Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. Both hardware accelerated and parallel software-based NIDS solutions, based on commodity multi-core and GPU processors, have been proposed to overcome these challenges. This work explores new parallel opportunities afforded by many-core processors for high performance, scalable and inexpensive NID-S. We exploit the huge many-core computational power by adopting a hybrid parallel architecture combining data and pipeline parallelism. We also design a hybrid load balancing scheme, using both ruleset and flow space partitioning. Furthermore, the proposed design leverages particular features of the processor to break the bottlenecks. We have integrated the open source NIDS Suricata into our proposed design and evaluated its performance with synthetic traffic. The prototype exhibits almost linear speedup and can handle up to 7.2 Gbps traffic with 100-bytes packets.
Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments.
The microstructure control and optimization of zeolite films and membranes is an indispensable challenge for various innovative applications. It can be steered by understanding the formation process. Here we design an unprecedented strategy to uncover direct evidence via the hydrothermal synthesis of chitosan-supported zeolite monolayers. The chitosan-supported layer involved in the hydrothermal reaction is observed using SEM, AFM, EPMA, and HRTEM while nucleation and crystal growth in the bulk synthesis solution are pursued with HRTEM, DLS, and SEM. The direct HRTEM observation is achieved on the chitosan-supported layer by peeling chitosan from its support. It reveals that a gel layer is initially formed on the chitosan layer where the subsequent crystal growth is fatally restrained. Our own experimental evidence and the literature reports clearly demonstrate that the formation mechanism is homogeneous for severely reduced crystal growth on the substrate but is heterogeneous when crystal growth on the substrate is significantly enhanced.
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