AI-Driven Threat Detection and Response Systems for Secure National Infrastructure Networks: A Comprehensive Review

Article Sidebar

Main Article Content

Akinkunle Akinloye.
Sunday Anwansedo
Oladayo Tosin Akinwande

Abstract: Due to the increased complexity and damage of cyberattacks in this digital age, the security of national infrastructure networks has become a vital concern. However, a possible approach to improve the cybersecurity of these crucial networks is to incorporate artificial intelligence (AI) into threat detection and response systems; to rapidly evaluate large data sets, identify anomalies, and automate countermeasures to lessen the effects of cyberattacks. The impact, implementation and approaches for anomaly detection and response automation of AI-powered solutions for safeguarding national infrastructure are examined in this paper. Understanding how AI technologies are used to automate threat detection and response, reviewing the operational usefulness of AI in enhancing cybersecurity measures and evaluating the deployment of these systems in critical infrastructure settings were also examined. The study revealed that the speed and accuracy of threat detection and response are greatly increased by AI-powered systems. The automation capacity of AI can potentially reduce the need for human analysts, while also providing faster threat mitigation. Additionally, the usefulness of AI across sectors indicates its practicality in situations and how it may adapt in response to new threats. In conclusion, AI-driven threat detection and response systems are an important development in national infrastructure network cybersecurity. Therefore, by improving the capacity to recognize and address cyber-attacks these technologies can ultimately increase the overall resilience of national infrastructures.

AI-Driven Threat Detection and Response Systems for Secure National Infrastructure Networks: A Comprehensive Review. (2024). International Journal of Latest Technology in Engineering Management & Applied Science, 13(7), 82-92. https://doi.org/10.51583/IJLTEMAS.2024.130710

Downloads

Downloads

Download data is not yet available.

References

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189 DOI: https://doi.org/10.3390/su11010189

Ade-Ibijola, A., & Okonkwo, C. (2023). Artificial Intelligence in Africa: Emerging Challenges. Social and Cultural Studies of Robots and AI, 101–117. https://doi.org/10.1007/978-3-031-08215-3_5 DOI: https://doi.org/10.1007/978-3-031-08215-3_5

Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102–109. https://doi.org/10.1016/j.rser.2014.01.069 DOI: https://doi.org/10.1016/j.rser.2014.01.069

Al Aani, S., Bonny, T., Hasan, S. W., & Hilal, N. (2019). Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? Desalination, 458, 84–96. https://doi.org/10.1016/j.desal.2019.02.005 DOI: https://doi.org/10.1016/j.desal.2019.02.005

Alanazi, R., & Aljuhani, A. (2023). Anomaly Detection for Industrial Internet of Things Cyberattacks. Computer Systems Science and Engineering, 44(3), 2361–2378. https://doi.org/10.32604/csse.2023.026712 DOI: https://doi.org/10.32604/csse.2023.026712

Alhosani K, & Alhashmi, S. M. (2024). Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00111-w DOI: https://doi.org/10.1007/s44163-024-00111-w

Aminu M., Anawansedo, S., Yusuf Ademola Sodiq, & Oladayo Tosin Akinwande. (2024). Driving Technological Innovation for a Resilient Cybersecurity Landscape. International Journal of Latest Technology in Engineering Management & Applied Science, XIII(IV), 126–133. https://doi.org/10.51583/ijltemas.2024.130414 DOI: https://doi.org/10.51583/IJLTEMAS.2024.130414

Antunes, A., Andrade-Campos, A., Sardinha-Lourenço, A., & Oliveira, M. S. (2018). Short-term water demand forecasting using machine learning techniques. Journal of Hydro informatics, 20(6), 1343–1366. https://doi.org/10.2166/hydro.2018.163 DOI: https://doi.org/10.2166/hydro.2018.163

Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yilmaz, A. A., & Akin, E. (2023). A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions. Electronics, 12(6), 1333. https://doi.org/10.3390/electronics12061333 DOI: https://doi.org/10.3390/electronics12061333

Audibert, R. B., Lemos, H., Pedro, Tavares, A. R., & Lamb, L. C. (2022). On the Evolution of A.I. and Machine Learning: Towards Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences. https://doi.org/10.48550/arxiv.2205.13131

Aven, T. (2016). Risk Assessment and Risk management: Review of Recent Advances on Their Foundation. European Journal of Operational Research, 253(1), 1–13. Science Direct. https://doi.org/10.1016/j.ejor.2015.12.023 DOI: https://doi.org/10.1016/j.ejor.2015.12.023

Barrett, M. P. (2018). Framework for Improving Critical Infrastructure Cybersecurity Version 1.1. NIST Cybersecurity Framework. http://dx.doi.org/10.1002/https://dx.doi.org/10.6028/NIST.CSWP.04162018

Bhardwaj, M. (2023). AI and Cyber Security with Reference to Military/ Defense. International Journal of Trend in Research and Development, 10(4), 2394–9333. https://www.ijtrd.com/papers/IJTRD26997.pdf

Bouhana, A., Zidi, A., Fekih, A., Chabchoub, H., & Abed, M. (2015). An ontology-based CBR approach for personalized itinerary search systems for sustainable urban freight transport. Expert Systems with Applications, 42(7), 3724–3741. https://doi.org/10.1016/j.eswa.2014.12.012 DOI: https://doi.org/10.1016/j.eswa.2014.12.012

Carballo, J. A., Bonilla, J., Berenguel, M., Fernández-Reche, J., & García, G. (2019). New approach for solar tracking systems based on computer vision, low cost hardware and deep learning. Renewable Energy, 133, 1158–1166. https://doi.org/10.1016/j.renene.2018.08.101 DOI: https://doi.org/10.1016/j.renene.2018.08.101

Casas, P., D’Alconzo, A., Wamser, F., Seufert, M., Gardlo, B., Schwind, A., Tran-Gia, P., & Schatz, R. (2017). Predicting QoE in cellular networks using machine learning and in-smartphone measurements. 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). https://doi.org/10.1109/qomex.2017.7965687 DOI: https://doi.org/10.1109/QoMEX.2017.7965687

Chahal, S. (2023). AI-Enhanced Cyber Incident Response and Recovery. International Journal of Science and Research, 12(3), 1795–1801. https://doi.org/10.21275/sr231003163025 DOI: https://doi.org/10.21275/SR231003163025

Chen, P., Wu, L., & Wang, L. (2023). AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications. Applied Sciences, 13(18), 10258–10258. https://doi.org/10.3390/app131810258 DOI: https://doi.org/10.3390/app131810258

Conde-Clemente, P., Alonso, J. L., & Gracián Triviño. (2018). Toward automatic generation of linguistic advice for saving energy at home. Soft Comput., 22(2), 345–359. https://doi.org/10.1007/s00500-016-2430-5 DOI: https://doi.org/10.1007/s00500-016-2430-5

Daniel, & Segun, S. (2024). EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW. Computer Science & IT Research Journal, 5(3), 576–593. https://doi.org/10.51594/csitrj.v5i3.872 DOI: https://doi.org/10.51594/csitrj.v5i3.872

Doğan, E., & Akgüngör, A. P. (2011). Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks. Neural Computing and Applications, 22(5), 869–877. https://doi.org/10.1007/s00521-011-0778-0 DOI: https://doi.org/10.1007/s00521-011-0778-0

Ekpenyong, F., Palmer-Brown, D., & Brimi combe, A. (2009). Extracting road information from recorded GPS data using snap-drift neural network. Neurocomputing, 73(1-3), 24–36. https://doi.org/10.1016/j.neucom.2008.11.032 DOI: https://doi.org/10.1016/j.neucom.2008.11.032

Fan, M., Hu, J., Cao, R., Ruan, W., & Wei, X. (2018). A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence. Chemosphere, 200, 330–343. https://doi.org/10.1016/j.chemosphere.2018.02.111 DOI: https://doi.org/10.1016/j.chemosphere.2018.02.111

Flammini, F., Pragliola, C., & Smarra, G. (2016, November 1). Railway infrastructure monitoring by drones. IEEE Xplore. https://doi.org/10.1109/ESARS-ITEC.2016.7841398 DOI: https://doi.org/10.1109/ESARS-ITEC.2016.7841398

GAVAGHAN, C., KNOTT, A., & MACLAURIN, J. (2021). The Impact of Artificial Intelligence on Jobs and Work in New Zealand. https://www.otago.ac.nz/__data/assets/pdf_file/0012/312060/https-wwwotagoacnz-caipp-otago828396pdf-828396.pdf

Ghadge, N. (2024). Enhancing threat detection in Identity and Access Management (IAM) systems. International Journal of Science and Research Archive, 11(2), 2050–2057. https://doi.org/10.30574/ijsra.2024.11.2.0761 DOI: https://doi.org/10.30574/ijsra.2024.11.2.0761

Ghaffarian, S., Taghikhah, F. R., & Maier, H. R. (2023). Explainable artificial intelligence in disaster risk management: Achievements and prospective futures. International Journal of Disaster Risk Reduction, 98, 104123. https://doi.org/10.1016/j.ijdrr.2023.104123 DOI: https://doi.org/10.1016/j.ijdrr.2023.104123

Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. Energy Economics, 81, 709–727. https://doi.org/10.1016/j.eneco.2019.05.006 DOI: https://doi.org/10.1016/j.eneco.2019.05.006

Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208–218. https://doi.org/10.1049/trit.2018.1008 DOI: https://doi.org/10.1049/trit.2018.1008

Gkioka, G., Dominguez, M., Athina Tympakianaki, & Gregoris Mentzas. (2024). AI-Driven Real-Time Incident Detection for Intelligent Transportation Systems. Advances in Transdisciplinary Engineering. https://doi.org/10.3233/atde240021 DOI: https://doi.org/10.3233/ATDE240021

Granata, F., Papirio, S., Esposito, G., Gargano, R., & De Marinis, G. (2017). Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators. Water, 9(2), 105. https://doi.org/10.3390/w9020105 DOI: https://doi.org/10.3390/w9020105

Gulenko, A., Wall schlager, M., Schmidt, F. I., Kao, O., & Liu, F. (2016). Evaluating machine learning algorithms for anomaly detection in clouds. IEEE International Conference on Big Data (Big Data) (2016), https://doi.org/10.1109/bigdata.2016.7840917 DOI: https://doi.org/10.1109/BigData.2016.7840917

Jada, I., & Mayayise, T. O. (2023). The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review. Data and Information Management, 100063–100063. https://doi.org/10.1016/j.dim.2023.100063 DOI: https://doi.org/10.1016/j.dim.2023.100063

Jiang, W., & Zhang, L. (2019). Geospatial data to images: A deep-learning framework for traffic forecasting. Tsinghua Science and Technology, 24(1), 52–64. https://doi.org/10.26599/TST.2018.9010033 DOI: https://doi.org/10.26599/TST.2018.9010033

Kang, Y., Cai, Z., Tan, C.-W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172. https://doi.org/10.1080/23270012.2020.1756939 DOI: https://doi.org/10.1080/23270012.2020.1756939

Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial Intelligence for Cybersecurity: Literature Review and Future Research Directions. Information Fusion, 97(101804), 101804. Science direct. https://doi.org/10.1016/j.inffus.2023.101804 DOI: https://doi.org/10.1016/j.inffus.2023.101804

Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behaviour research: a review. Transport Reviews, 40(3), 288–311. https://doi.org/10.1080/01441647.2019.1704307 DOI: https://doi.org/10.1080/01441647.2019.1704307

Li, L., Rong, S., Wang, R., & Yu, S. (2021). Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review. Chemical Engineering Journal, 405, 126673. https://doi.org/10.1016/j.cej.2020.126673 DOI: https://doi.org/10.1016/j.cej.2020.126673

Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017). Intelligent 5G: When Cellular Networks Meet Artificial Intelligence. IEEE Wireless Communications, 24(5), 175–183. https://doi.org/10.1109/mwc.2017.1600304wc DOI: https://doi.org/10.1109/MWC.2017.1600304WC

Li, Y., & Liu, Q. (2021). A comprehensive review study of cyber-attacks and cyber security; emerging trends and recent developments. Energy Reports, 7(7), 8176–8186. Science direct. https://doi.org/10.1016/j.egyr.2021.08.126 DOI: https://doi.org/10.1016/j.egyr.2021.08.126

Liu, Q., Veit Hagenmeyer, & Keller, H. B. (2021). A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids. IEEE Access, 9, 57542–57564. https://doi.org/10.1109/access.2021.3071263 DOI: https://doi.org/10.1109/ACCESS.2021.3071263

Lv, Z., Singh, A. K., & Li, J. (2021). Deep Learning for Security Problems in 5G Heterogeneous Networks. IEEE Network, 35(2), 1–8. https://doi.org/10.1109/mnet.011.2000229 DOI: https://doi.org/10.1109/MNET.011.2000229

Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020). Artificial intelligence applications in the development of autonomous vehicles: a survey. IEEE/CAA Journal of Automatica Sinica, 7(2), 315–329. https://doi.org/10.1109/jas.2020.1003021 DOI: https://doi.org/10.1109/JAS.2020.1003021

Macedo, M. N. Q., Galo, J. J. M., de Almeida, L. A. L., & de C. Lima, A. C. (2015). Demand side management using artificial neural networks in a smart grid environment. Renewable and Sustainable Energy Reviews, 41, 128–133. https://doi.org/10.1016/j.rser.2014.08.035 DOI: https://doi.org/10.1016/j.rser.2014.08.035

Maple, C., Szpruch, L., Epiphaniou, G., Staykova, K., Singh, S., & Penwarden, W. (2023). The AI Revolution: Opportunities and Challenges for the Finance Sector. The Alan Turing Institute.

Mardanghom, R., Sandal, H., & Xunhua, S. (2019). Artificial Intelligence in Financial Services. https://core.ac.uk/download/pdf/288306886.pdf

Markevych, M., & Dawson, M. (2023). A Review of Enhancing Intrusion Detection Systems for Cybersecurity Using Artificial Intelligence (AI). International Conference Knowledge Based Organization, 29(3), 30–37. https://doi.org/10.2478/kbo-2023-0072 DOI: https://doi.org/10.2478/kbo-2023-0072

Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews, 70, 1108–1118. https://doi.org/10.1016/j.rser.2016.12.015 DOI: https://doi.org/10.1016/j.rser.2016.12.015

Mata, J., de Miguel, I., Durán, R. J., Merayo, N., Singh, S. K., Jukan, A., & Chamania, M. (2018). Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and Networking, 28, 43–57. https://doi.org/10.1016/j.osn.2017.12.006 DOI: https://doi.org/10.1016/j.osn.2017.12.006

McMillan, L., & Varga, L. (2022). A review of the use of artificial intelligence methods in infrastructure systems. Engineering Applications of Artificial Intelligence, 116. https://doi.org/10.1016/j.engappai.2022.105472 DOI: https://doi.org/10.1016/j.engappai.2022.105472

Mendes-Moreira, J., Moreira-Matias, L., Gama, J., & Freire de Sousa, J. (2015). Validating the coverage of bus schedules: A Machine Learning approach. Information Sciences, 293, 299–313. https://doi.org/10.1016/j.ins.2014.09.005 DOI: https://doi.org/10.1016/j.ins.2014.09.005

Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99. https://doi.org/10.1016/j.segan.2016.02.005 DOI: https://doi.org/10.1016/j.segan.2016.02.005

Mohamed, N. (2023). Current trends in AI and ML for cybersecurity: A state-of-the-art survey. Cogent Engineering, 10(2). https://doi.org/10.1080/23311916.2023.2272358 DOI: https://doi.org/10.1080/23311916.2023.2272358

Mohammed Hussein Thwaini. (2022). Anomaly Detection in Network Traffic using Machine Learning for Early Threat Detection. Data & Metadata, 1, 34–34. https://doi.org/10.56294/dm202272 DOI: https://doi.org/10.56294/dm202272

Ochuba N. N. A., None Adetumi Adewumi, & Olanrewaju, D. (2024). THE ROLE OF AI IN FINANCIAL MARKET DEVELOPMENT: ENHANCING EFFICIENCY AND ACCESSIBILITY IN EMERGING ECONOMIES. Finance & Accounting Research Journal, 6(3), 421–436. https://doi.org/10.51594/farj.v6i3.969 DOI: https://doi.org/10.51594/farj.v6i3.969

Paras, R. (2023). Ethics in AI: A Deep Dive into Privacy Concerns. https://www.researchgate.net/publication/376517943_Ethics_in_AI_A_Deep_Dive_into_Privacy_Concerns

Rashid A. B., Ashfakul Karim Kausik, Hassan, A., & Mehedy Hassan Bappy. (2023). Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges. International Journal of Intelligent Systems, 2023, 1–31. https://doi.org/10.1155/2023/8676366 DOI: https://doi.org/10.1155/2023/8676366

Reddy, N. G., & G. J. Ugander Reddy. (2014). A Study of Cyber Security Challenges and Its Emerging Trends On Latest Technologies. ArXiv.org. https://arxiv.org/abs/1402.1842

Reding, D. F., & Eaton, J. (2020). Science & Technology Trends 2020-2040 Exploring the S&T Edge NATO Science & Technology Organization. Http:Www.sto.nato.int; NATO Science & Technology Organization Office of the Chief Scientist NATO Headquarters B-1110 Brussels Belgium. https://www.nato.int/nato_static_fl2014/assets/pdf/2020/4/pdf/190422-ST_Tech_Trends_Report_2020-2040.pdf

Reed, J. (2023, June 26). High-impact attacks on critical infrastructure climb 140%. Security Intelligence. https://securityintelligence.com/news/high-impact-attacks-on-critical-infrastructure-climb-140/

Ren, H., Song, Y., Wang, J., Hu, Y., & Lei, J. (2018, November 1). A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction. IEEE Xplore. https://doi.org/10.1109/ITSC.2018.8569437 DOI: https://doi.org/10.1109/ITSC.2018.8569437

Rizvi, M. (2023). Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention. International Journal of Advanced Engineering Research and Science (IJAERS), 10(5), 055–060. https://doi.org/10.22161/ijaers.105.8 DOI: https://doi.org/10.22161/ijaers.105.8

Robbins, S., & van Wynsberghe, A. (2022). Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future. Sustainability, 14(8), 4829. https://doi.org/10.3390/su14084829 DOI: https://doi.org/10.3390/su14084829

Ross, B., Hofeditz, L., Möllmann, N. R. J., Mirbabaie, M., & Stieglitz, S. (2023). Recommendations for managing AI-driven change processes: when expectations meet reality. International Journal of Management Practice, 16(4), 407. https://doi.org/10.1504/ijmp.2023.10055048 DOI: https://doi.org/10.1504/IJMP.2023.10055048

Saeed, S., Suayyid, S. A., Al-Ghamdi, M. S., Al-Muhaisen, H., & Almuhaideb, A. M. (2023). A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience. Sensors, 23(16), 7273. https://doi.org/10.3390/s23167273 DOI: https://doi.org/10.3390/s23167273

Samariya, D., & Thakkar, A. (2021). A Comprehensive Survey of Anomaly Detection Algorithms. Annals of Data Science. https://doi.org/10.1007/s40745-021-00362-9 DOI: https://doi.org/10.1007/s40745-021-00362-9

Šegvić S., Brkić, K., Zoran Kalafatić, Vladimir Stanisavljević, Marko Ševrović, Budimir, D., & Dadić, I. (2010). A computer vision assisted geoinformation inventory for traffic infrastructure. International Conference on Intelligent Transportation Systems. https://doi.org/10.1109/itsc.2010.5624979 DOI: https://doi.org/10.1109/ITSC.2010.5624979

Settanni, F. (2022, April 13). Towards intelligence driven automated incident response. Webthesis.biblio.polito.it. http://webthesis.biblio.polito.it/id/eprint/22865

Shukla, A., & Karki, H. (2016). Application of robotics in onshore oil and gas industry—A review Part I. Robotics and Autonomous Systems, 75, 490–507. https://doi.org/10.1016/j.robot.2015.09.012 DOI: https://doi.org/10.1016/j.robot.2015.09.012

Şimşek, D., Kutlu, I., & Şık, B. (2023). The role and applications of artificial intelligence (AI) in disaster management. Proceedings of 3rdInternational Civil Engineering and Architecture Congress (ICEARC’23). https://doi.org/10.31462/icearc.2023.arc992 DOI: https://doi.org/10.31462/icearc.2023.arc992

Singh S. K., Manjhi P. K., & Tiwari, R. S. (2021). Cloud Computing Security Using Blockchain Technology. In Book: Transforming Cybersecurity Solutions Using Blockchain (Pp.19-30). https://doi.org/10.1007/978-981-33-6858-3_2 DOI: https://doi.org/10.1007/978-981-33-6858-3_2

Sirohi, D., Kumar, N., & Rana, P. S. (2020). Convolutional neural networks for 5G-enabled Intelligent Transportation System: A systematic review. Computer Communications, 153, 459–498. https://doi.org/10.1016/j.comcom.2020.01.058 DOI: https://doi.org/10.1016/j.comcom.2020.01.058

Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems – A review. Renewable and Sustainable Energy Reviews, 48, 585–607. https://doi.org/10.1016/j.rser.2015.04.037 DOI: https://doi.org/10.1016/j.rser.2015.04.037

Taddeo, M., McNeish, D., Blanchard, A., & Edgar, E. (2021). Ethical Principles for Artificial Intelligence in National Defence. Philosophy & Technology, 34(4), 1707–1729. https://doi.org/10.1007/s13347-021-00482-3 DOI: https://doi.org/10.1007/s13347-021-00482-3

Tatineni S. (2023). AI-Infused Threat Detection and Incident Response in Cloud Security. International Journal of Science and Research, 12(11), 998–1004. https://doi.org/10.21275/sr231113063646 DOI: https://doi.org/10.21275/SR231113063646

Tomic, S., Fensel, A., & Pellegrini, T. (2010). SESAME demonstrator. In: Proceedings of the 6th International Conference on Semantic Systems. Graz, Austria, 1, 4. https://doi.org/10.1145/1839707.1839738 DOI: https://doi.org/10.1145/1839707.1839738

Tonhauser, M., & Jozef Ristvej. (2023). Cybersecurity Automation in Countering Cyberattacks. Transportation Research Procedia, 74, 1360–1365. https://doi.org/10.1016/j.trpro.2023.11.283 DOI: https://doi.org/10.1016/j.trpro.2023.11.283

Umoga, J., Oluwademilade, E., Ugwuanyi, D., Jacks, S., Lottu, A., Daraojimba, D., & None Alexander Obaigbena. (2024). Exploring the potential of AI-driven optimization in enhancing network performance and efficiency. Magna Scientia Advanced Research and Reviews, 10(1), 368–378. https://doi.org/10.30574/msarr.2024.10.1.0028 DOI: https://doi.org/10.30574/msarr.2024.10.1.0028

Veres, M., & Moussa, M. (2020). Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3152–3168. https://doi.org/10.1109/tits.2019.2929020 DOI: https://doi.org/10.1109/TITS.2019.2929020

Wang, C.-X., Renzo, M. D., Stanczak, S., Wang, S., & Larsson, E. G. (2020). Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Communications, 27(1), 16–23. https://doi.org/10.1109/mwc.001.1900292 DOI: https://doi.org/10.1109/MWC.001.1900292

Wazid, M., Das, A. K., Chamola, V., & Park, Y. (2022). Uniting cyber security and machine learning: Advantages, challenges and future research. ICT Express, 8(3). https://doi.org/10.1016/j.icte.2022.04.007 DOI: https://doi.org/10.1016/j.icte.2022.04.007

Xu, Z., Lian, J., Bin, L., Hua, K., Xu, K., & Chan, H. Y. (2019). Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States. Water, 11(2), 228. https://doi.org/10.3390/w11020228 DOI: https://doi.org/10.3390/w11020228

Yaacoub, J.-P. A., Noura, H. N., Salman, O., & Chehab, A. (2021). Robotics Cyber security: vulnerabilities, attacks, countermeasures, and Recommendations. International Journal of Information Security, 21(21). https://doi.org/10.1007/s10207-021-00545-8 DOI: https://doi.org/10.1007/s10207-021-00545-8

Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., & Li, Z. (2018). Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11836 DOI: https://doi.org/10.1609/aaai.v32i1.11836

Yin, J., & Zhao, W. (2016). Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach. Engineering Applications of Artificial Intelligence, 56, 250–259. https://doi.org/10.1016/j.engappai.2016.10.002 DOI: https://doi.org/10.1016/j.engappai.2016.10.002

Zhang, X., Nguyen, H., Bui, X.-N., Anh Le, H., Nguyen-Thoi, T., Moayedi, H., & Mahesh, V. (2020). Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization. Tunnelling and Underground Space Technology, 103, 103517. https://doi.org/10.1016/j.tust.2020.103517 DOI: https://doi.org/10.1016/j.tust.2020.103517

Article Details

How to Cite

AI-Driven Threat Detection and Response Systems for Secure National Infrastructure Networks: A Comprehensive Review. (2024). International Journal of Latest Technology in Engineering Management & Applied Science, 13(7), 82-92. https://doi.org/10.51583/IJLTEMAS.2024.130710

Similar Articles

You may also start an advanced similarity search for this article.