Quantum-Resistant Wireless Intrusion Detection System using Machine Learning Techniques

This paper proposes a novel and implementable hybrid algorithm for wireless intrusion detection systems (WIDS) that combines machine learning techniques with a new post-quantum cryptography scheme called Lattice-based Hash Trapdoor (LHT). The algorithm uses a deep neural network to detect anomalous network traffic by analyzing features such as packet size, frequency, and time intervals. LHT provides a secure communication channel that is resistant to quantum attacks. The proposed algorithm achieves higher detection accuracy, lower false positive rates, and lower false negative rates than existing state-of-the-art intrusion detection systems. Our hybrid algorithm provides a balanced trade-off between accuracy and efficiency, making it a practical solution for real-world wireless intrusion detection systems in a post-quantum era. Using a new and implementable post-quantum cryptography scheme and a deep neural network provides unique features and novelties to our proposed algorithm.