Becker, Christopher D

DOE OSTI2022

AviSense: A Real-time System for Detection, Classification, and Analysis of Aviation Signals

We present AviSense, an SDR-based real-time, versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and demonstrate that we can classify aviation signals as normal with 99% accuracy. We also provide an exhaustive evaluation of our anomalous signal detection capability and show that we can accurately classify anomalous signals with 99% accuracy, with few exceptions. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build a prototype implementation of AviSense for distributed monitoring.

42 ENGINEERING↗

DOE OSTI2021

An Overview of the Usefulness of Machine Learning Techniques on Network Packet Data

Understanding the health and behavior of a computer network allows for better network efficiency and security. We present an overview of various machine learning techniques for classifying network packet data via packet metadata. While some classical machine learning approaches achieve reasonable results, the most accurate classification can be achieved with deep learning. On the four data sets studied herein, a basic deep learning model achieved at or near 100\% classification accuracy. We also propose a method for determining variable importance as a means for potential transfer learning applications to classifying yet unseen network packet data.

97 MATHEMATICS AND COMPUTING↗