Security and Autonomic Management in System of Systems

A system of systems integrates systems that function independently but are networked together for a period of time to achieve a higher goal. These systems evolve over time and have emergent properties. Therefore, even with security controls in place, it is difficult to maintain a required level of security for the system of systems as a whole because uncertainties may arise at runtime. Uncertainties can occur from internal factors, such as malfunctions of a system, or from external factors, such as malicious attacks. Self-adaptation is an approach that allows a system to adapt in the face of such uncertainties without human intervention. This work outlines the progress made towards security mitigation in system of systems using a generic autonomic management system to assist engineers in developing self-adaptive systems. The manuscript describes the proposed system design, its implementation as part of the Eclipse Arrowhead framework, and its functionality in a smart agriculture use case. The system is designed and implemented in such a way that it can be reused and extended for a variety of use cases without requiring major changes.

AutoML for Log File Analysis (ALFA) in a Production Line System of Systems pointed towards Predictive Maintenance

Automated machine learning and predictive maintenance have both become prominent terms in recent years. Combining these two fields of research by conducting log analysis using automated machine learning techniques to fuel predictive maintenance algorithms holds multiple advantages, especially when applied in a production line setting. This approach can be used for multiple applications in the industry, e.g., in semiconductor, automotive, metal, and many other industrial applications to improve the maintenance and production costs and quality. In this paper, we investigate the possibility to create a predictive maintenance framework using only easily available log data based on a neural network framework for predictive maintenance tasks. We outline the advantages of the ALFA (AutoML for Log File Analysis) approach, which are high efficiency in combination with a low entry border for novices, among others. In a production line setting, one would also be able to cope with concept drift and even with data of a new quality in a gradual manner. In the presented production line context, we also show the superior performance of multiple neural networks over a comprehensive neural network in practice. The proposed software architecture allows not only for the automated adaption to concept drift and even data of new quality but also gives access to the current performance of the used neural networks.

Enhancing the operational efficiency of quantum random number generators

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.

Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.

QoS Impacts of Slice Traffic Limitation

Slicing is an essential building block of 5G networks and beyond. Different slices mean sets of traffic demands with different requirements, which need to be served over separated or shared network resources. Various service chaining methods applied to support slicing lead to different network load patterns, impacting the QoS experienced by the traffic. In this paper, we analyze QoS properties applying a theoretical model. We also suggest appropriate parameter setting policies in slice-aware service function chaining (SFC) algorithms to increase QoS. We evaluate several metrics in different analysis scenarios to show the advantages of the slice-aware approach.

Improved Model for Indoor Propagation Loss in the 5G FR2 Frequency Band

One of the latest developments today is the 5G, or 5th generation mobile network. In addition to a number of innovations, the new system also includes millimeter-wavelength frequency ranges denoted with FR2, that formerly not applied for these specific purposes. Proper management of the transmitter and receiver antenna beams is required for efficient communication in this frequency range. For future use, the simplest implementation way is electronically shaping the antenna beams by an algorithm to orient the antennas in the best possible direction. The prerequisites for these algorithms are appropriate propagation models, which are currently lacking, and those that publicly available are not accurate enough for practical use.

Risk Management and Standard Compliance for Cyber-Physical Systems of Systems

The Internet of Things (IoT) and cloud technologies are increasingly implemented in the form of Cyber-Physical Systems of Systems (CPSoS) for the railway sector. In order to satisfy the security requirements of Cyber-Physical Systems (CPS), domainspecific risk identification assessment procedures have been developed. Threat modelling is one of the most commonly used methods for threat identification for the security analysis of CPSoS and is capable of targeting various domains. This paper reports our experience of using a risk management framework identify the most critical security vulnerabilities in CPSoS in the domain and shows the broader impact this work can have on the domain of safety and security management. Moreover, we emphasize the application of common analytical methods for cyber-security based on international industry standards to identify the most vulnerable assets. These will be applied to a meta-model for automated railway systems in the concept phase to support the development and deployment of these systems. Furthermore, it is the first step to create a secure and standard complaint system by design.

Checking the Accuracy of Siitperf

Siitperf is the world’s first free software RFC 8219 compliant SIIT (Stateless IP/ICMP Translation, also called as Stateless NAT64) tester, which implements throughput, frame loss rate, latency and packet delay variation tests. In this paper, we show that the reliability of its results mainly depends on the accuracy of the timing of its frame sender algorithm. We also investigate the effect of Ethernet flow control on the measurement results. Siitperf is calibrated by the comparison of its results with that of a commercial network performance tester, when both of them are used for determining the throughput of the IPv4 routing of the Linux kernel.

Processing and Visualizing the Low Earth Orbit Radio Frequency Spectrum Measurement Results From the SMOG Satellite Project

December 6, 2019, the second and third Hungarian satellites, SMOG-P and ATL-1 (both having been developed at the Budapest University of Technology and Economics) were launched. They both had a radio frequency spectrum analyzer on board, which was used to measure for the first time the strength of radio frequency signals radiated into space by terrestrial digital TV transmitters – that can be detected in orbit around the Earth. In this paper, we present how two- and three-dimensional radiosmog maps were created from raw data received from space. The goal of this paper is to demonstrate the process of creating these maps from the raw data collected; the analysis of the results visible in these maps is beyond the scope of the present discussion.

An Enhanced Impulsive Noise Suppression Method Based on Wavelet Denoising and ICA for Power Line Communication

Aiming at the problem of noise suppression in power lines, traditional noise suppression methods need to know prior knowledge and other defects. In this paper, blind source separation methods that do not need prior knowledge are selected. In the case of low signal-to-noise ratio, the basic independent component analysis algorithm has poor denoising effect. Therefore, this paper proposes a joint independent component analysis algorithm based on Wavelet denoising and Power independent component analysis (WD-PowerICA). In this work, firstly, the pseudo observation signal is constructed by weighted processing, and the blind separation model of single channel is transformed into a multi-channel determined model. Then, the proposed WD-PowerICA algorithm is used to separate noise and source signals. Finally, the simulation results demonstrate that the proposed algorithm in this paper can effectively separate noise and source signal under low SNR. At the same time, the stronger the α pulse noise is, the closer the WD-PowerICA separated signal is to the source signal. The proposed algorithm is better than the state of the art PowerICA algorithm.