Mr. Skyler Carlson Profile

Skyler Carlson

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Proceedings Article | 14 June 2023 Poster + Paper

Data generation and separation of AM radio collisions with machine learning

Skyler Carlson, Jennifer Liu, Gerardo Leal, Keith Palermo

Proceedings Volume 12529, 125290U (2023) https://doi.org/10.1117/12.2662948

KEYWORDS: Modulation, Signal to noise ratio, Machine learning, Amplitude modulation, Data modeling, Neural networks, RF communications, Mixtures, Deep learning

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A yet-unsolved problem in the Radio Frequency (RF) domain is with transmission collisions in Amplitude Modulated (AM) radio, an example of which is air traffic control radios. In a high-traffic environment like aviation, radio operators often unknowingly transmit at the same time, leading to other radios receiving both transmissions layered together. This renders both transmissions difficult – if not impossible – to understand, leading to frustration at best and, at worst, critical transmissions being completely lost. Machine learning (ML) can successfully separate multiple overlapping speakers in audio, but we extend this idea to the RF domain. Training a performant ML model for such a scenario requires ample quantities of not only the signals with the overlapping transmissions, but also each original, individual signal. This data is easier to collect for audio – and many open-source datasets for such tasks are readily available – but no such datasets exist for AM radio separation. To collect adequate volumes of such data that is sufficiently diverse would be time-consuming and expensive. To solve this problem, we turn to data generation. Using our custom data generation pipeline combined with a Deep Neural Network (DNN), we demonstrate a 98.9% increase in signal separation efficacy when using AM radio as compared to when using audio alone. AM radio collision mitigation has broad implications, especially in congested scenarios with a high likelihood of colliding transmitters like aviation communications. Successful separation of such signals enables mitigation logic, leading to a smoother and safer user experience.

Proceedings Article | 13 June 2023 Presentation + Paper

StegAI: detecting steganography with deep learning

Emily Beatty, Skyler Carlson

Proceedings Volume 12544, 125440I (2023) https://doi.org/10.1117/12.2662974

KEYWORDS: Steganography, Steganalysis, Machine learning, Deep learning, Neural networks

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Adversaries or internal threats wishing to exfiltrate sensitive data may choose a method that obfuscates the fact that a transfer is even taking place by camouflaging it inside of allowable data. This technique is known as steganography, which is the practice of concealing data (such as hidden communications or sensitive data of any kind) in an existing data transfer medium, such as images or video. Video streams may be targeted more readily than other media because of the bandwidth they offer compared to single images. Current methods for mitigating the risk associated with data exfiltration through steganography sanitization through unconditional alteration video frames. However, these approaches are limited as they do not detect if a steganography attack is occurring. Deep Neural Networks (DNNs) excel at pattern recognition, presenting an opportunity to leverage them for detection of steganography. Having the capability to detect steganography could prove extremely useful, with uses such as prompting security administrators to investigate further or to immediately halt a video stream. We investigate Least Significant Bit Steganography with both encrypted and cleartext payloads and show that machine learning can detect both.

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