Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

In a society preoccupied with gradual erosion of electronic privacy, loss of privacy in the current Domain Name System is an important issue worth considering. In this paper, we first review the DNS and some security & privacy threats to make average users begin to concern about the significance of privacy preservation in DNS protocols. Then, by an careful survey of four noise query generation based existing privacy protection approaches, we analyze some benefits and limitations of these proposals in terms of both related performance evaluation results and theoretic proofs. Finally, we point out some problems that still exist for research community's continuing efforts in the future.