| Title | Adversarial Machine Learning for Enhanced Spread Spectrum Communications |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Fadul, Mohamed K. M., Reising, Donald R., Arasu, K. T., Clark, Michael R. |
| Conference Name | MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) |
| Date Published | nov |
| Keywords | adversarial learning, codes, Deep Learning, DSSS, Gold, human factors, Internet of Battlefield Things (IoBT), iobt, IoT, military communication, performance evaluation, pubcrawl, radio transmitters, resilience, Resiliency, Scalability, spread spectrum communication |
| Abstract | Recently deep learning has demonstrated much success within the fields of image and natural language processing, facial recognition, and computer vision. The success is attributed to large, accessible databases and deep learning's ability to learn highly accurate models. Thus, deep learning is being investigated as a viable end-to-end approach to digital communications design. This work investigates the use of adversarial deep learning to ensure that a radio can communicate covertly, via Direct Sequence Spread Spectrum (DSSS), with another while a third (the adversary) is actively attempting to detect, intercept and exploit their communications. The adversary's ability to detect and exploit the DSSS signals is hindered by: (i) generating a set of spreading codes that are balanced and result in low side lobes as well as (ii) actively adapting the encoding scheme. Lastly, DSSS communications performance is assessed using energy constrained devices to accurately portray IoT and IoBT device limitations. |
| DOI | 10.1109/MILCOM52596.2021.9652911 |
| Citation Key | fadul_adversarial_2021 |
Adversarial Machine Learning for Enhanced Spread Spectrum Communications