Open Access
An Analysis of Explainable Artificial Intelligence for Intelligent Cybersecurity Applications
4
Assistant Professor, Department of Computer Science and Applications, Mandsaur(M.P.), India
Abstract
Proactive cyber defenses, AI-powered threat detection systems, and automated reactions are changing the face of cybersecurity in the modern era. In security-critical applications, however, Explainable Artificial Intelligence (XAI) is in high demand due to the need to improve trust, transparency, and decision-making in light of the fact that traditional AI models are not transparent. Intelligent threat detection and response mechanisms, key cybersecurity applications, the most recent advances in the area of XAI-based security solutions, and the fundamentals of explainable AI are all covered in detail in this survey. Highlighting the most prominent explainability methods including SHAP, LIME, Grad-CAM, and counterfactual explanations, this article delves into their applications in several security domains, including cloud security, IoT security, fraud detection, intrusion detection, phishing, spam, cloud security, and identity and access management. The comparative analysis of recent studies is used to emphasize current accomplishments, problems, and new research areas. The results show that XAI can improve the transparency, trustworthiness and effectiveness of AI-based cybersecurity systems, in addition to highlighting a range of privacy, adversarial robustness, scalability and evaluation challenges that warrant further research to ensure reliable deployment in the real world.
Keywords
Intelligent Cybersecurity,
Threat Detection,
Incident Response
References
M. Humayun, M. Niazi, N. Z. Jhanjhi, M. Alshayeb, and S. Mahmood, “Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study,” Arab. J. Sci. Eng., vol. 45, no. 4, pp. 3171–3189, 2020, doi: 10.1007/s13369-019-04319-2.
P. Kumar, “A Zero Trust-Based Approach to Modern Cybersecurity Challenges in Software Development,” Int. J. Emerg. Res. Eng. Technol., vol. 6, no. 9, pp. 113–122, September, 2025.
S. Kumara and M. Shah, “Securing national connectivity infrastructure through identity resilience: implications for zero trust,” Futur. Technol. Open Access J., vol. 5, no. 3, pp. 276–286, August, 2026, doi: 10.55670/fpll.futech.5.3.24.
J. Kaur and K. R. Ramkumar, “The recent trends in cyber security: A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5766–5781, Sep. 2022, doi: 10.1016/j.jksuci.2021.01.018.
B. Guembe, A. Azeta, S. Misra, V. C. Osamor, L. Fernandez-Sanz, and V. Pospelova, “The Emerging Threat of AI-driven Cyber Attacks: A Review,” Appl. Artif. Intell., vol. 36, no. 1, p. 2037254, Dec. 2022, doi: 10.1080/08839514.2022.2037254.
A. Kuppa and N.-A. Le-Khac, “Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security,” in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2020, pp. 1–8. doi: 10.1109/IJCNN48605.2020.9206780.
V. Rohilla, K. Rohilla, P. Kumar, and N. Jain, “Intrusion Detection in Network Traffic Using Feature Selection and Optuna-Based Machine Learning Optimization,” in 2026 3rd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India: IEEE, Apr. 2026, pp. 1–6. doi: 10.1109/RMKMATE69073.2026.11518834.
S. C. Pallaprolu, “Zero-day Attack Identification in Streaming Data: Nearest Neighbor Heuristics and Dynamic Semantic Network Generation in the Spark Eco-system,” University of Maryland, Baltimore County, 2017.
I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” J. Big Data, vol. 7, pp. 1–29, 2020.
S. Wali and I. Khan, “Explainable AI and Random Forest-Based Reliable Intrusion Detection System,” TechRxiv, vol. 2021, no. 1218, 2021, doi: 10.36227/techrxiv.17169080.v1.
S. Singh, “Advancing Network Security in 5G: Leveraging the 5G-NIDD Dataset for Intrusion Detection and Mitigation,” in 2025 IEEE 12th International Conference on Cyber Security and Cloud Computing (CSCloud), New York City, NY, USA: IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/CSCloud66326.2025.00055.
S. P. Sivashanmugam, S. Kumara, A. Mohile, and D. R. Suram, “Improving Detection Accuracy of Phishing Attacks with Feature Selection and Machine Learning Techniques in Cybersecurity,” in 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), Bhubaneswar, India: IEEE, 2026, pp. 1–6, April. doi: 10.1109/ETAACT69135.2026.11542138.
V. Jain and A. Mitra, “Real-Time Threat Detection in Cybersecurity,” in Machine Intelligence Applications in Cyber- Risk Management, 2024, pp. 315–344. doi: 10.4018/979-8-3693-7540-2.ch014.
S. C. Pallaprolu, R. Sankineni, M. Thevar, G. Karabatis, and J. Wang, “Zero-Day Attack Identification in Streaming Data Using Semantics and Spark,” in 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA: IEEE, Jun. 2017, pp. 121–128. doi: 10.1109/BigDataCongress.2017.25.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv., vol. 41, no. 3, Jul. 2009, doi: 10.1145/1541880.1541882.
J. Sehgal, “Enhancing Site Reliability Engineering: Scalable Strategies for Automated Incident Response and System Resilience,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 2, no. 4, pp. 2484–2488, 2024, doi: 10.51219/jaimld/jaya-sehgal/533.
V. Sai Swaroop Reddy, “Cybersecurity Threat Prediction Using Machine Learning,” Int. J. Sci. Res., vol. 12, no. 4, pp. 1972–1976, 2023, doi: 10.21275/sr23048115831.
S. Cramer, C. Glantz, G. Landine, S. Eggers, and J. Knight, “Technical Guide for Implementing Cybersecurity Continuous Monitoring in the Nuclear Industry,” 2021.
V. Koppireddy, D. G. Parasad, D. Patel, S. R. Somula, V. Kamaraj, and A. Meher, “AI Security Governance for Trustworthy Intelligent Systems: A Critical Analysis of Cyber Risks, Privacy Threats, and Responsible Deployment Frameworks,” Int. J. Res. Trends Innov., vol. 11, no. 07, pp. a407–a435, July, 2026, doi: 10.56975/ijrti.v11i7.214029.
V. K. Sharma and K. S. Abhilash, “Latency-Aware Edge-Cloud Architecture for 5G IoT Integration,” in 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India: IEEE, Sep. 2025, pp. 1398–1405. doi: 10.1109/ICESC65114.2025.11212232.
M. Özkan-Okay, R. Samet, Ö. Aslan, and D. Gupta, “A Comprehensive Systematic Literature Review on Intrusion Detection Systems,” IEEE Access, vol. 9, pp. 157727–157760, 2021, doi: 10.1109/ACCESS.2021.3129336.
V. B. Savant and R. D. Kasar, “A Review on Network Security and Cryptography,” Res. J. Eng. Technol., vol. 3, no. 1, pp. 110–114, 2021, doi: 10.52711/2321-581x.2021.00019.
S. Singh, Y.-S. Jeong, and J. H. Park, “A survey on cloud computing security: Issues, threats, and solutions,” J. Netw. Comput. Appl., vol. 75, pp. 200–222, Nov. 2016, doi: 10.1016/j.jnca.2016.09.002.
M. Moucharraf, M. Ridouani, F. Salahdine, and N. Kaabouch, “IoT Security: A Comprehensive Review of Architectures, Threat Models, Detection Methods, and Countermeasures,” Futur. Internet, vol. 18, no. 5, pp. 1–31, 2026, doi: 10.3390/fi18050266.
V. K. R. K. Koppireddy, “Passwordless Authentication: Enhancing Security And User Experience In Modern Digital Ecosystems,” Int. J. Inf. Technol. Manag. Inf. Syst., vol. 16, no. 1, pp. 82–96, Feb. 2025, doi: 10.34218/IJITMIS_16_01_008.
J. Glöckler, J. Sedlmeir, M. Frank, and G. Fridgen, “A Systematic Review of Identity and Access Management Requirements in Enterprises and Potential Contributions of Self-Sovereign Identity,” Bus. Inf. Syst. Eng., vol. 66, no. 4, pp. 421–440, 2024, doi: 10.1007/s12599-023-00830-x.
D. Vale, A. El-Sharif, and M. Ali, “Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law,” AI Ethics, vol. 2, no. 4, pp. 815–826, 2022, doi: 10.1007/s43681-022-00142-y.
L. Maglaras, V. C. Gerogiannis, M. A. Ferrag, A. Lekidis, A. Lakas, and N. Moradpoor, “The Dual Role of Artificial Intelligence and LLM in Cybersecurity,” in 2025 International Conference on Artificial Intelligence Security and Governance (ICAISG), IEEE, Dec. 2025, pp. 113–118. doi: 10.1109/ICAISG68699.2025.11452144.
T. K. Lengyel, S. Maresca, B. D. Payne, G. D. Webster, S. Vogl, and A. Kiayias, “Scalability, fidelity and stealth in the DRAKVUF dynamic malware analysis system,” in Proceedings of the 30th Annual Computer Security Applications Conference, New York, NY, USA: ACM, Dec. 2014, pp. 386–395. doi: 10.1145/2664243.2664252.
E. G. Dada, J. S. Bassi, H. Chiroma, S. M. Abdulhamid, A. O. Adetunmbi, and O. E. Ajibuwa, “Machine learning for email spam filtering: review, approaches and open research problems,” Heliyon, vol. 5, no. 6, Jun. 2019, doi: 10.1016/j.heliyon.2019.e01802.
S. S. C. Silva, R. M. P. Silva, R. C. G. Pinto, and R. M. Salles, “Botnets: A survey,” Comput. Networks, vol. 57, no. 2, pp. 378–403, Feb. 2013, doi: 10.1016/j.comnet.2012.07.021.
D. Buil-Gil, F. Miró-Llinares, A. Moneva, S. Kemp, and N. Díaz-Castaño, “Cybercrime and shifts in opportunities during COVID-19: a preliminary analysis in the UK,” Eur. Soc., vol. 23, no. S1, pp. S47–S59, 2021, doi: 10.1080/14616696.2020.1804973.
A. Das, S. Baki, A. El Aassal, R. Verma, and A. Dunbar, “SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective,” IEEE Commun. Surv. Tutorials, vol. 22, no. 1, pp. 671–708, 2020, doi: 10.1109/COMST.2019.2957750.
G. Andresini, A. Appice, F. P. Caforio, D. Malerba, and G. Vessio, “ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection,” Expert Syst. Appl., vol. 201, p. 117144, Sep. 2022, doi: 10.1016/j.eswa.2022.117144.
P. Wagner et al., “PTB-XL, a large publicly available electrocardiography dataset,” Sci. Data, vol. 7, no. 1, p. 154, 2020, doi: 10.1038/s41597-020-0495-6.
R. H. Jhaveri, S. J. Patel, and D. C. Jinwala, “DoS Attacks in Mobile Ad Hoc Networks: A Survey,” in 2012 Second International Conference on Advanced Computing & Communication Technologies, IEEE, Jan. 2012, pp. 535–541. doi: 10.1109/ACCT.2012.48.
W. Garcia, J. I. Choi, S. K. Adari, S. Jha, and K. R. B. Butler, “Explainable Black-Box Attacks Against Model-based Authentication,” pp. 1–23, 2018.
A. Yan, T. Huang, L. Ke, X. Liu, Q. Chen, and C. Dong, “Explanation leaks: Explanation-guided model extraction attacks,” Inf. Sci. (Ny)., vol. 632, pp. 269–284, Jun. 2023, doi: 10.1016/j.ins.2023.03.020.
J. Cohen, E. Rosenfeld, and J. Z. Kolter, “Certified adversarial robustness via randomized smoothing,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 2323–2356, 2019.
D. Bhusal et al., “SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability,” in Proceedings of the 18th International Conference on Availability, Reliability and Security, in ARES ’23. New York, NY, USA, NY, USA: ACM, Aug. 2023, pp. 1–12. doi: 10.1145/3600160.3600193.
A. Vashisth, G. Kaur, A. Sharma, B. Kaur, and M. Rakhra, “Cybersecurity Threat Landscape in Intelligent Transportation Systems: A Systematic Review,” in 2026 International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC), IEEE, Apr. 2026, pp. 770–775. doi: 10.1109/ICISESSC68634.2026.11542737.
R. H. M and S. J. Upadhyaya, “Explainable AI (XAI) for Cyber Threat Intelligence and Decision Making,” in 2025 International Conference on Transformative Computing Technologies (ICTCT), IEEE, Sep. 2025, pp. 79–84. doi: 10.1109/ICTCT69201.2025.00030.
M. T. Masud, M. Keshk, N. Moustafa, I. Linkov, and D. K. Emge, “Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things,” IEEE Open J. Commun. Soc., vol. 6, pp. 2877–2906, 2025, doi: 10.1109/OJCOMS.2024.3413790.
S. Safavi, M. S. H. Abdulnabi, M. E. Rana, and S. Alizadeh, “From Black Box to Trustworthy AI: A Secure Framework for Explainable Cybersecurity Decision-Making,” in 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), IEEE, May 2025, pp. 1–4. doi: 10.1109/ASSIC64892.2025.11158699.
R. Barton, P. W. C. Prasad, I. Seher, and A. Elchouemi, “Artificial Intelligence (AI) in Cybersecurity and Inhibitors to AI Adoption,” in 2024 International Conference on Intelligent Education and Intelligent Research (IEIR), IEEE, Nov. 2024, pp. 1–10. doi: 10.1109/IEIR62538.2024.10959777.
K. Kumar, Kuldeep, and B. Bhushan, “Augmenting Cybersecurity and Fraud Detection Using Artificial Intelligence Advancements,” in 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), IEEE, Nov. 2023, pp. 1207–1212. doi: 10.1109/ICCCIS60361.2023.10425069.
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