Skip to main content
Springer Nature Link
Log in

Different Attack and Defense Types for AI Cybersecurity

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14886))

  • 560 Accesses

Abstract

Artificial Intelligence emerged as a field of study in the mid-20th century, driven by the ambition to develop machines capable of emulating human intelligence and reasoning. However, its rapid advancement has brought forth many cybersecurity challenges, encompassing data security, privacy preservation, and model resilience. Consequently, the field of AI necessitates tailored cybersecurity defense mechanisms and protective technologies to safeguard its integrity. In this paper, we delve into the realm of AI cybersecurity, exploring its prominent areas and delineating various attacks occurring across different phases of the Artificial Intelligence lifecycle. Furthermore, we elucidate defensive strategies against adversarial attacks, encompassing preprocessing techniques, adversarial training methodologies, and distillation methods.

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

References

  1. McCarthy, J., Minsky, M.L., et al.: A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 27(4), 12 (2006)

    Google Scholar 

  2. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  3. Han, X., Zhou, Y., Chen, K., et al.: ADS-lead: Lifelong anomaly detection in autonomous driving systems. IEEE Trans. Intell. Transp. Syst. 24(1), 1039–1051 (2022)

    Article  Google Scholar 

  4. Hasselgren, C., Oprea, T.I.: Artificial intelligence for drug discovery: are we there yet? Annu. Rev. Pharmacol. Toxicol. 64, 527–550 (2024)

    Article  Google Scholar 

  5. Endsley, M.R.: Autonomous driving systems: a preliminary naturalistic study of the Tesla Model S. J. Cogn. Eng. Decis. Making 11(3), 225–238 (2017)

    Article  Google Scholar 

  6. Ammari, T., Kaye, J., Tsai, J.Y., et al.: Music, search, and IoT: how people (really) use voice assistants. ACM Trans. Comput. Hum. Interact. (TOCHI) 26(3), 1–28 (2019)

    Article  Google Scholar 

  7. Zhao, W., Chellappa, R., Phillips, P.J., et al.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

  8. Kaur, R., Gabrijelčič, D., Klobučar, T.: Artificial intelligence for cybersecurity: literature review and future research directions. Inf. Fusion 97, 101804 (2023)

    Article  Google Scholar 

  9. Chaslot, G.M.J., Winands, M.H.M., Herik, H.J., et al.: Progressive strategies for Monte-Carlo tree search. New Math. Nat. Comput. 4(03), 343–357 (2008)

    Article  MathSciNet  Google Scholar 

  10. Kong, Y., Zhang, J.: Adversarial audio: a new information hiding method. In:INTERSPEECH 2020, pp. 2287–2291 (2020)

    Google Scholar 

  11. Liang, W., et al.: Deep neural network security collaborative filtering scheme for service recommendation in intelligent cyber–physical systems. IEEE IoTJ 9(22), 22123–22132 (2022)

    Google Scholar 

  12. Cockburn, D., Jennings, N.R.: ARCHON: a distributed artificial intelligence system for industrial applications (1996)

    Google Scholar 

  13. Board, F.S.B.F.S.: Artificial intelligence and machine learning in financial services: market developments and financial stability implications. Financial Stability Board (2017)

    Google Scholar 

  14. Hu, Y., Kuang, W., Qin, Z., et al.: Artificial intelligence security: threats and countermeasures. ACM Comput. Surv. (CSUR) 55(1), 1–36 (2021)

    Article  Google Scholar 

  15. Wirkuttis, N., Klein, H.: Artificial intelligence in cybersecurity. Cyber Intell. Secur. 1(1), 103–119 (2017)

    Google Scholar 

  16. Szegedy, C., Zaremba, W., Sutskever, I., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)

  17. Liu, Y., Ma, S., Aafer, Y., et al.: Trojaning attack on neural networks. In: 25th Annual Network and Distributed System Security Symposium (NDSS 2018). Internet Soc (2018)

    Google Scholar 

  18. Chen, T., Liu, J., Xiang, Y., et al.: Adversarial attack and defense in reinforcement learning-from AI security view. Cybersecurity 2, 1–22 (2019)

    Article  Google Scholar 

  19. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  20. Henderson, P., Islam, R., Bachman, P., et al.: Deep reinforcement learning that matters. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)

    Google Scholar 

  21. Arulkumaran, K., Deisenroth, M.P., Brundage, M., et al.: A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866 (2017)

  22. Cheng, R., Orosz, G., et al.: End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks. In:AAAI, vol. 33, no. 01, pp. 3387–3395 (2019)

    Google Scholar 

  23. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013)

    Article  Google Scholar 

  24. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning, arXiv Prepr. arXiv:1509.02971 (2015)

  25. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  26. Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Playing atari with deep reinforcement learning. arXiv 2013. arXiv preprint arXiv:1312.5602 (2013)

  27. Pattanaik, A., Tang, Z., Liu, S., et al.: Robust deep reinforcement learning with adversarial attacks. arXiv preprint arXiv:1712.03632 (2017)

  28. Favarò, F.M., Nader, N., Eurich, S.O., et al.: Examining accident reports involving autonomous vehicles in California. PLoS ONE 12(9), e0184952 (2017)

    Article  Google Scholar 

  29. Chan, M., Estève, D., Escriba, C., et al.: A review of smart homes—present state and future challenges. Comput. Methods Programs Biomed. 91(1), 55–81 (2008)

    Article  Google Scholar 

  30. Lai, C.S., Jia, Y., Dong, Z., et al.: A review of technical standards for smart cities. Clean Technol. 2(3), 290–310 (2020)

    Article  Google Scholar 

  31. Bruce, V., Young, A.: Understanding face recognition. Br. J. Psychol. 77(3), 305–327 (1986)

    Article  Google Scholar 

  32. Devlin, J., Chang, M.W., Lee, K., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  33. Boscardin, C.K., Gin, B., Golde, P.B., et al.: ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Acad. Med. 99(1), 22–27 (2023)

    Article  Google Scholar 

  34. Lin, J.C., Younessi, D.N., Kurapati, S.S., et al.: Comparison of GPT-3.5, GPT-4, and human user performance on a practice ophthalmology written examination. Eye 37, 1–2 (2023)

    Article  Google Scholar 

  35. Lee, P., Bubeck, S., Petro, J.: Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N. Engl. J. Med. 388, 1233–1239 (2023). https://doi.org/10.1056/NEJMsr2214184

    Article  Google Scholar 

  36. Dwivedi, Y.K., Pandey, N., et al.: Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda. Intl. J. Contemp. Hosp. Mana. 36(1), 1–12 (2024)

    Article  Google Scholar 

  37. Cheng, L., Liu, F., Yao, D.: Enterprise data breach: causes, challenges, prevention, and future directions, Wiley. Data Min. Knowl. Disc. 7(5), e1211 (2017)

    Article  Google Scholar 

  38. Kim, W., Choi, B.J., Hong, E.K., et al.: A taxonomy of dirty data. Data Min. Knowl. Disc. 7, 81–99 (2003)

    Article  MathSciNet  Google Scholar 

  39. Dee, D.P.: Bias and data assimilation. Q. J. R. Meteorol. Soc. 131(613), 3323–3343 (2005)

    Article  Google Scholar 

  40. Papernot, N., McDaniel, P., Sinha, A., et al.: SoK: security and privacy in machine learning. In: IEEE European Symposium on Security and Privacy (EuroS&P), pp. 399–414. IEEE (2018)

    Google Scholar 

  41. Quiring, E., Rieck, K.: Backdooring and poisoning neural networks with image-scaling attacks. In: 2020 IEEE Security and Privacy Workshops (SPW), pp. 41–47. IEEE (2020)

    Google Scholar 

  42. Xiao, Q., Chen, Y., Shen, C., et al.: Seeing is not believing: camouflage attacks on image scaling algorithms. In: USENIX Security, pp. 443–460 (2019)

    Google Scholar 

  43. Shafahi, A., Huang, W.R., et al.: Poison frogs! targeted clean-label poisoning attacks on neural networks. In: NIPS (2018)

    Google Scholar 

  44. Zeng, Y., Pan, M., et al.: Narcissus: a practical clean-label backdoor attack with limited information.In: ACM CCS, pp. 771–785 (2023)

    Google Scholar 

  45. Gao, X., Qiu, M.: Energy-based learning for preventing backdoor attack. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) KSEM 2022. LNCS, vol. 13370, pp. 706–721. Springer, Cham (2022)

    Google Scholar 

  46. Qiu, H., Zeng, Y., et al.: Deepsweep: an evaluation framework for mitigating DNN backdoor attacks using data augmentation. In:ACM Asia CCS, pp. 363–377 (2021)

    Google Scholar 

  47. Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE 6th BigDataSecurity (2020)

    Google Scholar 

  48. Li, C., Qiu, M.: Reinforcement Learning for Cyber-Physical Systems: With Cybersecurity Case Studies. CRC Press, Boca Raton (2019)

    Google Scholar 

  49. Zhang, Y., Qiu, M., et al.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2015)

    Article  Google Scholar 

  50. Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. ITS (2020)

    Google Scholar 

  51. Qiu, M., Gao, W., et al.: Energy efficient security algorithm for power grid wide area monitoring system. IEEE Trans. Smart Grid 2(4), 715–723 (2011)

    Article  Google Scholar 

  52. Qiu, M., Su, H., et al.: Balance of security strength and energy for a PMU monitoring system in smart grid. IEEE Commun. Mag. 50(5), 142–149 (2012)

    Article  Google Scholar 

  53. Qiu, H., Qiu, M., Lu, R.: Secure V2X communication network based on intelligent PKI and edge computing. IEEE Network 34(2), 172–178 (2019)

    Article  Google Scholar 

  54. Wei, X., Guo, H., et al.: Reliable data collection techniques in underwater wireless sensor networks: a survey. IEEE Comm. Surv. Tutor. 24(1), 404–431 (2021)

    Article  MathSciNet  Google Scholar 

  55. Li, Y., Dai, W., et al.: Privacy protection for preventing data over-collection in smart city. IEEE Trans. Comput. 65(5), 1339–1350 (2015)

    Article  MathSciNet  Google Scholar 

  56. Gai, K., Zhang, Y., et al.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput. (2022)

    Google Scholar 

  57. Papernot, N., McDaniel, P., Wu, X., et al.: Distillation as a defense to adversarial perturbations against deep neural networks. In:IEEE Symposium on Security and Privacy (SP), pp. 582–597 (2016)

    Google Scholar 

  58. Papernot, N., McDaniel, P., Goodfellow, I., et al.: Practical black-box attacks against machine learning.In: ACM on Asia Conference on Computer and Communications Security, pp. 506–519 (2017)

    Google Scholar 

  59. Vivek, B.S., Mopuri, K.R., Babu, R.V.: Gray-box adversarial training. In: European Conference on Computer Vision (ECCV), pp. 203–218 (2018)

    Google Scholar 

  60. Nicolae, M.I., Sinn, M., Tran, M.N., et al.: Adversarial Robustness Toolbox v1. 0.0. arXiv preprint arXiv:1807.01069 (2018). https://doi.org/10.48550/arXiv.1807.01069

  61. Jia, X., Wei, X., Cao, X., et al.: Comdefend: an efficient image compression model to defend adversarial examples. In: IEEE/CVF CVPR, pp. 6084–6092 (2019)

    Google Scholar 

  62. Xu, H., Pei, C., Yang, G.: Adversarial example defense based on image reconstruction. PeerJ Comput. Sci. 7, e811 (2021)

    Article  Google Scholar 

  63. Yang, Y., Zhang, G., Katabi, D., et al.: Me-net: towards effective adversarial robustness with matrix estimation. arXiv preprint arXiv:1905.11971 (2019)

    Google Scholar 

  64. Xu, W., Evans, D., Qi, Y.: Feature squeezing: detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 (2017)

    Google Scholar 

  65. Zhao, Z., Chen, G., et al.: Attack as defense: characterizing adversarial examples using robustness. In:30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 42–55 (2021)

    Google Scholar 

  66. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)

  67. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

  68. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In:IEEE Symposium on Security and Privacy (SP), pp. 39–57 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Cyber Sciences, Augusta University, Augusta, GA, 30912, USA

    Jing Zou, Shungeng Zhang & Meikang Qiu

Authors
  1. Jing Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shungeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meikang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meikang Qiu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Cungeng Cao

  2. Zhejiang University, Zhejiang, China

    Huajun Chen

  3. Emory University, Atlanta, GA, USA

    Liang Zhao

  4. Birmingham City University, Birmingham, UK

    Junaid Arshad

  5. Monash University, Banten, Indonesia

    Taufiq Asyhari

  6. Birmingham City University, Birmingham, UK

    Yonghao Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zou, J., Zhang, S., Qiu, M. (2024). Different Attack and Defense Types for AI Cybersecurity. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14886. Springer, Singapore. https://doi.org/10.1007/978-981-97-5498-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-5498-4_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5497-7

  • Online ISBN: 978-981-97-5498-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics