Domain Name System (DNS) provides domain name resolution service, i.e., converting domain names to IP addresses. Malicious domain detection is mainly for discovering illegal activities and ensuring the normal operation of the domain name servers. Prior work on malicious domain name detection was summarized, and a new machine learning based malicious domain detection algorithm for exploiting multiple-dimensional features was further proposed. With respect to domain name lexical features, more fine-grained features were extracted, such as the conversion frequency of the numbers and letters and the maximum length of continuous letters. As for the network attribute features, more attentions were paid to the name servers, such as the quantity, and the degree of dispersion. The experimental results show that the accuracy, recall rate, F1 value of the proposed method reaches 99.8%, which means a better performance on malicious domain name detection.