Log-based monitoring, detection and automated correction of anomalies in the 5G Core

This project focuses on monitoring the 5G Core through logs to identify potential problems in the operations and workflows between the different network functions and the RAN. For this purpose, it has been used an environment based on open source RAN simulators (UERANSIM and my5G-RANTester), an open source implementation of the 5G Core (Open5GS), a SDR-based radio gNB (Amarisoft) and the Grafana Loki framework as a monitoring tool. In addition, an automation solution has been developed to detect unregistered UEs and to add them to the Open5gs database based on the analysis of received logs, the creation of alerts and the development of an API.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Deep Learning Enhanced Visulization Tool For Network Monitroing

In this era of web technology driven by social networks, cloud computing, big data, and E-business, technology is also rapidly evolving. Most of the information is stored and managed via the Internet. With an increase in these development tools and techniques, cyber-crime is constantly increasing. The level of damage these attacks cause to the system affects the organizations to the core. Contemporary Deep Learning and Machine Learning technologies have become the popular choice of intrusion detection systems for the detection and prediction of cyber-attack. Similarly, cyber-security visualization is also an integral and essential part of monitoring network traffic and optimization. Abundant work has already been done to detect attacks, but monitoring these attacks still appears as elusive as detection for cyber analysts. However, the current open-source visualization tool has not been integrated with Deep Learning models to gain intelligence on the network. While many researchers [3] are already working on cyber-attack defense mechanisms, this research also takes advantage of Deep Learning and Machine Learning technologies to contribute to the work against such crimes. A novel Deep Learning enhanced visualization tool is also proposed for malicious traffic node prediction and monitoring. The proposed method exploits the intriguing properties of Deep Learning models to gain intelligence for network monitoring. A real-world DARPA dataset has been used to validate the proposed method. Index Terms—Cyber-security, data analysis, data science, darpa-dataset, decision tree, deep learning, deep neural network, DL model, ML model, network analysis tool, network monitoring tool, supervised learning, support vector machine, visualization tool

Regional specialised observatories networks in technological development and innovation exemplified by the Silesia Voivodship

The Regional Specialised Observatories Network is a systemic tool to encourage interdisciplinary cooperation between the key participants of the regional innovation system in order to build the competitive advantage of the region. The network responds to the region’s requirements by creating a modern tool to monitor the effects of the pro-technological development of the region in particular areas of technology, established in the Technological Development Strategy (TDS) for the Silesian Voivodship for the years 2010-2020, which is a constituent of the Regional Innovation Strategy. The observatory network will concentrate on collecting and processing specialised knowledge in the areas of technology in accord with TDS, monitoring technological and economic trends and assessment of the endogenous technological potential of the Silesian Voivodship. The network’s operation, through the link to the regional observatory as well as to national initiatives, will stimulate many forms of cooperation and contribute to the bonding of economic circles, innovators, science and research centres, the regional government and authorities responsible for drawing up and implementing development policy. The Regional Specialised Observatories Network is an open structure geared towards collecting, processing and publicising specialised knowledge, being a trustworthy source of data and information on technological areas in the region. The article presents the Network’s impact on identifying challenges and technological trends in reference to the region’s potential.Preparation and printing funded by the National Agency for Research and Development under project “Kreator Innowacyjności – wparcie dla Przedsiębiorczości akademickiej

an open and modular hardware node for wireless sensor and body area networks

Health monitoring is nowadays one of the hottest markets due to the increasing interest in prevention and treatment of physical problems. In this context the development of wearable, wireless, open-source, and nonintrusive sensing solutions is still an open problem. Indeed, most of the existing commercial architectures are closed and provide little flexibility. In this paper, an open hardware architecture for designing a modular wireless sensor node for health monitoring is proposed. By separating the connection and sensing functions in two separate boards, compliant with the IEEE1451 standard, we add plug and play capabilities to analog transducers, while granting at the same time a high level of customization. As an additional contribution of the work, we developed a cosimulation tool which simplifies the physical connection with the hardware devices and provides support for complex systems. Finally, a wireless body area network for fall detection and health monitoring, based on wireless node prototypes realized according to the proposed architecture, is presented as an application scenario

Recognition of traffic generated by WebRTC communication

Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results