Aug 2026· International Journal of Advanced Artificial Intelligence Research· 0 citations
TL;DR
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.
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.
The sophistication of cyber threats is growing, and there is a growing need for timely detection and response to security threats, which is now possible with the help of artificial intelligence (AI) based Intrusion Detection System (IDS). While the accuracy of detection has increased with the implementation of more sophisticated machine learning and deep learning models, those models tend to be opaque and complicated, making it difficult for cybersecurity professionals to understand, verify and believe automated predictions. The study explores how XAI can enhance the understanding and accuracy of artificial intelligence (AI) intrusion detection systems (IDSs). The study is carried out using the qualitative method which examines the application of the existing techniques of XAI such as feature attribution, local or global explanation models, visualization techniques and rule based interpretations for explaining the techniques and gaining enhanced confidence of the analyst and informed security decisions. The secondary data used in this research was obtained from scholarly articles, cybersecurity frameworks, industry reports, and case studies to identify real-world applications, problems in implementation, as well as the current trends of the explainable AI for cyber defense. The results showed that embedding explainability in an IDS enhances the human-AI partnership, allowing security analysts to confirm the results of their IDS, mitigate false-positive ambiguity, optimize incident response, and meet regulatory and ethical obligations. Other challenges remain such as: maintaining the explainability attribute while obtaining the predictive performance, handling large traffic density, avoiding adversarial manipulation on the explanation mechanisms, and scalability. The study finds explainable AI to be an important milestone on the path towards trustworthy and responsible cybersecurity systems. By enabling organizations to make their security operations more resilient, boost the trust in automated cyber defense, and enhance transparency without compromising detection, XAI can help organizations achieve these goals. The study provides valuable insights for practitioners in the cybersecurity industry, AI developers, decision makers and organizations developing intrusion detection systems that are transparent, reliable and ethically responsible in the dynamic digital landscape.
Christian Manna Guimma· Scriptora International Jour...· 0 citations
Cyberattacks are becoming more frequent and sophisticated in today’s digital world, rendering conventional security measures inadequate. In order to increase the accuracy of cyber threat detection, this study investigates the application of deeplearning methods to increase the accuracy of cyber threat detection. A cybersecurity dataset was used to test four classification models: Artificial Neural Networks (ANN), Random Forest, XGBoost, and Logistic Regression. The models were evaluated using the key 95.32. The performance of Artificial Neural Networks, Random Forest, XGBoost, and Logistic Regression was examined. These findings imply that learning-based and ensemble models are better at spotting intricate and changing attack patterns. In general, the study highlights the significance of clever, data-driven methods for creating cybersecurity defence systems that are quicker, more dependable, and more resilient.
D. Sharma, Inderdeep Kaur, Krishika Gupta et al.· International Conference on...· 0 citations
The review finds that artificial intelligence enables proactive threat detection, anomaly identification, and the automation of analysis and response at a scale beyond human capacity, yet its effectiveness is constrained by adversarial machine learning, data quality and drift, false positives, opacity, and the dual use of generative models by attackers.
N. Hussain· WORLD JOURNAL OF INNOVATION...· 0 citations
A structured taxonomy is proposed to organize various dimensions of AI-driven cybersecurity; review them critically; and finally, discuss key challenges, open problems, and emerging trends.
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The role of artificial intelligence, innovations, challenges, and prospects for its development in the field of cybersecurity, the advantages and challenges of its implementation, as well as potential opportunities and threats to information security are examined.
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