Lightweight Secure Protocols for Low-Powered IoT Devices: Modern Ciphers, Authentication, and Machine Learning-Based Intrusion Detection.

This paper provides the design and simulation of a lightweight cryptographic protocol on smart house IoT devices using ChaCha20 and Ascon-AEAD128. The protocol, implemented in Python 3.14.6 and tested on a virtual ESP32 platform using Wokwi and CloudAMQP, uses stream cipher encryption with authenticated message tagging to provide confidentiality and integrity. Two conditions, normal and tampered transmission, were experimented to confirm tag validation and successful decryption. Findings affirmed sound detection of tampering and unauthorized access prevention, proving usefulness of current AEAD ciphers on limited devices. The protocol highlights cryptographic systems that prioritize computationally efficiency and robustness, which is essential in smart homes that have limited power and memory. The hybrid design provides confidentiality and authenticity using a minimal overhead by utilizing ChaCha20 to provide lightweight encryption and Ascon-AEAD128 as authentication. The resilience to the replay and modification attacks was demonstrated in experiments based on message queues and injected packet modifications simulating real-world conditions. Even though benchmarking of hardware was not carried out, the simulated values reveal stability and flexibility to use in low-power systems. This article emphasizes the necessity of authenticated encryption as default, which is consistent with the NIST standards and reflects the appropriateness of Ascon to new IoT security requirements. It also creates a reconfigurable structure that can be used in other highly constrained systems, such as healthcare monitoring and industrial IoT. The main contribution is the gap between theoretical cryptography and practical IoT security provided by the practical prototype. Further development will include tests on physical ESP32 modules and fine performance profiling, yet already, the current implementation proves a scalable, secure model of smart home IoT. Finally, this research demonstrates that the demand of reliable and low-power-based communication in resource constrained networks can be met efficiently without sacrificing device performance by means of lightweight cryptography. In addition to the cryptographic protocol design, this study integrates a machine learning-based intrusion detection layer trained on the Edge-IIoTset dataset, in which Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers are evaluated to complement the encryption-authentication framework with anomaly-aware monitoring of network traffic.