Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models.

The rapid expansion of cloud-based Internet of Things (IoT) systems has intensified security challenges due to the large-scale transmission of sensitive data from resource-constrained devices to cloud infrastructures. Conventional cryptographic techniques often impose high computational and memory overhead. Consequently, there is a critical need for security frameworks that balance strong data protection with efficient resource utilization while supporting intelligent threat detection. This study proposes an integrated security framework that combines lightweight and hybrid cryptographic algorithms with machine learning (ML) models to secure IoT data transmission in cloud-based environments. Four encryption techniques, XOR, ChaCha20, AES, and a hybrid AES-RSA scheme, are systematically evaluated in terms of memory consumption, CPU usage, and overall resource efficiency using the Overall Resource Consumption Score (ORCS). Secure data transmission is simulated using the MQTT protocol, while ML-based intrusion detection is performed using Random Forest (RF), XGBoost, CatBoost, and ensemble classifiers. Experiments are conducted on two real-world IoT datasets, MQTTEEB-D and CIC IoT 2023 for IoT network traffic. On the MQTTEEB-D dataset, the hybrid AES-RSA scheme achieved a low memory usage of 0.126 KB per traffic with an ORCS of 0.56, while the voting ensemble classifier attained the highest detection accuracy of 92.68%. On the CIC IoT 2023 dataset, comprising 605,839 test records, the hybrid AES-RSA method required 0.374 KB per traffic and achieved an ORCS of 0.5425, whereas the voting ensemble model achieved an accuracy of 81.09%. The findings demonstrate that hybrid cryptography provides an effective balance between security and efficiency for cloud-based IoT systems, while ensemble ML models significantly enhance intrusion detection performance.