Trust Management in the Internet of Everything

Digitalization is leading us towards a future where people, processes, data and things are not only interacting with each other, but might start forming societies on their own. In these dynamic systems enhanced by artificial intelligence, trust management on the level of human-to-machine as well as machine-to-machine interaction becomes an essential ingredient in supervising safe and secure progress of our digitalized future. This tutorial paper discusses the essential elements of trust management in complex digital ecosystems, guiding the reader through the definitions and core concepts of trust management. Furthermore, it explains how trust-building can be leveraged to support people in safe interaction with other (possibly autonomous) digital agents, as trust governance may allow the ecosystem to trigger an auto-immune response towards untrusted digital agents, protecting human safety.Comment: Proceedings of the 16th European Conference on Software Architecture-Companion Volum

Future Vision of Dynamic Certification Schemes for Autonomous Systems

As software becomes increasingly pervasive in critical domains like autonomous driving, new challenges arise, necessitating rethinking of system engineering approaches. The gradual takeover of all critical driving functions by autonomous driving adds to the complexity of certifying these systems. Namely, certification procedures do not fully keep pace with the dynamism and unpredictability of future autonomous systems, and they may not fully guarantee compliance with the requirements imposed on these systems. In this paper, we have identified several issues with the current certification strategies that could pose serious safety risks. As an example, we highlight the inadequate reflection of software changes in constantly evolving systems and the lack of support for systems' cooperation necessary for managing coordinated movements. Other shortcomings include the narrow focus of awarded certification, neglecting aspects such as the ethical behavior of autonomous software systems. The contribution of this paper is threefold. First, we analyze the existing international standards used in certification processes in relation to the requirements derived from dynamic software ecosystems and autonomous systems themselves, and identify their shortcomings. Second, we outline six suggestions for rethinking certification to foster comprehensive solutions to the identified problems. Third, a conceptual Multi-Layer Trust Governance Framework is introduced to establish a robust governance structure for autonomous ecosystems and associated processes, including envisioned future certification schemes. The framework comprises three layers, which together support safe and ethical operation of autonomous systems

Effective measures to foster girls’ interest in secondary computer science education: A Literature Review

The interest of girls in computing drops early during primary and secondary education, with minimal recovery in later education stages. In combination with the growing shortage of qualified computer science personnel, this is becoming a major issue, and also a target of numerous studies that examine measures, interventions, and strategies to boost girls’ commitment to computing. Yet, the results of existing studies are difficult to navigate, and hence are being very rarely employed in classrooms. In this paper, we summarize the existing body of knowledge on the effective interventions to recruit and retain girls in computer science education, intending to equip educators with a comprehensive and easy-to-navigate map of interventions recommended in the existing literature. To this end, we perform an aggregated umbrella literature review of 11 existing reviews on the topic, together accumulating joined knowledge from over 800 publications, and formulate the findings in a map of 22 concrete interventions structured in six groups according to their phase and purpose

A Paradigm for Safe Adaptation of Collaborating Robots

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment

CopAS: A Big Data Forensic Analytics System

With the advancing digitization of our society, network security has become one of the critical concerns for most organizations. In this paper, we present CopAS, a system targeted at Big Data forensics analysis, allowing network operators to comfortably analyze and correlate large amounts of network data to get insights about potentially malicious and suspicious events. We demonstrate the practical usage of CopAS for insider threat detection on a publicly available PCAP dataset and show how the system can be used to detect insiders hiding their malicious activity in the large amounts of networking data streams generated during the daily activities of an organization