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Design and Analysis of NICS Based Web Attack Detection for Advanced Intrusion Detection System

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Knowledge Graphs and Semantic Web (KGSWC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1459))

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Abstract

Worldwide Web has been the most prominent data sources since its inception, and several technologies have emerged around it and finally amalgamated with it. With the decades of progression and advancements, web is still structuring and welcoming latest technologies. However, cyber threats are also raising and becoming the concern. Since last decade, the defensive methods in Cyber Security domain have also transformed from conventional pattern/rule-based response systems to more active defensive systems. The active defensive methods are now equipped with Artificial Intelligence and Machine Learning techniques which are capable enough to learn and respond quickly to an entirely new cyber-attack. However, these techniques needs training and sometimes fail to perform in case of incomplete or inaccurate training data. Nature-inspired Cyber Security attempts to deliver robust solution to this problem as they are fundamentally tolerant to the missing, incomplete and inaccurate data. While the development both in Worldwide Web and Cyber Security is still continuing, the researchers are working on advanced security measures to make the worldwide web safe in all respects. This paper focuses on the design and analysis of NICS-based web attack detection system in cooperation with the existing Intrusion Detection System (IDS), by experimenting with the methods and procedures of Nature-inspired Cyber Security to generate adaptive responses against advanced cyber-attacks. The proposed method is tested and validated on multiple attack scenarios and achieved better results in early detection of suspicious activities of web attacks for IDS.

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References

  1. Fraley, J.B., Cannady, J.: The promise of machine learning in cybersecurity. In: Southeast Conference, pp. 1–6. IEEE (2017)

    Google Scholar 

  2. Ghafir, I., et al.: Detection of advanced persistent threat using machine learning correlation analysis. Future Gener. Comput. Syst. 89, 349–359 (2018)

    Article  Google Scholar 

  3. Breza, M., McCann, J.A.: Lessons in implementing bio-inspired algorithms on wireless sensor networks. In: 2008 NASA/ESA Conference on Adaptive Hardware and Systems, pp. 271–276 (2008)

    Google Scholar 

  4. Mthunzi, S., Benkhelifa, E., Bosakowski, T., Hariri, S.: A Bio-inspired Approach to Cyber Security: Principles, Algorithms, and Practices, pp. 75–104 (2019)

    Google Scholar 

  5. Mishra, S., Sagban, R., Yakoob, A., Gandhi, N.: Swarm intelligence in anomaly detection systems: an overview. Int. J. Comput. Appl. 43(2), 109–118 (2021)

    Google Scholar 

  6. Rauf, U.: A taxonomy of bio-inspired cyber security approaches: existing techniques and future directions. Arab. J. Sci. Eng. 43, 6693–6708 (2018)

    Article  Google Scholar 

  7. Thakkar, A., Lohiya, R.: Role of swarm and evolutionary algorithms for intrusion detection system: a survey. Swarm Evol. Comput. 53, 100631 (2020)

    Article  Google Scholar 

  8. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, vol. 12. Luniver Press, Bristol (2008)

    Google Scholar 

  9. Pervez, M.S., Farid, D.: Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs. In: SKIMA 2014 - 8th International Conference on Software, Knowledge, Information Management and Applications (2015)

    Google Scholar 

  10. Selvakumar, B., Muneeswaran, K.: Firefly algorithm based feature selection for network intrusion detection. Comput. Secur. 81, 148–155 (2019)

    Article  Google Scholar 

  11. Najeeb, R.F., Dhannoon, B.N.: A feature selection approach using binary firefly algorithm for network intrusion detection system. ARPN J. Eng. Appl. Sci. 13(6), 2347–2352 (2018)

    Google Scholar 

  12. Ram, B.H., Rao, B.V.: An efficient ids based on fuzzy firefly optimization and fast learning network. Int. J. Eng. Technol. (UAE) 7, 557–561 (2018)

    Google Scholar 

  13. Dhanarao, S., Kumar, M.: Efficient IDS for manet using hybrid firefly with a genetic algorithm (2019)

    Google Scholar 

  14. Albadran, M.: A new firefly-fast learning network model based intrusion-detection system. Int. J. Innov. Technol. Exploring Eng. (2020)

    Google Scholar 

  15. Hossein, P., Reza, F.: A firefly algorithm for power management in wireless sensor networks (WSNs). J. Supercomputing 77, 1–22 (2021)

    Article  Google Scholar 

  16. Kaur, A., Pal, S.K., Singh, A.P.: Hybridization of kmeans and firefly algorithm for intrusion detection system. Int. J. Syst. Assur. Eng. Manag. 9(4), 901–910 (2018)

    Article  Google Scholar 

  17. Ghosh, P., Sarkar, D., Sharma, J., Phadikar, S.: An intrusion detection system using modified-firefly algorithm in cloud environment. Int. J. Digit. Crime Forensics (IJDCF) 13(2), 77–93 (2021)

    Article  Google Scholar 

  18. Karatas, G., Demir, Ö., Sahingoz, O.: Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE Access 8, 1 (2020)

    Article  Google Scholar 

  19. Bhattacharya, S., et al.: A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics 9, 219 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

The author is thankful for all the support from the research team and professors who are extensively working on Nature-inspired Cyber Security at VIT Bhopal University-India (Mr. Saket Upadhyay), Soongsil University-South Korea (Dr. Ajit Kumar & Dr. Bong Jun Choi), Liverpool Hope University-United Kingdom (Prof. Atulya K Nagar) and Devi Ahilya University-India (Prof. VB Gupta).

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Shandilya, S.K. (2021). Design and Analysis of NICS Based Web Attack Detection for Advanced Intrusion Detection System. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-91305-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91304-5

  • Online ISBN: 978-3-030-91305-2

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