Artificial Intelligence in Cyber Threat Detection: A Survey of Predictive Security Systems
Keywords:
Artificial intelligence, Cyber threat detection, Federated learning, Intrusion detection, Machine learning, Neural networks, Predictive security systemsAbstract
The scope and nuances of cyber threats have been escalating with the blistering pace of digital technologies development, and several questions are raised regarding the applicability of the traditional strategies of cybersecurity. Antivirus and firewall programs are examples of conventional security measures; however, they only protect against known threats and cannot detect or prevent new ones. By combining machine learning, neural networks, and natural language processing to identify outliers, anticipate attacks, and automate responses, Artificial Intelligence (AI) enables more proactive and adaptable defense. This article is a review of the state of the cyber threat detection using AI, in which it cites multi-layered system incorporating data collection, preprocessing, real-time analytics, and automated cancellation. It examines major types of threats, including phishing, ransomware, insider threats, and vulnerabilities at the protocol level, as well as issues related to implementation, including data quality, model transparency, and integration. New methods such as Federated Learning and Generative AI are also discussed, possibly to augment decentralized learning and to create attack-inspired scenarios. This paper highlights the necessity of developing intelligent systems that can adapt to cyber threats to enhance the resilience of infrastructure in the digital world.
References
A. Mishra, "AI-powered cybersecurity framework for secure data transmission in IoT network," International Journal of Advances in Engineering and Management, vol. 7, no. 3, pp. 05–13, 2025, doi: https://doi.org/10.35629/5252-07030513
S. Saeed, S. A. Suayyid, M. S. Al-Ghamdi, H. Al-Muhaisen and A. M. Almuhaideb, "A systematic literature review on cyber threat intelligence for organizational cybersecurity resilience," Sensors, vol. 23, no. 16, p. 7273, Aug. 2023, doi: https://doi.org/10.3390/s23167273
L. Ilić, A. Šijan, B. Predić, D. Viduka, and D. Karabašević, "Research trends in artificial intelligence and security—Bibliometric analysis," Electronics, vol. 13, no. 12, p. 2288, Jun. 2024, doi: https://doi.org/10.3390/electronics13122288
L. Alevizos and M. Dekker, "Towards an AI-enhanced cyber threat intelligence processing pipeline," Electronics, vol. 13, no. 11, p. 2021, May 2024, doi: https://doi.org/10.3390/electronics13112021
M. Ahsan, K. E. Nygard, R. Gomes, M. M. Chowdhury, N. Rifat, and J. F. Connolly, "Cybersecurity threats and their mitigation approaches using Machine Learning—A Review," Journal of Cybersecurity and Privacy, vol. 2, no. 3, pp. 527–555, Jul. 2022, doi: https://doi.org/10.3390/jcp2030027
V. Prajapati, "Enhancing threat intelligence and cyber defense through big data analytics: A review study," Journal of Global Research in Mathematical Archives, vol. 12, no. 4, pp. 1–6, Apr. 2025. Available: https://zenodo.org/records/15223174
P. Santos, R. Abreu, M. J. Reis, C. Serôdio, and F. Branco, "A systematic review of cyber threat intelligence: The effectiveness of technologies, strategies, and collaborations in combating modern threats," Sensors, vol. 25, no. 14, p. 4272, Jul. 2025, doi: https://doi.org/10.3390/s25144272
S. A. Pahune, P. Matapurkar, S. Mathur and H. Sinha, "Generative Adversarial Networks for Improving Detection of Network Intrusions in IoT Environments," 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Apr. 2025, pp. 1–6. doi: https://doi.org/10.1109/ICDCECE65353.2025.11035844
S. Arora, P. Khare, and S. Gupta, "AI-driven DDoS mitigation at the edge: Leveraging machine learning for real-time threat detection and response," in Proc. 2024 Int. Conf. Data Sci. Netw. Secur. (ICDSNS), Jul. 26, 2024, pp. 1–7. Doi: https://doi.org/10.1109/ICDSNS62112.2024.10690930
A. M. Alnajim, S. Habib, M. Islam, S. M. Thwin and F. Alotaibi, "A comprehensive survey of cybersecurity threats, attacks, and effective countermeasures in industrial internet of things," Technologies, vol. 11, no. 6, p. 161, Nov. 2023. Available: https://doi.org/10.3390/technologies11060161
N. K. Prajapati, "Federated learning for privacy-preserving cybersecurity: A review on secure threat detection," Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 4, pp. 520–528, Apr. 2025. Available: https://ijarsct.co.in/Paper25168.pdf
M. Aminu, A. Y. Akinsanya, O. Y. Oyedokun, and O. L. Tosin, "A review of advanced cyber threat detection techniques in critical infrastructure: Evolution, current state, and future directions," Int. J. Comput. Appl. Technol. Res., vol. 13, no. 8, pp. 74–87, Jul. 2024. Available: https://www.irejournals.com/paper-details/1706103
F. R. Alzaabi and A. Mehmood, "A review of recent advances, challenges, and opportunities in malicious insider threat detection using machine learning methods," IEEE Access, vol. 12, pp. 30907–30927, Feb. 2024, doi: https://doi.org/10.1109/ACCESS.2024.3369906
A. Mishra, “Ai-Powered Cyber Threat Intelligence System for Predicting and Preventing Cyber Attacks,” International Journal of Advances in Engineering and Management, vol. 7, no. 2, pp. 873–892, Feb. 2025, doi: https://doi.org/10.35629/5252-0702873892.
M. R. Faraji, F. Shikder, M. H. Hasan, M. M. Islam, and U. K. Akter, "Examining the role of artificial intelligence in cyber security (CS): a systematic review for preventing prospective solutions in financial transactions," International Journal, vol. 5, no. 10, pp. 4766–4782, Jul. 2024, doi: https://doi.org/10.61707/7rfyma13
P. Barik and K. Padhi, "An overview of AI techniques and their applications," Journal of Nonlinear Analysis and Optimization, vol. 11, no. 1, pp. 302–305, 2020. Available: https://www.jnao-nu.com/Vol_11_Issue_01_January-June_2020/62.pdf
H. Kali, "The Future of HR Cybersecurity: AI-Enabled Anomaly Detection in Workday Security,” International Journal of Recent Technology Science & Management, vol 8, no. 6, pp. 80-88, Jun. 2023. Available: https://ijrtsm.com/wp-content/uploads/2025/05/2023-June-2023-Honie-80-88.pdf
R. Patel, "Automated Threat Detection and Risk Mitigation for ICS (Industrial Control Systems) Employing Deep Learning in Cybersecurity Defence," International Journal of Current Engineering and Technology, vol. 13, no. 6, pp. 584-591, Dec. 2023. Available: https://inpressco.com/wp-content/uploads/2025/06/Paper11584-591.pdf
Z. Hao, "Deep learning review and discussion of its future development," MATEC Web of Conferences, vol. 277, p. 02035, 2019. Doi: https://doi.org/10.1051/matecconf/201927702035
S. Rongala, S. A. Pahune, H. Velu, and S. Mathur, "Leveraging Natural Language Processing and Machine Learning for Consumer Insights from Amazon Product Reviews," in Proc. 3rd Int. Conf. Smart Syst. Appl. Elect. Sci. (ICSSES), Mar. 2025, pp. 1–6, doi: https://doi.org/10.1109/ICSSES64899.2025.11009528
A. M. Sanadi and V. Bhat, "A review paper on natural language processing (NLP)," Int. J. Adv. Res. Sci. Commun. Technol., vol. 2022, pp. 23–29, 2022. Available: https://ijarsct.co.in/Paper2685.pdf
H. T. Gurme, A. A. Kotmale, and M. T. Shinde, "Review paper: The role of artificial intelligence technology in electrical automation control," Int. Res. J. Modern. Eng. Technol. Sci., vol. 6, no. 5, pp. 8297–8301, May 2024. Available: https://www.irjmets.com/uploadedfiles/paper//issue_5_may_2024/57726/final/fin_irjmets1716917374.pdf
D. Patel, "Leveraging Blockchain and AI Framework for Enhancing Intrusion Prevention and Detection in Cybersecurity," TIJER - International Research Journal, vol. 10, no. 6, pp. 853–858, Jun. 2023. Available: https://tijer.org/tijer/viewpaperforall.php?paper=TIJER2306333
F. Olaoye, A. Egon, and L. Doris, "Deep learning techniques for medical image analysis and diagnosis," preprint, 2024. Doi: https://doi.org/10.21203/rs.3.rs-5339461/v1
N. Alromaihi, M. Rouached, and A. Akremi, "Design and Analysis of an Effective Architecture for Machine Learning Based Intrusion Detection Systems," Network, vol. 5, no. 2, p. 13, Apr. 2025, doi: https://doi.org/10.3390/network5020013
M. T. Alam, B. V. Krishnaveni, M. A. Babu, M. H. Sallaah, and A. Pavithra, "Smart cities threat intelligence driven by artificial intelligence for enhanced cyber resilience using federated learning," in Proc. 2025 Int. Conf. Comput. Innovations Eng. Sustain. (ICCIES), Apr. 24, 2025, pp. 1–5. Doi: https://doi.org/10.1109/ICCIES63851.2025.11032508
M. R. V. P. and V. S. Vardhan, "AI-driven cyber threat detection and log analysis," in Proc. 2025 Int. Conf. Inventive Computation Technol. (ICICT), Apr. 23, 2025, pp. 676–681. Doi: https://doi.org/10.1109/ICICT64420.2025.11004938
O. Alsodi, X. Zhou, R. Gururajan, A. Shrestha, and E. Btoush, "Cyber threat detection on Twitter using deep learning techniques: IDCNN and BiLSTM integration," in Proc. 2024 12th Int. Conf. Adv. Cloud Big Data (CBD), Nov. 2024, pp. 375–379. doi: https://doi.org/10.1109/CBD65573.2024.00073
M. A. Aslam and C. A. Hassan, "Effective cyber threat detection through machine learning algorithms," in Proc. 2nd Int. Conf. Comput. Data Anal. (ICCDA), Nov. 12, 2024, pp. 1–6. Doi: https://doi.org/10.1109/ICCDA64887.2024.10867310
R. Jyothi and R. Jagadeesha, "Next-Gen Threat Detection: Leveraging AI and Cyber Twin Technologies for IoT Security," in Proc. 2024 First Int. Conf. Software, Systems and Information Technology (SSITCON), Oct. 2024, pp. 1–6. doi: https://doi.org/10.1109/SSITCON62437.2024.10796384
R. Vadisetty and A. Polamarasetti, "Generative AI for Cyber Threat Simulation and Defense," in Proc. 2024 12th Int. Conf. Control, Mechatronics and Autom. (ICCMA), Nov. 2024, pp. 272–279. doi: https://doi.org/10.1109/ICCMA63715.2024.10843938
