Document Type
Student Research Paper
Date
Summer 2020
Academic Department
Computer Science
Abstract
Implementing traditional machine learning models and neural networks has become trivial in detecting malicious network traffic and has sparked interest in many researchers investigating this field. Standard implementations include using the baseline models in packages such as sklearn, tensorflow, and keras. In this paper we seek to advance the field of network detection and produce results which will have great benefits in terms of speed and performance of these models. We take advantage of Intel’s DAAL and OpenVINO packages as they are the two best performance enhancing methods which are publicly available today. Furthermore, comparisons will be made to determine the impact of these two Intel packages on network intrusion detection.
Recommended Citation
Grohotolski, Matthew and DiLeo, Connor, "ANTA: Accelerated Network Traffic Analytics." (2020). Summer Scholarship, Creative Arts and Research Projects (SCARP). 14.
https://jayscholar.etown.edu/scarp/14
Notes
SCARP 2020
Faculty advisor: Dr. Li