ARTIFICIAL INTELLIGENCE IN CYBERSECURITY: THREATS, DEFENSES, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.17770/etr2025vol2.8592Keywords:
Cybersecurity, Deep Learning, Anomaly Detection, Intrusion Prevention, Machine Learning, Neural Networks, Cyber Threats, Artificial Intelligence, Network Security, Adversarial AttacksAbstract
This study explores the usage of Artificial Intelligence and machine learning technology in modern cybersecurity. When cyberattacks becoming more frequent, organizations are turning to AI-powered solutions to improve their defense systems. Adoption of AI technologies like machine learning and deep learning is shown to improve the accuracy of threat detection, but data reliability, model interpretability, and bias in decision-making systems remain significant issues. Research has proven AI to be most successful in preventing large-scale attacks, i.e., DDoS, through the recognition of patterns in network traffic based on models like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The application of AI technologies, such as machine learning and deep learning, has been shown to improve the speed and precision of threat detection so that it becomes easier to identify and counteract previously unknown vulnerabilities. Despite these advantages, very low rates of organizations have fully implemented AI-driven security solutions, and confidence in AI decision-making still affects deployment. This article also highlights increasing incidents of AI-driven cyber attacks, such as AI-created malware, automated phishing, and deepfake identity theft, that are outrunning traditional security controls. This analysis also emphasizes the growing importance of application of AI in cybersecurity with underscoring its potential to strengthen defens systems against zero-day attacks and similar evolving threats. However, it also reviews some of the challenges that need to be addressed for more effective integration of AI models in cybersecurity, such as the reliability of the data that is being used for its' training. With the development in AI technologies, machine learning based models are expected to play an increasingly crucial role in protection of both businesses and individuals from sophisticated cyberattacks. And despite all the advantages, the full implementation of AI-driven security solutions remains low. What is more, concerns about the trustworthiness of AI decisions and the lack of transparency in AI models still remains.
References
A. Abdallah, A. Alkaabi, G. Alameri, S. H. Rafique, N. S. Musa, and T. Murugan, "Cloud network anomaly detection using machine and deep learning techniques—recent research advancements," IEEE Access, vol. 12, pp. 56749–56773, 2024. [Online]. Available: https://doi.org/10.1109/ACCESS.2024.3390844.
M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, M. Hasan, B. C. Van Essen, A. A. S. Awwal, and V. K. Asari, "A state-of-the-art survey on deep learning theory and architectures," Electronics, vol. 8, no. 3, p. 292, 2019. [Online]. Available: https://doi.org/10.3390/electronics8030292.
T. Al-Shehari, et al., "Enhancing insider threat detection in imbalanced cybersecurity settings using the density-based local outlier factor algorithm," IEEE Access. [Online]. Available: https://ieeexplore.ieee.org/document/10459083.
V. Barba-Sánchez, et al., "Effects of digital transformation on firm performance: The role of IT capabilities and digital orientation," Heliyon, vol. 10, no. 6, 2024. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/38509885/.
K. Bathiri and M. Vijayakumar, "Enhancing intrusion detection system (IDS) through deep packet inspection (DPI) with machine learning approaches," in 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), IEEE, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10533473.
Check Point Research, "A closer look at Q3 2024: 75% surge in cyber attacks worldwide," Check Point Blog, Feb. 24, 2025. [Online]. Available: https://blog.checkpoint.com/research/a-closer-look-at-q3-2024-75-surge-in-cyber-attacks-worldwide/.
F. Cerasuolo, et al., "Adaptive intrusion detection systems: Class incremental learning for IoT emerging threats," in 2023 IEEE International Conference on Big Data (BigData), IEEE, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10386129.
S. Chakraborty, et al., "Interpretability of deep learning models: A survey of results," in IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, 2017. [Online]. Available: https://doi.org/10.1109/UIC-ATC.2017.8397411.
J. N. Chukwunweike, M. Yussuf, O. Okusi, T. O. Bakare, and A. J. Abisola, "The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions," World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 2550, 2024. [Online]. Available: https://doi.org/10.30574/wjarr.2024.23.2.2550.
X. D. Do, H. D. Nguyen, and V. N. Tisenko, "Malicious URL detection based on machine learning," International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, 2020. [Online]. Available: https://doi.org/10.14569/IJACSA.2020.0110119.
H. N. Fakhouri, et al., "A comprehensive study on the role of machine learning in 5G security: Challenges, technologies, and solutions," Electronics, vol. 12, no. 22, p. 4604, 2023. [Online]. Available: https://doi.org/10.3390/electronics12224604.
J. Fox, "Top 40 AI cybersecurity statistics," Cobalt Blog, Oct. 10, 2024. [Online]. Available: https://www.cobalt.io/blog/top-40-ai-cybersecurity-statistics.
H. Gonaygunta, et al., "Enhancing cybersecurity: The development of a flexible deep learning model for enhanced anomaly detection," in 2024 Systems and Information Engineering Design Symposium (SIEDS), IEEE, 2024. [Online]. Available: https://doi.org/10.1109/SIEDS61124.2024.10534661.
M. Z. Gunduz and R. Das, "Cyber-security on smart grid: Threats and potential solutions," Computer Networks, vol. 169, p. 107094, 2020. [Online]. Available: https://doi.org/10.1016/j.comnet.2019.107094.
S. R. Hong, J. Hullman, and E. Bertini, "Human factors in model interpretability: Industry practices, challenges, and needs," Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW1, pp. 1–26, 2020. [Online]. Available: https://doi.org/10.1145/3392878.
R.-H. Hwang, M.-C. Peng, C.-W. Huang, P.-C. Lin, and N. Nguyen, "Unsupervised deep learning model for early network traffic anomaly detection," IEEE Access, vol. 8, pp. 30387–30399, 2020. [Online]. Available: https://doi.org/10.1109/ACCESS.2020.2973023.
J. M. Kaplan, et al., Beyond cybersecurity: Protecting your digital business. Hoboken, NJ: Wiley, 2015.
A. R. Khan, M. Kashif, R. H. Jhaveri, R. Raut, T. Saba, and S. A. Bahaj, "Deep learning for intrusion detection and security of Internet of Things (IoT): Current analysis, challenges, and possible solutions," Security and Communication Networks, vol. 2022, p. 4016073, 2022. [Online]. Available: https://doi.org/10.1155/2022/4016073.
G. Kocher and G. Kumar, "Machine learning and deep learning methods for intrusion detection systems: Recent developments and challenges," Soft Computing, vol. 25, no. 15, pp. 9731–9763, 2021. [Online]. Available: https://www.researchgate.net/publication/352725425_Machine_learning_and_deep_learning_methods_for_intrusion_detection_systems_recent_developments_and_challenges.
A. Y.-P. Lee, et al., "PS-IPS: Deploying intrusion prevention system with machine learning on programmable switch," Future Generation Computer Systems, vol. 152, pp. 333–342, 2024. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4440724.
W. Liang, et al., "Advances, challenges, and opportunities in creating data for trustworthy AI," Nature Machine Intelligence, vol. 4, no. 8, pp. 669–677, 2022. [Online]. Available: https://www.researchgate.net/publication/362752511_Advances_challenges_and_opportunities_in_creating_data_for_trustworthy_AI.
F. Liu, et al., "Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional LSTM-CNN," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2626–2640, 2020. [Online]. Available: https://www.researchgate.net/publication/343492730_Anomaly_Detection_in_Quasi-Periodic_Time_Series_based_on_Automatic_Data_Segmentation_and_Attentional_LSTM-CNN.
A. Manoharan and M. Sarker, "Revolutionizing cybersecurity: Unleashing the power of artificial intelligence and machine learning for next-generation threat detection," International Research Journal of Modern Engineering and Technology & Science, 2023. [Online]. Available: https://www.researchgate.net/publication/379308659_REVOLUTIONIZING_CYBERSECURITY_UNLEASHING_THE_POWER_OF_ARTIFICIAL_INTELLIGENCE_AND_MACHINE_LEARNING_FOR_NEXT-_GENERATION_THREAT_DETECTION.
N. S. Musa, et al., "Machine learning and deep learning techniques for distributed denial of service anomaly detection in software-defined networks—current research solutions," IEEE Access, vol. 12, pp. 17982–18011, 2024. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10418146.
D. Nair and N. Mhavan, "Augmenting cybersecurity: A survey of intrusion detection systems in combating zero-day vulnerabilities," in Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, Emerald Publishing Limited, 2023. [Online]. Available: https://doi.org/10.1108/S1569-37592023000110A007.
M. Ozkan-Okay, et al., "A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cybersecurity solutions," IEEE Access, vol. 12, pp. 12229–12256, 2024. [Online]. Available: https://doi.org/10.1109/ACCESS.2024.3355547.
G. Pang, C. Shen, L. Cao, and A. Van Den Hengel, "Deep learning for anomaly detection: A review," ACM Computing Surveys, vol. 54, no. 2, Article 38, 2021. [Online]. Available: https://doi.org/10.1145/3439950.
A. Yaseen, "The role of machine learning in network anomaly detection for cybersecurity," Sage Science Review of Applied Machine Learning, vol. 6, no. 8, pp. 16–34, 2023. [Online]. Available: https://journals.sagescience.org/index.php/ssraml/article/view/126.
Z. Zamanzadeh Darban, et al., "Deep learning for time series anomaly detection: A survey," ACM Computing Surveys, vol. 57, no. 1, pp. 1–42, 2024. [Online]. Available: https://dl.acm.org/doi/10.1145/3691338.
X. X. Zhu, et al., "Deep learning in remote sensing: A comprehensive review and list of resources," IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8–36, 2017. [Online]. Available: http://dx.doi.org/10.1109/MGRS.2017.2762307.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Laylo Bakhronkulova, Muhammad Ali, Zulfiya Khabirova, Akimjonov Azimjon, Alimova Zebo, Abdumajidova Muslima
This work is licensed under a Creative Commons Attribution 4.0 International License.
