DOI: 10.36347/sjet.2024.v12i05.001

Pages: 151-155

In today's era, with the continuous development of the internet, surfing the internet has become an inseparable part of our lives. For those who frequently roam the internet, URLs are information that we are constantly exposed to. And YRL is also divided into malicious and normal. Clicking on malicious URLs can cause certain losses to our internet tools, and in severe cases, it can even lead to money theft. Malicious URL detection is an important task in the field of network security, aimed at identifying and preventing potential network attacks and malicious activities. Traditional malicious URL detection methods, such as Blacklist based detection and Machine learning based on feature extraction, have achieved certain results, but face challenges in real-time, accuracy, and data requirements. Therefore, this article proposes the use of Quantum Long Short-Term Memory Neural Network (QLSTM) to solve this problem. By observing the accuracy, recall, and F1 values obtained from training a large amount of data on the QLSTM network, it is shown that this method is feasible.