# Special Issue: Selected Papers from IEEE ICASI 2019
###### Abstract
If detecting malicious traffic can be performed at the packet level, then the detection time will be significantly reduced, thereby improving the potential of online real-time malicious traffic detection based on deep learning technologies. To accelerate the entire detection process by considering a packet-level classification, which has not been studied in the literature, we propose a novel approach in building the malicious classification system with the primary support of word embedding and an LSTM model. Specifically, we propose a novel word-embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and further classify whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, the IoT dataset from Robert Gordon University, and the IoT dataset collected on our Mirai botnet show that our approach is competitive to the prior literature, which detects malicious traffic at the flow level. As network traffic continues to grow every year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay. [PERSON] et al. [4] reported on \"Hierarchical Guided Image Filtering for Efficient Stereo Matching\". Stereo matching is complicated by the uneven distribution of textures on image pairs. We address this problem by applying edge-preserving guided image filtering (GIF) at different resolutions. In contrast to most multi-scale stereo-matching algorithms, the parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive GIF scheme, by equipping it with hierarchical filtering modules, thereby resulting in a disparity in images with a large number of details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation, thereby improving matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets. [PERSON] et al. [5] reported on \"Automatic Error Compensation for Free-form Surfaces by Using On-machine Measurement Data\". Currently, most computer numerical control controllers lack the function needed to compensate machining errors for free-form surfaces. The objective of this study is to enhance the accuracy and precision of the machined free-form surfaces of a workpiece using a mirror compensation method with on-measurement data. A new free-form surface for finishing machining can be automatically reconstructed by mirroring the points measured after semi-finishing. The surface can then be used to generate the cutting tool path to reduce errors during finishing. In this study, three types of surfaces were used to evaluate the proposed method. The results show that the proposed method reduced the standard deviations of the three surface geometries by 61%, 61%, and 32%. We also evaluated the tool radius modification method commonly used in the industry for error compensation and found no substantial reduction on standard deviation. Therefore, the effectiveness of the proposed error compensation method is evident. [PERSON] et al. [6] reported on \"3D and Boundary Effects on 2D Electrical Resistivity Tomography\". Electrical resistivity tomography (ERT) is one of the most widely used geophysical methods in geological, hydrogeological, and geo-environmental investigations. Although 3D ERT is now available, 2D ERT remains state-of-the-practice due to its simplicity in fieldwork and lower space requirements.