With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents a greater threat than ever before. One promising solution for countering this threat is to leverage deep learning (DL)-based text classification methods for fake-news detection. However, since such methods have been shown to be vulnerable to adversarial attacks, the integrity and security of DL-based fake news classifiers are under question. Although many works study text classification under the adversarial threat, to the best of our knowledge, we do not find any work in literature that specifically analyzes the performance of DL-based fake-news detectors under adversarial settings. We bridge this gap by evaluating the performance of fake-news detectors under various configurations under black-box settings. In particular, we investigate the robustness of four different DL architectural choices-multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and a recently proposed Hybrid CNN-RNN trained on three different state-of-the-art datasets-under different adversarial attacks (Text Bugger, Text Fooler, PWWS, and Deep Word Bug) implemented using the state-of-the-art NLP attack library, Text-Attack. Additionally, we explore how changing the detector complexity, the input sequence length, and the training loss affect the robustness of the learned model. Our experiments suggest that RNNs are robust as compared to other architectures. Further, we show that increasing the input sequence length generally increases the detector's robustness. Our evaluations provide key insights to robustify fake-news detectors against adversarial attacks.INDEX TERMS fake news detection, deep neural networks, adversarial attacks, adversarial robustness.
Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing (WDM) capable free-space optic (FSO) channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution which grooms mice flows (MFs) and mission-critical flows (CFs) and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated serverto-server (S2S) express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.
State of the art wireless technologies have recently shown a great potential for enabling re-configurable data center network (DCN) topologies by augmenting the cabling complexity and link inflexibility of traditional wired data centers (DCs). In this paper, we propose an optical traffic grooming (TG) method for mice flows (MFs) and elephant flows (EFs) in wireless DCNs which are interconnected with wavelength division multiplexing (WDM) capable free-space optical (FSO) links. Since handling the bandwidth-hungry EFs along with delay-sensitive MFs over the same network resources have undesirable consequences, proposed TG policy handles MFs and EFs over distinctive network resources. MFs/EFs destined to the same rack are groomed into larger rack-to-rack MF/EF flows over dedicated lightpaths whose routes and capacities are jointly determined in a load balancing manner. Performance evaluations of proposed TG policy show a significant throughput improvement thanks to efficient bandwidth utilization of the WDM-FSO links. As MFs and EFs are needed to be separated, proposed TG requires expeditious flow detection mechanisms which can immediately classify EFs with very high accuracy. Since these cannot be met by existing packet-sampling and port-mirroring based solutions, we propose a fast and lightweight in-network flow detection (LightFD) mechanism with perfect accuracy. LightFD is designed as a module on the Virtual-Switch/Hypervisor, which detects EFs based on acknowledgment sequence number of flow packets. Emulation results show that LightFD can provide up to 500 times faster detection speeds than the sampling-based methods with %100 detection precision. We also demonstrate that the EF detection speed has a considerable impact on achievable EF throughput.
LightFDG is an integrated approach to flow detection (FD) and flow grooming (FG) in optical wireless data center networks (DCNs), which is interconnected via wavelength division multiplexing (WDM) based free-space optical (FSO) links. Since forwarding bandwidth-hungry elephant flows (EFs) and delay-sensitive mice flows (MFs) on the same path can cause severe performance degradation, the LightFDG optically grooms flows of each class into rack-to-rack (R2R) flows. Then, R2R-MF and R2R-EF flows are separately forwarded over lightpaths of separate MF and EF virtual topologies, respectively. Lightpaths are provisioned by jointly determining the capacity and route based on flows' arrival rate, size, and completion time request. To prevent EFs from congesting the MF lightpaths, high speed and accurate flow-detection mechanisms are also necessary for classifying EFs as soon as possible. Therefore, a fast-lightweight-andaccurate flow detection framework is developed by leveraging the transmission control protocol (TCP) behaviors. The proposed FD scheme has the flexibility of being implemented as in-network or centralized to classify flows of modifiable and unmodifiable hosts, respectively. Since the centralized scheme incurs considerable overhead, the processing and communication overhead is also mitigated by proposed techniques. Numerical results show that LightFDG outperforms traditional load balancers by about 3f or EFs and 10ˆfor MFs. Along with the developed overhead mitigation methods, the centralized scheme is shown to provide up to 62ˆlower overhead with 100% accuracy and with about 224ˆhigher detection speeds than the existing centralized solutions.
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