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AICA Development Challenges

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Autonomous Intelligent Cyber Defense Agent (AICA)

Part of the book series: Advances in Information Security ((ADIS,volume 87))

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Abstract

In this chapter we explore the development challenges that must be tackled before fulfilling the great potential of Autonomous Intelligent Cyberdefense Agent (AICA). We propose dividing development challenges into two kinds: the ones that are associated with the AICA engineering ecosystem and the ones that are associated with the AICA research ecosystem. This is reasonable because adequately addressing engineering challenges requires to tackling a range of research challenges. Moreover, engineering and research have different ways of thinking; in general, engineering focuses on narrower aspects and is often built on technical breakthroughs resulting from fundamental research. The engineering ecosystem has six components: design; implementation; individual test & certification; composition; composite test & certification; and deployment. The research ecosystem also accommodating six components: models; architectures; mechanisms; testing and certification; operations; and social, ethical, and legal aspects. To show how the challenges associated with these components are related to each other, we make connections between these two ecosystems by describing how tackling challenges in the research ecosystem would contribute to tackling the challenges that are encountered when engineering AICAs. We draw insights into the gaps between the state-of-the-art technology and the desired ultimate goals and propose research directions to bridge them. We hope this chapter will serve as a milestone in guiding the development (i.e., engineering and research) activities in fulfilling the vision of AICAs.

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References

  • Al-Shaer, E., Wei, J., Hamlen, K., & Wang, C. (2019). Autonomous cyber deception – Reasoning, adaptive planning, and evaluation of HoneyThings. Springer. ISBN 978-3-030-02109-2.

    Book  Google Scholar 

  • Bagchi, S., Aggarwal, V., Chaterji, S., Douglis, F., El Gamal, A., Han, J., Henz, b., Hoffmann, H., Jana, S., Kulkarni, M., Lin, F., Marais, K., Mittal, P., Mou, S., Qiu, X., & Scutari, G. (2020). Vision paper: Grand challenges in resilience: Autonomous system resilience through design and runtime measures. IEEE Open Journal of the Computer Society, 1, 155–172.

    Article  Google Scholar 

  • Chen, H., Cho, J., & Xu, S. (2018). Quantifying the security effectiveness of firewalls and DMZs. HotSoS 2018, pp. 9:1–9:11.

    Google Scholar 

  • Chen, H., Cam, H., & Xu, S. (2021). Quantifying cybersecurity effectiveness of dynamic network diversity. Accepted to IEEE Transactions on Dependable and Secure Computing.

    Google Scholar 

  • Cho, J., Hurley, P., & Xu, S. (2016). Metrics and measurement of trustworthy systems. MILCOM 2016, pp. 1237–1242.

    Google Scholar 

  • Cho, J., Xu, S., Hurley, P., Mackay, M., Benjamin, T., & Beaumont, M. (2019, November). STRAM: Measuring the trustworthiness of computer-based systems. ACM Computing Surveys, 51(6), Article No.: 128, 1–47. https://doi.org/10.1145/3277666

  • Clark, M. (2008, February 4). Defense of self: How the immune system really works (Illustrated ed.). Oxford University Press. ISBN-13: 978-0195335552, ISBN-10: 0195335554.

    Google Scholar 

  • Da, G., Xu, M., & Xu, S. (2014). A new approach to modeling and analyzing security of networked systems. HotSoS 2014, p. 6.

    Google Scholar 

  • Dai, W., Parker, T., Jin, H., & Xu, S. (2012). Enhancing data trustworthiness via assured digital signing. IEEE Transactions on Dependable and Secure Computing, 9(6), 838–851.

    Article  Google Scholar 

  • De Gaspari, F., Jajodia, S., Mancini, L., & Panico, A. (2016). AHEAD: A new architecture for active defense. SafeConfig@CCS 2016, pp. 11–16.

    Google Scholar 

  • Dodis, Y., Katz, J., Xu, S., & Yung, M. (2003). Strong key-insulated signature schemes. Public Key Cryptography 2003, pp. 130–144.

    Google Scholar 

  • Fang, Z., Xu, M., Xu, S., & Hu, T. (2021). A framework for predicting data breach risk: Leveraging dependence to cope with sparsity. IEEE Transactions on Information Forensics and Security, 16, 2186–2201.

    Article  Google Scholar 

  • Han, Y., Lu, W., & Xu, S. (2014). Characterizing the power of moving target defense via cyber epidemic dynamics. HotSoS 2014, p. 10.

    Google Scholar 

  • Han, Y., Lu, W., & Xu, S. (2021). Preventive and reactive cyber defense dynamics with ergodic time-dependent parameters is globally attractive. IEEE Transactions on Network Science and Engineering, 8(3), 2517–2532.

    Article  MathSciNet  Google Scholar 

  • Kott, K., & Linkov, I. (2021). To improve cyber resilience, measure it. Computer, 54(2), 80–85.

    Article  Google Scholar 

  • Kott, A., & Théron, P. (2020). Doers, not watchers: Intelligent autonomous agents are a path to cyber resilience. IEEE Security and Privacy, 18(3), 62–66.

    Article  Google Scholar 

  • Kott, A., Théron, P., Drašar, M., Dushku, E., LeBlanc, B., Losiewicz, P., Guarino, A., Mancini, L., Panico, A., Pihelgas, M., & Rzadca, K. (2018). Autonomous Intelligent Cyber-defense Agent (AICA) reference architecture. Release 2.0. arXiv:1803.10664.

    Google Scholar 

  • Kott, A., Golan, M., Trump, B., & Linkov, I. (2021). Cyber resilience: By design or by intervention? Computer, 54(8), 112–117.

    Article  Google Scholar 

  • Kraus, A., Buckley, K., & Salinas, I. (2021, April). Sensing the world and its dangers: An evolutionary perspective in neuroimmunology. eLife, 10, e66706. https://doi.org/10.7554/eLife.66706

  • Li, X., Parker, P., & Xu, S. (2007). Towards quantifying the (in)security of networked systems. AINA 2007, pp. 420–427.

    Google Scholar 

  • Li, X., Parker, P., & Xu, S. (2011). A stochastic model for quantitative security analyses of networked systems. IEEE Transactions on Dependable and Secure Computing, 8(1), 28–43.

    Article  Google Scholar 

  • Li, J., Zhao, B., & Zhang, C. (2018a). Fuzzing: A survey. Cybersecurity, 1(1), 6.

    Article  Google Scholar 

  • Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., & Zhong, Y. (2018b) VulDeePecker: A deep learning-based system for vulnerability detection. Proceedings of NDSS’2018.

    Google Scholar 

  • Li, D., Li, Q., Ye, Y., & Xu, S. (2021a). A framework for enhancing deep neural networks against adversarial malware. IEEE Transactions on Network Science and Engineering, 8(1), 736–750.

    Article  MathSciNet  Google Scholar 

  • Li, D., Qiu, T., Chen, S., Li, Q., & Xu, S. (2021b). Can we leverage predictive uncertainty to detect dataset shift and adversarial examples in android malware detection? ACSAC 2021, pp. 596–608.

    Google Scholar 

  • Li, Z., Zou, D., Xu, S., Chen, Z., Zhu, Y., & Jin, H. (2022a). VulDeeLocator: A deep learning-based fine-grained vulnerability detector. IEEE TDSC 2022, to appear.

    Google Scholar 

  • Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., Chen, Z., Wang, S., & Wang, J. (2022b). SySeVR: A framework for using deep learning to detect software vulnerabilities. IEEE TDSC 2022, to appear.

    Google Scholar 

  • Li, D., Li, Q., Ye, Y., & Xu, S. (2023, January). Arms race in adversarial malware detection: A survey. ACM Computing Survey, 55(1), Article No.: 15, 1–35. https://doi.org/10.1145/3484491

  • Ligo, A., Kott, A., & Linkov, I. (2021). Autonomous cyberdefense introduces risk: Can we manage the risk? Computer, 54(10), 106–110.

    Article  Google Scholar 

  • Lin, Z., Lu, W., & Xu, S. (2019). Unified preventive and reactive cyber defense dynamics is still globally convergent. IEEE/ACM Transactions on Networking, 27(3), 1098–1111.

    Article  Google Scholar 

  • Longtchi, T., Rodriguez, R., Al-Shawaf, L., Atyabi, A., & Xu, S. (2022). SoK: Why have defenses against social engineering attacks achieved limited success? arXiv preprint arXiv:2203.08302.

    Google Scholar 

  • Lu, W., Xu, S., & Yi, X. (2013). Optimizing active cyber defense. GameSec 2013, pp. 206–225.

    Google Scholar 

  • Lu, Z., Wang, C., & Zhao, S. (2020). Cyber deception for computer and network security: Survey and challenges. CoRR abs/2007.14497.

    Google Scholar 

  • Mireles, J., Ficke, E., Cho, J., Hurley, P., & Xu, S. (2019). Metrics towards measuring cyber agility. IEEE Transactions on Information Forensics and Security, 14(12), 3217–3232.

    Article  Google Scholar 

  • Pendleton, M., Garcia-Lebron, R., Cho, J., & Xu, S. (2017). A survey on systems security metrics. ACM Computing Surveys, 49(4), 62:1–62:35.

    Article  Google Scholar 

  • Practical Law Intellectual Property & Technology. (2022). Artificial intelligence key legal issues: Overview. https://content.next.westlaw.com/Document/Ibc68c39002d611e9a5b3e3d9e23d7429/View/FullText.html?transitionType=Default&contextData=(sc.Default)&firstPage=true. Accessed 3 Jan 2022.

  • Rodriguez, R., Golob, E., & Xu, S. (2020, September). Human cognition through the lens of social engineering cyberattacks. Frontiers in Psychology, 30. https://doi.org/10.3389/fpsyg.2020.01755

  • Rodriguez, R., Atyabi, A., & Xu, S. (2022). Social engineering attacks and defenses in the physical world vs. cyberspace a contrast study. Invited book chapter to “Cybersecurity and Cognitive Science”.

    Google Scholar 

  • Schiller, M., Ben-Shaanan, T., & Rolls, A. (2021). Neuronal regulation of immunity: Why, how and where? Nature Reviews Immunology, 21, 20–36. https://doi.org/10.1038/s41577-020-0387-1

    Article  Google Scholar 

  • Steinman, L. (2004). Elaborate interactions between the immune and nervous systems. Nature Immunology, 5, 575–581. https://doi.org/10.1038/ni1078

    Article  Google Scholar 

  • Théron, P., & Kott, A. (2019). When autonomous intelligent goodware will fight autonomous intelligent malware: A possible future of cyber defense. MILCOM 2019, pp. 1–7.

    Google Scholar 

  • Tsudik, G., & Xu, S. (2006). A flexible framework for secret handshakes. Privacy Enhancing Technologies 2006, pp. 295–315.

    Google Scholar 

  • U.S. Government Accountability Office. (2021, April 22). SolarWinds cyberattack demands significant federal and private-sector response (infographic). https://www.gao.gov/blog/solarwinds-cyberattack-demands-significant-federal-and-private-sector-response-infographic. Accessed on 22 Mar 2022.

  • United States Department of Defense. (2020). DOD adopts ethical principles for artificial intelligence. https://www.defense.gov/News/Releases/Release/Article/2091996/dod-adopts-ethical-principles-for-artificial-intelligence/

  • Vought, R. (2020). Guidance for regulation of artificial intelligence applications. https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf

  • Wang, C., & Lu, Z. (2018). Cyber deception: Overview and the road ahead. IEEE Security and Privacy, 16(2), 80–85.

    Article  Google Scholar 

  • Xu, S. (2014a). Cybersecurity dynamics. HotSoS 2014, p. 14.

    Google Scholar 

  • Xu, S. (2014b). Emergent behavior in cybersecurity. HotSoS 2014, p. 13.

    Google Scholar 

  • Xu, S. (2019). Cybersecurity dynamics: A foundation for the science of cybersecurity. In C. Wang & Z. Lu (Eds.), Proactive and dynamic network defense (Advances in information security) (Vol. 74). Springer. https://doi.org/10.1007/978-3-030-10597-6_1

    Chapter  Google Scholar 

  • Xu, S. (2020). The cybersecurity dynamics way of thinking and landscape. In The 2020 ACM workshop on moving target defense, pp. 69–80.

    Google Scholar 

  • Xu, S. (2021). SARR: A cybersecurity metrics and quantification framework (Keynote). SciSec 2021, pp. 3–17.

    Google Scholar 

  • Xu, M., & Xu, S. (2012). An extended stochastic model for quantitative security analysis of networked systems. Internet Mathematics, 8(3), 288–320.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, S., & Yung, M. (2004). k-anonymous secret handshakes with reusable credentials. ACM CCS 2004, pp. 158–167.

    Google Scholar 

  • Xu, S., & Yung, M. (2007). K-anonymous multi-party secret handshakes. Financial cryptography 2007, pp. 72–87.

    Google Scholar 

  • Xu, S., & Yung, M. (2009). Expecting the unexpected: Towards robust credential infrastructure. Financial cryptography 2009, pp. 201–221.

    Google Scholar 

  • Xu, S., Li, X., Parker, P., & Wang, X. (2011). Exploiting trust-based social networks for distributed protection of sensitive data. IEEE Transactions on Information Forensics and Security, 6(1), 39–52.

    Article  Google Scholar 

  • Xu, S., Lu, W., & Xu, L. (2012a). Push- and pull-based epidemic spreading in networks: Thresholds and deeper insights. ACM Transactions on Autonomous and Adaptive Systems, 7(3), 32:1–32:26.

    Article  Google Scholar 

  • Xu, S., Lu, W., & Zhan, Z. (2012b). A stochastic model of multivirus dynamics. IEEE Transactions on Dependable and Secure Computing, 9(1), 30–45.

    Article  Google Scholar 

  • Xu, S., Lu, W., Xu, L., & Zhan, Z. (2014). Adaptive epidemic dynamics in networks: Thresholds and control. ACM Transactions on Autonomous and Adaptive Systems, 8(4), 19:1–19:19.

    Article  Google Scholar 

  • Xu, M., Da, G., & Xu, S. (2015a). Cyber epidemic models with dependences. Internet Mathematics, 11(1), 62–92.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, S., Lu, W., & Li, H. (2015b). A stochastic model of active cyber defense dynamics. Internet Mathematics, 11(1), 23–61.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, M., Hua, L., & Xu, S. (2017). A vine copula model for predicting the effectiveness of cyber defense early-warning. Technometrics, 59(4), 508–520.

    Article  MathSciNet  Google Scholar 

  • Xu, M., Schweitzer, K., Bateman, R., & Xu, S. (2018). Modeling and predicting cyber hacking breaches. IEEE Transactions on Information Forensics and Security, 13(11), 2856–2871.

    Article  Google Scholar 

  • Xu, L., Chen, L., Gao, Z., Fan, X., Doan, K., Xu, S., & Shi, W. (2019). KCRS: A blockchain-based key compromise resilient signature system. BlockSys 2019, pp. 226–239.

    Google Scholar 

  • Zheng, R., Lu, W., & Xu, S. (2015). Active cyber defense dynamics exhibiting rich phenomena. HotSoS 2015, pp. 2:1–2:12.

    Google Scholar 

  • Zheng, R., Lu, W., & Xu, S. (2018). Preventive and reactive cyber defense dynamics is globally stable. IEEE Transactions on Network Science and Engineering, 5(2), 156–170.

    Article  MathSciNet  Google Scholar 

  • Zou, D., Wang, S., Xu, S., Li, Z., & Jin, H. (2021a). μVulDeePecker: A deep learning-based system for multiclass vulnerability detection. IEEE Transactions on Dependable and Secure Computing, 18(5), 2224–2236.

    Google Scholar 

  • Zou, D., Zhu, Y., Xu, S., Li, Z., Jin, H., & Ye, H. (2021b). Interpreting deep learning-based vulnerability detector predictions based on Heuristic searching. ACM Transactions on Software Engineering and Methodology, 30(2), 23:1–23:31.

    Article  Google Scholar 

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Acknowledgement

We thank Dr. Alexander Kott for his constructive feedbacks that guided us in revising the content. The work was supported in part by ARO Grant #W911NF-17-1-0566, NSF Grants #2122631 and #2115134, and Colorado State Bill 18-086.

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Correspondence to Shouhuai Xu .

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Xu, S. (2023). AICA Development Challenges. In: Kott, A. (eds) Autonomous Intelligent Cyber Defense Agent (AICA). Advances in Information Security, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-29269-9_18

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