We propose a method for covert communications using an IEEE 802.11 OFDM/QAM packet as a carrier. We show how to hide the covert message so that the transmitted signal does not violate the spectral mask specified by the standard, and we determine its impact on the OFDM packet error rate (PER). We show conditions under which the hidden signal is not usable and those under which it can be retrieved with a usable bit error rate (BER). The hidden signal is extracted by cancellation of the OFDM signal in the covert receiver. We explore the effects of the hidden signal on OFDM parameter estimation and the covert signal BER. We test the detectability of the covert signal with and without cancellation. We conclude with an experiment where we inject the hidden signal into Over-The-Air (OTA) recordings of 802.11 packets and demonstrate the effectiveness of the technique using that real-world OTA data.

In a spectrum-monitoring scenario, a monitor will attempt to intercept and classify a signal. If the monitor uses a Convolutional Neural Network (CNN) for classification, the intercepted signal can frustrate classification attempts by employing an adversarial waveform. An adversarial waveform is a small additive perturbation at the transmitter, and is generated similarly to adversarial attacks against image classifiers. We demonstrate that without foreknowledge of the CNN employed at the monitor the communication system can develop such an adversarial waveform and deploy it thus transferring the attack. The adversarial waveform is created by constraining the signal-to-interference ratio at the transmitter, which has the dual benefits of making the adversarial waveform easy to deploy and mitigates impairment to the communications link. We also demonstrate the vulnerability of a spectrum monitoring system to this type of attack as a function of symbol rate uncertainty, where the monitor does not have an exact estimate of the symbol rate employed by the communications link. The spectrum monitor becomes more susceptible to the attack as bandwidth is increased.

SUMMARY: Many eukaryotic proteins are modified by N-glycans. Liquid chromatography (ultra-performance -UPLC and high-performance-HPLC) coupled with mass spectrometry (MS) is conventionally used to characterize N-glycan structures. Software can automatically assign glycan structures by matching their observed retention times and masses with standardized values in reference databases. However, more precise confirmation of N-glycan structures can be derived using exoglycosidases, enzymes that remove specific monosaccharides from glycans. Exoglycosidase removal of monosaccharides results in signature peak shifts, in both UPLC and MS1, yielding an effective way to verify N-glycan structure with high detail (down to the position and isomeric linkage of each monosaccharide). Because manual interpretation of exoglycosidase data is complex and time consuming, we developed GlycanAnalyzer, a web application that pattern matches N-glycan peak shifts following exoglycosidase digestion and automates structure assignments. GlycanAnalyzer significantly improves assignment accuracy over other auto-assignment methods on tests with a monoclonal antibody and four glycan standards (100% versus 82% for the next best software). By automating data interpretation, GlycanAnalyzer enables the easier use of exoglycosidases to precisely define N-glycan structure. AVAILABILITY AND IMPLEMENTATION: http://glycananalyzer.neb.com. Datasets available online. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) doi: 10.1093/bioinformatics/bty681, 35, 688–690. In the original article, author Tasha José’s first and surname were originally transposed. This has now been corrected.