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RESEARCH ARTICLE

Balancing Detection Accuracy and Response Time in Explainable AI-Based Cloud Intrusion Detection

Kavin, Simion Deepak, Manoj Prabakaran
IJCCCS Journal Cover
International Journal of Cloud Computing and Cyber Security
© 2026 by IJCCCS
VOLUME Volume 1
ISSUE Issue 2
PUBLICATION November 2026
PAGES 18-30
DOI 10.xxxx/ijcccs.v1i2p02
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CITATION

How to Cite This Article

Kavin, Simion Deepak, Manoj Prabakaran, 2026. "Balancing Detection Accuracy and Response Time in Explainable AI-Based Cloud Intrusion Detection" , IJCCCS , Volume 1, Issue 2, pp. 18-30.
ARTICLE SUMMARY

Abstract

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Cloud computing has emerged as a fundamental architecture to store data, deploy applications and provide scalable digital services. Due to the distributed nature of cloud computing, resource sharing, and dynamic workload conditions, there are substantial security issues associated with cloud computing; especially in terms of detecting and responding to complex network-based intrusions. Conventional intrusion detection systems are generally limited by maintaining high levels of detection accuracy with short response times and providing understandable explanations of their decisions. This research will address the balance of detection accuracy and response time through the use of an explainable artificial intelligence (XAI)-based cloud intrusion detection framework. We propose a conceptual framework that combines machine learning-based anomaly detection, real-time event processing, and explainability methods to detect suspicious activity and enable timely responses to potential security incidents. We plan to utilize lightweight feature selection, optimized model inference, and explanation generation as complementary elements for increasing operational efficiency. In addition to our conceptual framework, we intend to utilize evaluation metrics such as accuracy, precision, recall, F1-score, false-positive rate, detection latency, and explanation overhead to assess the performance of our framework. Our research will investigate the relationship between predictive performance, computational resources needed, and interpretability in order to create a systematic method for assessing the performance of AI-powered cloud security systems. Our goal is to develop a transparent, timely, and dependable intrusion detection system without sacrificing security functions. To validate the viability of our conceptual framework in real world cloud settings, experimental evaluations using representative cloud intrusion data sets and realistic workloads will be conducted.

INDEX TERMS

Keywords

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Explainable AI, Cloud Security, Intrusion Detection, Machine Learning, Detection Accuracy, Response Time, Anomaly Detection, Feature Selection, Real-Time Processing, Cybersecurity.

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