 This paper proposes a novel approach for detecting malicious domains using domain name service, DNS, data. It combines a genetic algorithm for feature selection with a two-step quantum ant colony optimization, QABC, algorithm for classification. The modified two-step QABC classifier uses k-means instead of random initialization to place food sources. Additionally, it employs the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach, k-mean and QABC, to deal with the large size of uniform resource locators, URL, data is one of the main contributions of this paper. The results show that the suggested model can achieve over 96.6% accuracy for more than 10 million query answer pairs. This article was authored by Saad M. Darwish, D. L. Diney Farhan, and Adele A. El-Zegabi. We are article.tv, links in the description below.