International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 958KB)
Integrating Machine Learning–Driven Threat Detection with Advanced Cryptographic Solutions for Secure Cloud and Web Applications
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Author(s)
Tanu Sharma
1,*
Farheen Siddiqui
1
Khyati Chopra
2
Jawed Ahmed
1
Index Terms
Federated Learning, Deep Reinforcement Learning, Privacy-Preserving Security, Intrusion Detection Sys-tems, Cloud Computing Security, Adaptive Threat Detection, Web Application Security, Distributed Cybersecurity
Abstract
With the accelerated proliferation of cloud services and web-based applications, exposure to sophisticated cy-ber threats like zero-day vulnerabilities, advanced persistent threats, and application-layer attacks, has sharply increased. Conventional intrusion detection systems, along with cryptographic security mechanisms, often do not fulfill the require-ments of adaptive detection, privacy preservation, and variety of scalability within distributed systems. To alleviate these problems, this paper suggests a cross-layer adaptive security model, which includes, in the secure cloud and web applica-tions, machine learning-based anomaly detection complemented with advanced cryptographic security. The model com-bines, in this context, lightweight local anomaly detection, federated learning, selective privacy, and a deep reinforcement learning-based threat detection and security orchestration. Through federated learning, active participation in the learning process is assured, while the selective privacy mechanism preserves the model parameters. The deep reinforcement learn-ing agent adjusts the interaction, aggregation, and privacy settings according to demands of the adaptive system and the environment. The assessment of the model is realized through the CSE-CIC-IDS2018, CIC-IDS2017, and the CSIC 2010 HTTP benchmark datasets to verify the model in detection, generalization, and operational effectiveness. The results of the performed tests reflect an accuracy of 97.9%, a F1 score of 97.5%, a false positive rate of 1.8%, and a detection latency of 36 ms, surpassing performance of conventional federated and centralized state-of-the-art models. Cross-dataset testing validates the model effectiveness in the presence of highly variable traffic. The results suggest that adaptive orchestration, federated learning, and selective privacy preservation, when combined, substantially boost intrusion detection, decrease communication overhead, and ensure privacy preservation. Therefore, this framework is a scalable and robust approach to intrusion detection within contemporary cloud and web settings.
Cite This Paper
Tanu Sharma, Farheen Siddiqui, Khyati Chopra, Jawed Ahmed, "Integrating Machine Learning–Driven Threat Detection with Advanced Cryptographic Solutions for Secure Cloud and Web Applications", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.4, pp. 328-345, 2026. DOI:10.5815/ijwmt.2026.04.19
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