Classification for DGA-Based Malicious Domain Names with Deep Learning Architectures

Feng Zeng
2017 International Journal of Intelligent Information Systems  
The preemptive defenses against various malware created by domain generation algorithms (DGAs) have traditionally been solved using manually-crafted domain features obtained by heuristic process. However, it is difficult to achieve real-world deployment with most research on detecting DGA-based malicious domain names due to poor performance and time consuming. Based on the recent overwhelming success of deep learning networks in a broad range of applications, this article transfers five
more » ... learned ImageNet models from Alex Net, VGG, Squeeze Net, Inception, Res Net to classify DGA domains and non-DGA domains, which: (i) is suited to automate feature extraction from raw inputs; (ii) has fast inference speed and good accuracy performance; and (iii) is capable of handling large-scale data. The results show that the proposed approach is effective and efficient.
doi:10.11648/j.ijiis.20170606.11 fatcat:qc5r6d6ztbh6lcdkxm4uxgeuwy