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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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.
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.
Chen, H., Cho, J., & Xu, S. (2018). Quantifying the security effectiveness of firewalls and DMZs. HotSoS 2018, pp. 9:1–9:11.
Chen, H., Cam, H., & Xu, S. (2021). Quantifying cybersecurity effectiveness of dynamic network diversity. Accepted to IEEE Transactions on Dependable and Secure Computing.
Cho, J., Hurley, P., & Xu, S. (2016). Metrics and measurement of trustworthy systems. MILCOM 2016, pp. 1237–1242.
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.
Da, G., Xu, M., & Xu, S. (2014). A new approach to modeling and analyzing security of networked systems. HotSoS 2014, p. 6.
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.
De Gaspari, F., Jajodia, S., Mancini, L., & Panico, A. (2016). AHEAD: A new architecture for active defense. SafeConfig@CCS 2016, pp. 11–16.
Dodis, Y., Katz, J., Xu, S., & Yung, M. (2003). Strong key-insulated signature schemes. Public Key Cryptography 2003, pp. 130–144.
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.
Han, Y., Lu, W., & Xu, S. (2014). Characterizing the power of moving target defense via cyber epidemic dynamics. HotSoS 2014, p. 10.
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.
Kott, K., & Linkov, I. (2021). To improve cyber resilience, measure it. Computer, 54(2), 80–85.
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.
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.
Kott, A., Golan, M., Trump, B., & Linkov, I. (2021). Cyber resilience: By design or by intervention? Computer, 54(8), 112–117.
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.
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.
Li, J., Zhao, B., & Zhang, C. (2018a). Fuzzing: A survey. Cybersecurity, 1(1), 6.
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.
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.
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.
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.
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.
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.
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.
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.
Lu, W., Xu, S., & Yi, X. (2013). Optimizing active cyber defense. GameSec 2013, pp. 206–225.
Lu, Z., Wang, C., & Zhao, S. (2020). Cyber deception for computer and network security: Survey and challenges. CoRR abs/2007.14497.
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.
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.
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”.
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
Steinman, L. (2004). Elaborate interactions between the immune and nervous systems. Nature Immunology, 5, 575–581. https://doi.org/10.1038/ni1078
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.
Tsudik, G., & Xu, S. (2006). A flexible framework for secret handshakes. Privacy Enhancing Technologies 2006, pp. 295–315.
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.
Xu, S. (2014a). Cybersecurity dynamics. HotSoS 2014, p. 14.
Xu, S. (2014b). Emergent behavior in cybersecurity. HotSoS 2014, p. 13.
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
Xu, S. (2020). The cybersecurity dynamics way of thinking and landscape. In The 2020 ACM workshop on moving target defense, pp. 69–80.
Xu, S. (2021). SARR: A cybersecurity metrics and quantification framework (Keynote). SciSec 2021, pp. 3–17.
Xu, M., & Xu, S. (2012). An extended stochastic model for quantitative security analysis of networked systems. Internet Mathematics, 8(3), 288–320.
Xu, S., & Yung, M. (2004). k-anonymous secret handshakes with reusable credentials. ACM CCS 2004, pp. 158–167.
Xu, S., & Yung, M. (2007). K-anonymous multi-party secret handshakes. Financial cryptography 2007, pp. 72–87.
Xu, S., & Yung, M. (2009). Expecting the unexpected: Towards robust credential infrastructure. Financial cryptography 2009, pp. 201–221.
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.
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.
Xu, S., Lu, W., & Zhan, Z. (2012b). A stochastic model of multivirus dynamics. IEEE Transactions on Dependable and Secure Computing, 9(1), 30–45.
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.
Xu, M., Da, G., & Xu, S. (2015a). Cyber epidemic models with dependences. Internet Mathematics, 11(1), 62–92.
Xu, S., Lu, W., & Li, H. (2015b). A stochastic model of active cyber defense dynamics. Internet Mathematics, 11(1), 23–61.
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.
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.
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.
Zheng, R., Lu, W., & Xu, S. (2015). Active cyber defense dynamics exhibiting rich phenomena. HotSoS 2015, pp. 2:1–2:12.
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.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-29269-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29268-2
Online ISBN: 978-3-031-29269-9
eBook Packages: Computer ScienceComputer Science (R0)