In the aviation sector, communication problems have contributed into 70% to 80% of safety occurrences. However, to date we haven’t depicted which communication aspects have affected aviation safety most frequently.Based on literature, we developed a tool which includes communication characteristics related to actors, signal, coder, channel, decoder, direction, timing, distance, predictability and interference. After achieving inter-rater reliability, the tool was used to analyse 103 safety investigation reports that correspond to events occurred in various regions and which included in total 256 communication problems. The results suggest that communication between humans and representation media, visual and audio signalling and decoding, air-transmitted messages, and verbal, unidirectional, local and synchronous communication contributed most frequently into safety events. Statistical tests showed that the frequencies of most of those characteristics were significantly different across regions, time periods, types of operations and event severity.The tool developed can be used by different organizations and industry sectors to distil and analyse data from mandatory and voluntary reports and identify weak communication areas. Depending on the findings, analysts might need to alert designers of technical systems, inform management of organizations, warn end-users about most frequent pitfalls, modify/enrich communication training and steer research efforts.
Charging an electric vehicle needs to be as simple as possible for the user. He needs to park his car, plug his vehicle and identify to start charging. There is no need to understand the technology and protocols needed to reach this simple task.For the students and researchers of the Amsterdam University of Applied Science (AUAS / HvA), there is a need to understand as deep as possible all the techniques involved in this technology.The purpose of this document is to give to the reader the information he needs to understand how an electric car can be charged and how he can use these knowledges to analyses and interpret data.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
During the coronavirus pandemic, the use of eHealth tools became increasingly demanded by patients and encouraged by the Dutch government. Yet, HBO health professionals demand clarity on what they can do, must do, and cannot do with the patients’ data when using digital healthcare provision and support. They often perceive the EU GDPR and its national application as obstacles to the use of eHealth due to strict health data processing requirements. They highlight the difficulty of keeping up with the changing rules and understanding how to apply them. Dutch initiatives to clarify the eHealth rules include the 2021 proposal of the wet Elektronische Gegevensuitwisseling in de Zorg and the establishment of eHealth information and communication platforms for healthcare practitioners. The research explores whether these initiatives serve the needs of HBO health professionals. The following questions will be explored: - Do the currently applicable rules and the proposed wet Elektronische Gegevensuitwisseling in de Zorg clarify what HBO health practitioners can do, must do, and cannot do with patients’ data? - Does the proposed wet Elektronische Gegevensuitwisseling in de Zorg provide better clarity on the stakeholders who may access patients’ data? Does it ensure appropriate safeguards against the unauthorized use of such data? - Does the proposed wet Elektronische Gegevensuitwisseling in de Zorg clarify the EU GDPR requirements for HBO health professionals? - Do the eHealth information and communication platforms set up for healthcare professionals provide the information that HBO professionals need on data protection and privacy requirements stemming from the EU GDPR and from national law? How could such platforms be better adjusted to the HBO professionals’ information and communication needs? Methodology: Practice-oriented legal research, semi-structured interviews and focus group discussions will be conducted. Results will be translated to solutions for HBO health professionals.
