In the last few years, Input/Output (I/O) bandwidth limitation of legacy computer architectures forced us to reconsider where and how to store and compute data across a large range of applications. This shift has been made possible with the concurrent development of both smart NICs and programmable switches with a common programming language(P4), and the advent of attached High Bandwidth Memory within smartNICs/FPGAs. Recently, proposals to use this kind of technology have emerged to tackle computer science related issues such as fast consensus algorithm in the net-work, network accelerated key-value stores, machine learn-ing, or data-center data aggregation. In this paper, we intro-duce a novel architecture that leverages these advancements to potentially accelerate and improve the processing of radio-astronomy Digital Signal Processing (DSP), such as correlators or beamformers, at unprecedented continuous rates inwhat we have called the “Atomic COTS” design. We givean overview of this new type of architecture to accelerate digital signal processing, leveraging programmable switches and HBM capable FPGAs. We also discuss how to handle radio astronomy data streams to pre-process this stream ofdata for astronomy science products such as pulsar timingand search. Finally, we illustrate, using a proof of concept,how we can process emulated data from the Square Kilometer Array(SKA) project to time pulsars.
Traffic fingerprinting and developing defenses against them has always been an arms race between the attackers and the defenders. The rapid evolution of deep learning methods makes developing stronger traffic fingerprinting models much easier, while overhead, latency, and deployment constraints restrict the abilities of the defenses. As such, there is always the need of coming up with novel defenses against traffic fingerprinting. In this paper, we propose SMAUG, a novel CGAN-based (Conditional Generative Adversarial Network) defense to protect video streaming traffic against fingerprinting. We first assess the performance of various GANs in video streaming traffic synthesis using multiple GAN quality metrics and show that CGAN outperforms other types of GANs such as basic GANs and WGANs (Wasserstein GAN). Our proposed defense, SMAUG, uses CGANs to synthesize video traffic flows and use those synthesized flows to camouflage the original traffic that needs protection. We compare SMAUG with other state-of-the-art defenses – FPA and d*-private methods, as well as a kernel density estimation-based baseline and show that SMAUG provides better privacy with lower overhead and delay.