Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently, which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN), and it has great potential for tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN, and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN, and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on an NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods, we argue and evaluate the most prominent GANs for tabular network data synthesis.
Current Multi-View Stereo (MVS) algorithms are tools for high-quality 3D model reconstruction, strongly depending on image spatial resolution. In this context, the combination of image Super-Resolution (SR) with image-based 3D reconstruction is turning into an interesting research topic in photogrammetry, around which however only a few works have been reported so far in the literature. Here, a thorough study is carried out on various state-of-the-art image SR techniques to evaluate the suitability of such an approach in terms of its inclusion in the 3D reconstruction process. Deep-learning techniques are tested here on a UAV image dataset, while the MVS task is then performed via the Agisoft Metashape photogrammetric tool. The data under experimentation are oblique cultural heritage imagery. According to results, point clouds from low-resolution images present quality inferior to those from upsampled high-resolution ones. The SR techniques HAT and DRLN outperform bicubic interpolation, yielding high precision/recall scores for the differences of reconstructed 3D point clouds from the reference surface. The current study indicates spatial image resolution increased by SR techniques may indeed be advantageous for state-of-the art photogrammetric 3D reconstruction.
Abstract. In this work we present the development of a prototype, mobile mapping platform with modular design and architecture that can be suitably modified to address effectively both outdoors and indoors environments. Our system is built on the Robotics Operation System (ROS) and utilizes multiple sensors to capture images, pointclouds and 3D motion trajectories. These include synchronized cameras with wide angle lenses, a lidar sensor, a GPS/IMU unit and a tracking optical sensor. We report on the individual components of the platform, it’s architecture, the integration and the calibration of its components, the fusion of all recorded data and provide initial 3D reconstruction results. The processing algorithms are based on existing implementations of SLAM (Simultaneous Localisation and Mapping) methods combined with SfM (Structure-from-Motion) for optimal estimations of orientations and 3D pointclouds. The scope of this work, which is part of an ongoing H2020 program, is to digitize the physical world, collect relevant spatial data and make digital copies available to experts and public for covering a wide range of needs; remote access and viewing, process, design, use in VR etc.
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