Abstract: Immense growth of network usage and the associated proliferation of network, traffic, traffic classes, and diverse QoS requirements pose numerous challenges for network operators. Though data-driven approaches can provide better solutions for these challenges, limited data has been a barrier to developing those methods with high resiliency. In this work, we propose SyNIG (Synthetic Network Traffic Generation through Time Series Imaging) , which utilizes Generative Adversarial Networks (GANs) for network traffic synthesis by converting time series data to a specific image format called GASF (Gramian Angular Summation Field). With GASF images we encode correlation between samples in 1D signals on a single 2D pixel map. Taking three types of network traffic; video streaming, accessing websites
and IoT, we synthesize over 200,000 traces using over 40,000 original traces generalizing our method for different network traffic. We validate our method by demonstrating the fidelity of the synthetic data and applying them to several network related use cases showing improved performance.
Traffic fingerprinting allows making inferences about encrypted traffic flows through passive observation. They have been used for tasks such as network performance management and analytics and in attacker settings such as censorship and surveillance. A key challenge when implementing traffic fingerprinting in real- time settings is how the state-of-the-art traffic fingerprint models can be ported into programmable in-network computing devices with limited computing resources. Towards this, in this work, we characterize the performance of binarized traffic fingerprinting neural networks that are efficient and well-suited for in-network computing devices and propose a new data encoding method that is better suited for network traffic. Overall, we show that the proposed binary neural network with first-layer binarization and last-layer quantization reduces the performance requirement of hardware equipment while retaining the accuracies of those models of binary datasets over 70%. Furthermore, when combined with our proposed encoding algorithm, accuracies of binarized models of numeric datasets show further improvements to achieve over 65% accuracy.
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.