This research aims to establish a secure system for key exchange by using post-quantum cryptography (PQC) schemes in the classic channel of quantum key distribution (QKD). Modern cryptography faces significant threats from quantum computers, which can solve classical problems rapidly. PQC schemes address critical security challenges in QKD, particularly in authentication and encryption, to ensure the reliable communication across quantum and classical channels. The other objective of this study is to balance security and communication speed among various PQC algorithms in different security levels, specifically CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon, which are finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization project. The quantum channel of QKD is simulated with Qiskit, which is a comprehensive and well-supported tool in the field of quantum computing. By providing a detailed analysis of the performance of these three algorithms with Rivest–Shamir–Adleman (RSA), the results will guide companies and organizations in selecting an optimal combination for their QKD systems to achieve a reliable balance between efficiency and security. Our findings demonstrate that the implemented PQC schemes effectively address security challenges posed by quantum computers, while keeping the the performance similar to RSA.

Network packet sniffing is one of the techniques that is widely used in the network and cyber security fields. However, sniffing can also be used as a malicious technique that allows threat actors to intercept and capture data flow to collect various information within the victim network. Where the wireless network environment can be vulnerable to sniffing vulnerabilities attacks due to the broadcasting function of Wi-Fi network. Wi-Fi access point devices can often be compromised, and critical information is leaked through sniffing attacks. Moreover, since sniffing is usually one of passive attacks, it is very challenging to detect sniffing activity in the network completely. The primary aim of this research is to contribute to enhancing the security of Internet of Things (IoT) home Wi-Fi systems. This is achieved by applying ensemble machine learning technology with sniffing detection methods using a Network Monitoring System (NMS) to effectively identify and mitigate potential sniffing behaviour within the IoT home Wi-Fi environment. Ultimately, this research will prove whether it is possible to precisely detect abnormal sniffing in a smart home Wi-Fi environment using machine learning techniques.