Leveraging Business Intelligence and AI-Driven Analytics to Strengthen U.S. Cybersecurity Infrastructure
DOI:
https://doi.org/10.15662/IJEETR.2025.0702003Keywords:
Business Intelligence (BI), Artificial Intelligence (AI), Cybersecurity, Predictive Analytics, National Threat DetectionAbstract
The United States government and the private sector's increasing reliance on digital technologies has heightened their vulnerability to advanced cyber threats. Traditional reactive cybersecurity measures are insufficient for mitigating the threat posed by ever-changing attack vectors, and there is a strong need for predictive, data-driven defense measures. This paper discusses the opportunities for using Business Intelligence (BI) systems and Artificial Intelligence (AI)-driven analytics to enhance the national cybersecurity infrastructure by proactively detecting and shaping threats, enabling intelligent cybersecurity monitoring, and enabling predictive assessment. The research combines data from the available literature and national datasets to develop a BI-AI analytical framework that can detect anomalies, predict potential breaches, and reduce response times across distributed networks. Emphasis is placed on integrating machine learning (ML) models, such as random forests, support vector machines (SVMs), and neural networks, into BI dashboards to enhance situational awareness and automated decision-making. A comparative evaluation shows that AI-enhanced BI systems outperform conventional rule-based methods in terms of accuracy, precision, and scalability [3]; [7]. The research provides an important signal that unifying threat intelligence platforms, cloud-based BI infrastructure, and explainable AI is essential to ensuring the resilience and transparency of cybersecurity operations. Moreover, the study highlights the importance of public-private partnerships and policy-based governance in implementing data-centric defense frameworks at the national level. Findings offer a blueprint for introducing predictive analytics into U.S. cybersecurity ecosystems to reduce systemic vulnerabilities and enable adaptive, real-time defense strategies.
References
[1] Blum, A., Ding, D., &Endres, R. (2019). The role of threat intelligence for proactive defense: Opportunities and challenges. Journal of Cybersecurity, 5(1). https://doi.org/10.1093/cybsec/tyz009
[2] Bridges, S. M., & Vaughn, R. B. (2000). Fuzzy data mining and genetic algorithms applied to intrusion detection. National Information Systems Security Conference, 13–31. https://nvlpubs.nist.gov
[3] Buczak, A. L., &Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
[4] Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. ACM Computing Surveys, 51(3), 1–36. https://doi.org/10.1145/3243213
[5] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882
[6] Conti, M., Dehghantanha, A., Choo, K. K. R., & Watson, S. (2018). Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems, 78, 544–546. https://doi.org/10.1016/j.future.2017.07.060
[7] Cui, L., Wang, H., Xu, W., & Sun, J. (2019). Machine learning for network intrusion detection: A comprehensive survey. IEEE Communications Surveys & Tutorials, 21(4), 3553–3576. https://doi.org/10.1109/COMST.2019.2902130
[8] European Union Agency for Cybersecurity (ENISA). (2020). ENISA Threat Landscape 2020 – Mid-Year Update. https://www.enisa.europa.eu
[9] Executive Office of the President. (2017). Executive Order 13800 — Strengthening the Cybersecurity of Federal Networks and Critical Infrastructure. The White House. https://www.federalregister.gov/documents/2017/05/16/2017-10004
[10] Ferrag, M. A., Shu, L., & Jiang, J. (2018). Authentication protocols for Internet of Things: A survey. Security and Communication Networks, 2018, Article 1218047. https://doi.org/10.1155/2018/1218047
[11] Furnell, S. M., & Clarke, N. P. (2018). Integrating business intelligence and security: Challenges and approaches. Computers & Security, 74, 1–13. https://doi.org/10.1016/j.cose.2017.11.010
[12] Goodfellow, I. J., Bengio, Y., &Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org
[13] Goodfellow, D., Shlens, J., &Szegedy, C. (2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1412.6572
[14] IBM Security. (2020). IBM X-Force Threat Intelligence Index 2020. IBM Corporation. https://www.ibm.com/reports/threat-intelligence
[15] Khraisat, A., Gondal, I., Vamplew, P., &Kamruzzaman, J. (2019). Survey of intrusion detection systems: Techniques, datasets and challenges. Cybersecurity, 2(1), 1–22. https://doi.org/10.1186/s42400-019-0038-7
[16] Kruegel, C., Valeur, F., &Vigna, G. (2004). Intrusion detection and correlation: Challenges and approaches. Advances in Information Security, 18, Springer. https://doi.org/10.1007/978-0-387-35512-9
[17] Lippmann, R. P., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. DARPA Information Survivability Conference and Exposition, 12–26. https://doi.org/10.1109/DISCEX.2000.821515
[18] McHugh, J. M. (2000). Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Transactions on Information and System Security, 3(4), 262–294. https://doi.org/10.1145/382912.382923
[19] National Institute of Standards and Technology (NIST). (2018). Framework for Improving Critical Infrastructure Cybersecurity, Version 1.1. NIST, Gaithersburg, MD. https://doi.org/10.6028/NIST.CSWP.04162018
[20] Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z., & Swami, A. (2016). The limitations of deep learning in adversarial settings. IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 372–387. https://doi.org/10.1109/EuroSPW.2016.36
[21] Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448–3470. https://doi.org/10.1016/j.comnet.2007.02.001
[22] Ring, M., Wunderlich, S., Scheuring, D., Landes, D., &Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers & Security, 86, 147–167. https://doi.org/10.1016/j.cose.2019.06.005
[23] Sharafaldin, I., Lashkari, A. H., &Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Information Systems Security and Privacy (ICISSP 2018), 108–116. https://doi.org/10.5220/0006639801080116
[24] Somani, A., Sethi, A. K., Kumar, P., &Jha, R. K. (2016). A study on machine learning approaches for anomaly detection in cyber-security. Procedia Computer Science, 93, 116–123. https://doi.org/10.1016/j.procs.2016.07.197
[25] Sommer, P., &Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25
[26] Symantec (Broadcom). (2019). Internet Security Threat Report (ISTR) 2019. https://www.broadcom.com/company/newsroom/press-releases?cat=istr
[27] Tavallaee, M., Bagheri, E., Lu, W., &Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1–6. https://doi.org/10.1109/CISDA.2009.5356528
[28] Thomas, T. (2019). Security information and event management (SIEM) systems: A comprehensive review and future research directions. Journal of Information Security and Applications, 45, 1–15. https://doi.org/10.1016/j.jisa.2019.01.005
[29] Verizon Enterprise Solutions. (2020). 2020 Data Breach Investigations Report (DBIR). https://enterprise.verizon.com/resources/reports/dbir/
[30] Zargar, S., Joshi, J., & Tipper, D. (2013). A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Communications Surveys & Tutorials, 15(4), 2046–2069. https://doi.org/10.1109/SURV.2013.031413.00127
