radiation-induced effects in Ge-As-S chalcogenide glasses

The radiation-optical properties of vitreous semiconductors in the Ge-As-S system are studied near the 2D-3D topological phase transition. The results obtained are interpreted within the framework of the model of coordination defect centers, which are characteristic of chalcogenide vitreous semiconductors.     

Redistribution of residual stresses of the first kind in cylindrical specimens with hardened surface layers

The nature of the distribution in axial residual stresses of the first kind was determined for specimens from which successive surface layers had been removed. It was established that the complete removal (by machining) of surface hardened layers, whose extent is determined by metallographic examination and microhardness measurements, does not eliminate residual stresses which extend deeper than the work-hardened zone and beyond the confines of etch-resistant surface layers. The character of residual stress distribution in specimen subsurface regions is retained after the removal of metal surface layers.     

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.     

radiation-induced effects in Ge-As-S chalcogenide glasses

The radiation-optical properties of vitreous semiconductors in the Ge-As-S system are studied near the 2D-3D topological phase transition. The results obtained are interpreted within the framework of the model of coordination defect centers, which are characteristic of chalcogenide vitreous semiconductors.