Detecting Domain Generation Algorithms With Convolutional Neural Language Models

Detecting the malicious domains generated by the dgas in real time is the most challenging task and significant research has been carried out by applying different machine learning algorithms.

Detecting domain generation algorithms with convolutional neural language models. Therefore they seemed suitable for dga detection. Convolutional neural network cnn are well known for performing real time detection in fields like image and video recognition. As an example of a reverse engineering based model plohmann et al.

Recent work in dga detection leveraged deep learning architectures like convolutional neural networks cnns and character level long short term memory networks lstms to classify domains. Convolutional neural network can extract high level information and discover intricate structure which dramatically improve the performance of many computer vision tasks such as object detection speech recognition and image classication. In this way each bot generates a large number of domain names detecting domain generation algorithms with convolutional neural language models ieee conference publication.

Given the simplicity of the generation process and speed at which the domains are generated a fast and accurate detection method is required. Security researchers make a lot of efforts to detect algorithmically generated domains agds for fighting botnets and relevant malicious network behaviors. Recent work in dga detection leveraged deep learning architectures like convolutional neural networks cnns and character level long short term memory networks lstms to classify domains.

To evade detection botnets apply dns domain fluxing for command and control c c servers. Abstract domain generation algorithms dgas are fre quently employed by malware to generate domains used for connecting to command and control c2 servers. A convolutional generative adversarial network for symbolic domain music generation.

Domain generation algorithms dgas are frequently employed by malware to generate domains used for connecting to command and control c2 servers. This research considers contemporary state of the art dga malicious detection approaches and proposes a deep learning architecture for detecting the dga. Various malware families frequently apply domain generation algorithms dgas to generate numerous pseudorandom domain names to communicate with their command and control c c servers.