systems and try to steal valuable information from datasets. To protect the data various algorithms have to be developed in data mining. This paper focusses on robust study on various researchers using datamining applications used to check the privacy and security threats such as Denial of service (DoS), distributed denial of service (DDoS), probing, malware, adware, spyware and ransomware.

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References

  1. Kiran, A., Vasumathi, D.A.: Comprehensive survey on privacy preservation algorithms in data mining. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2017)

    Google Scholar 

  2. Kumar, S.R., Jassi, J.S., Yadav, S.A., Sharma, R.: Data-mining a mechanism against cyber threats: a review. In: International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH) (2016)

    Google Scholar 

  3. Berry, M.J., Linoff, G.: Data Mining Techniques: for Marketing, Sales, and Customer Support. John Wiley & Sons Inc, Hoboken (1997)

    Google Scholar 

  4. Piatetsky-Shapiro, G.: Advances in knowledge discovery and data mining. Fayyad, U.M., Smyth, P., Uthurusamy R. (Eds.), vol. 21. Menlo Park, AAAI press (1996)

    Google Scholar 

  5. Hand, D.J.: Principles of data mining. Drug safe. 30(7), 621–622 (2007)

    Article  Google Scholar 

  6. Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., Zhou, Z.H.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)

    Article  Google Scholar 

  7. Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Berlin, Heidelberg (2006)

    Google Scholar 

  8. Miller, H.J., Han, J. (eds.): Geographic Data Mining and Knowledge Discovery. Taylor & Francis, London (2001)

    Google Scholar 

  9. Szymkowiak, A., Larsen, J., Hansen, L.: Modeling, Mathematical & Plads, Richard. Hierarchical Clustering for Datamining (2001)

    Google Scholar 

  10. Kallio, A., Tuimala, J.: Data mining. In: Dubitzky, W., Wolkenhauer, O., Cho, K.H., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY (2013). https://doi.org/10.1007/978-1-4419-9863-7_599

  11. URL: http://www.veronis.com

  12. Alparslan, A., Karahoca, A., Karahoca, D., Karahocam, A.: BotNet detection: enhancing analysis by using data mining techniques. Adv. Data Min. Knowl. Discovery Appl. Intechopen ch 17 (2012)

    Google Scholar 

  13. Shahrestani, A., Feily, M., Ahmad, R., Ramadass, S.: Architecture for applying data mining and visualizationon network flow for botnet traffic detection. In: International Conference on Computer Technology and Development, pp. 33–37 (2009)

    Google Scholar 

  14. Liao, W-H., Chang, C-C.: Peer to peer botnet detection using data mining scheme. In: 2010 International Conference on Internet Technology and Applications, Wuhan (2010)

    Google Scholar 

  15. Wang, Z.: LiFu. The research of detecting IRe botnet based on Kmeans algorithms. In: Second International Conference on Communication Systems, Networks and Applications, pp: 208–210 (2010)

    Google Scholar 

  16. Tripathi, N., Mehtre, B.: DoS and DDoS attacks: impact, analysis and countermeasures, pp. 1–6 (2013)

    Google Scholar 

  17. Sharma, M., Unde, N., Borude, K., Paradkar, A.: A data mining based approach towards detection of low rate DoS attack. In: International Conference for Convergence of Technology –, pp. 1–6 (2014)

    Google Scholar 

  18. Nijim, M., Albataineh, H., Khan, M., Rao, D.: FastDetict: a data mining engine for predecting and preventing DDoS attacks. In: IEEE International Symposium on Technologies for Homland Security (HST), Waltham, MA (2017)

    Google Scholar 

  19. Ng, J., Joshi, D., Shankar, M., Banik, D.: Applying data mining techniques to intrusion detection. In: 2015 12th International Conference on Information Technology - New Generations. pp. 800–801 (2015)

    Google Scholar 

  20. Aidan, J.S., Verma, H.K., Awasthi, L.K.: Comprehensive survey on petya ransomware attack. In: 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS), Jammu, pp. 122–125, (2017). https://doi.org/10.1109/icngcis.2017.30

  21. Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., et al.: (2 more authors) Know abnormal, find evil: frequent pattern mining for ransomware threat hunting and intelligence. IEEE Trans. Emerg. Top. Comput. (2017). ISSN 2168-6750

    Google Scholar 

  22. Sheen, S., Yadav, A.: Ransomware detection by mining API call usage. In: IEEE Conference on Network Softwarization. 25–29 June Montreal, Canada (2018)

    Google Scholar 

  23. Poudyal, S., Subedi, K.P., Dasgupta, D.: A framework for analyzing ransomware using machine learning. In: IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18–21 November, pp. 1692–1699 (2018)

    Google Scholar 

  24. Mallikarajunan, K.M.E.N., Preethi, S.R., Selvalakshmi, S., Nithish, N.: Detection of spyware in software using virtual environment. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, pp. 1138–1142 (2019). https://doi.org/10.1109/icoei.2019.8862547

  25. Divya, P., Rajalakshmi, S.: Classifying Spyware Files Using Data Mining Algorithms and Hexadecimal Representation. https://www.ijser.org/researchpaper/Classifying-Spyware-Files-Using-Data-Mining-Algorithms-and Hexadecimal-Representation.pdf

  26. Tzu-Yen, W., Horng, S.-J., Su, M.-Y., Wu, C.-H., Wang, P.-C., Su, W.-Z.: A surveillance spyware detection system based on data mining methods. In: 2006 IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, 16–21 July (2006)

    Google Scholar 

  27. Anil Kumar, D., Kapat, S.K., Das, S.K., Tripathy, S.N.: Classification of spyware affected files using data mining techniques. Int. J. Rec. Technol. Eng. (IJRTE), vol.-8 Issue-2S6, July 2019. ISSN: 2277-3878

    Google Scholar 

  28. Samantray, O.P., Tripathy, S.N., Das, S.K.: A study to understand malware behavior through malware analysis. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, pp. 1–5 (2019). https://doi.org/10.1109/icscan.2019.8878680

  29. Fraley, J.B., Figueroa, M.: Polymorphic Malware detection using topological feature extraction with data mining. “network security analysis using big data technology. In: SoutheastCon 2016, Norfolk, VA (2016)

    Google Scholar 

  30. Sundarkumar, G.G., Vadlamani, R.: Malware detection by text and data mining. In: 2013 IEEE International Conference on Computational Intelligence and Computing Research, Enathi (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Vikrama Simhapuri University, Nellore, India

    P. S. S. Nitin

  2. Department of Computer Science and Applications, Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati, India

    T. Sudha

Authors
  1. P. S. S. Nitin
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  2. T. Sudha
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Nitin, P.S.S., Sudha, T. (2021). Robust Review on Privacy and Security Issues in Present Day Attacks of Data Mining. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_61

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