Skip to main content
Springer Nature Link
Log in

Trustworthy Artificial Intelligence for Cyber Threat Analysis

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 542))

Included in the following conference series:

  • 1010 Accesses

Abstract

Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Fig. 1, 2, 3 image source: [3].

References

  1. Abdulraheem, M., Ibraheem, N.: A detailed analysis of new intrusion detection dataset. In: Semantic Scolar (2019)

    Google Scholar 

  2. Ablayev, F., Ablayev, M., Huang, J.Z., Khadiev, K., Salikhova, N., Dingming, W.: On quantum methods for machine learning problems part i: Quantum tools. Big Data Mining and Analytics 3(1), 41–55 (2020)

    Article  Google Scholar 

  3. Amazon. Aws, 3 (2022)

    Google Scholar 

  4. Mohana, K., Venugopal, V.K., Sathwik, H.N.: Data security using genetic algorithm and artificial neural network. Int. J. Sci. Eng. Res. 5, 543–548 (2014)

    Google Scholar 

  5. Bernagozzi, M., Srivastava, B., Rossi, F., Usmani, S.: Gender bias in online language translators: visualization, human perception, and bias/accuracy trade-offs. IEEE Internet Comput. 25, 53–63 (2021)

    Article  Google Scholar 

  6. Betarte, G., Pardo, A., Martínez, R.: Web application attacks detection using machine learning techniques. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1065–1072 (2018)

    Google Scholar 

  7. Sagar, B.S., Niranjan, S., Kashyap, N., Sachin, D.N.: Providing cyber security using artificial intelligence - a survey. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 717–720 (2019)

    Google Scholar 

  8. Dilmaghani, S., Brust, M.R., Danoy, G., Cassagnes, N., Pecero, J., Bouvry, P.: Privacy and security of big data in AI systems: a research and standards perspective. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5737–5743 (2019)

    Google Scholar 

  9. Chen Ding, Tian-Yi Bao, and He-Liang Huang. Quantum-inspired support vector machine. IEEE Trans. Neural Networks Learn. Syst. 1–13 (2021)

    Google Scholar 

  10. Edwards, D., Rawat, D.B.: Quantum adversarial machine learning: status, challenges and perspectives. In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 128–133 (2020)

    Google Scholar 

  11. Geron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd edn. O’Reilly (2019)

    Google Scholar 

  12. Gong, X., et al.: Estimating web attack detection via model uncertainty from inaccurate annotation. In: 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 53–58 (2019)

    Google Scholar 

  13. Gupta, S., Mohanta, S., Chakraborty, M., Ghosh, S.: Quantum machine learning-using quantum computation in artificial intelligence and deep neural networks: quantum computation and machine learning in artificial intelligence. In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 268–274 (2017)

    Google Scholar 

  14. Swapnil Ramesh Kumbar: Int. J. Innov. Res. Comput. Commun. Eng. An overview on use of artificial intelligence techniques in effective security management 2, 5893–5898 (2014)

    Google Scholar 

  15. Lohia, P.K., Natesan Ramamurthy, K., Bhide, M., Saha, D., Varshney, K.R., Puri, R.: Bias mitigation post-processing for individual and group fairness. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2847–2851 (2019)

    Google Scholar 

  16. Microsoft. Azure counterfeit, 3 (2022)

    Google Scholar 

  17. Mittu, R., Lawless, W.F.: Human factors in cybersecurity and the role for AI. In: 2015 AAAI Spring Symposium, pp. 39–43 (2015)

    Google Scholar 

  18. Mohanty, J.P., Swain, A., Mahapatra, K.: Headway in quantum domain for machine learning towards improved artificial intelligence. In: 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), pp. 145–149 (2019)

    Google Scholar 

  19. Ngoc, H.L., Cong Hung, T., Huy, N.D., Thanh Hang, N.T.: Early phase warning solution about system security based on log analysis. In: 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), pp. 398–403 (2019)

    Google Scholar 

  20. Obaidat, M., Singh, N., Vergara, G.: Artificial intelligence bias minimization via random sampling technique of adversary data. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1226–1230 (2021)

    Google Scholar 

  21. Rajan, H.M.: Artificial intelligence in cyber security-an investigation. Int. Res. J. Comput. Sci. 09, 28–30 (2017)

    Google Scholar 

  22. Su, T.-J., Wang, S.-M., Chen, Y.-F., Liu, C.-L.: Attack detection of distributed denial of service based on splunk. In: 2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE), pp. 397–400 (2016)

    Google Scholar 

  23. Tian, Z., Luo, C., Qiu, J., Xiaojiang, D., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. IEEE Trans. Industr. Inf. 16(3), 1963–1971 (2020)

    Article  Google Scholar 

  24. Torrano-Gimenez, C., Nguyen, H.T., Alvarez, G., Franke, K.: Combining expert knowledge with automatic feature extraction for reliable web attack detection. Secur. Commun. Networks 8(16), 2750–2767 (2015)

    Article  Google Scholar 

  25. Wang, P., Kelly, W.: A novel threat analysis and risk mitigation approach to prevent cyber intrusions. Colloquium Inf. Syst. Secu. Educ. (CISSE) 3, 157–174 (2015)

    Google Scholar 

  26. Wang, S., Kelly, W.: inVideo - a novel big data analytics tool for video data analytics. In: IEEE/NIST IT Pro Conference, pp. 1–19 (2014)

    Google Scholar 

  27. Wang, S.P.: Computer Architecture and Organization - Fundamentals and Architecture Security, 1st edn. Springer, Singapor (2021)

    Google Scholar 

  28. Wang, S.P., Sakk, E.: Quantum algorithms: overviews, foundations, and speedups. In: 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP), pp. 17–21 (2021)

    Google Scholar 

  29. Zhang, D.Y., Kou, Z., Wang, D.: Fairfl: a fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1051–1060 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Morgan State University, Baltimore, MD, USA

    Shuangbao Paul Wang

  2. Softrams, LLC, Leesburg, VA, USA

    Paul A. Mullin

Authors
  1. Shuangbao Paul Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul A. Mullin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuangbao Paul Wang .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, S.P., Mullin, P.A. (2023). Trustworthy Artificial Intelligence for Cyber Threat Analysis. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_36

Download citation

Publish with us

Policies and ethics