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While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control…
Frontier artificial intelligence (AI) systems present both benefits and risks to society. Safety cases - structured arguments supported by evidence - are one way to help ensure the safe development and deployment of these systems. Yet the…
Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum…
Vehicular Communication (VC) systems are on the verge of practical deployment. Nonetheless, their security and privacy protection is one of the problems that have been addressed only recently. In order to show the feasibility of secure VC,…
Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for…
Open-source software (OSS) dependencies introduce systemic risks that are difficult to manage at scale. Existing Software Composition Analysis (SCA) and reachability tools generate severe alert fatigue by treating risk as an intrinsic…
With the escalating threats posed by cyberattacks on Industrial Control Systems (ICSs), the development of customized Industrial Intrusion Detection Systems (IIDSs) received significant attention in research. While existing literature…
There is a gap in scientific information systems development concerning modern software engineering and scientific computing. Historically, software engineering methodologies have been perceived as an unwanted accidental complexity to…
The performance of Machine Learning (ML) and Deep Learning (DL)-based Intrusion Detection and Prevention Systems (IDS/IPS) is critically dependent on the relevance and quality of the datasets used for training and evaluation. However,…
This research assesses the aspects of software organizations' DevOps environments and identifies the factors contributing to these environments' success. DevOps is a recent concept, and many organizations are moving from old-style software…
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud…
In 2023, Sonatype reported a 200\% increase in software supply chain attacks, including major build infrastructure attacks. To secure the software supply chain, practitioners can follow security framework guidance like the Supply-chain…
The visions and ideas of Industry 4.0 require a profound interconnection of machines, plants, and IT systems in industrial production environments. This significantly increases the importance of software, which is coincidentally one of the…
Employees are often required to use Enterprise Security Software ("ESS") on corporate and personal devices. ESS products collect users' activity data including users' location, applications used, and websites visited - operating from…
The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and…
AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI…
Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are…
Although machine learning is widely used in practice, little is known about practitioners' understanding of potential security challenges. In this work, we close this substantial gap and contribute a qualitative study focusing on…
Software vulnerabilities (SVs) have emerged as a prevalent and critical concern for safety-critical security systems. This has spurred significant advancements in utilizing AI-based methods, including machine learning and deep learning, for…
Water scarcity and the low quality of wastewater produced in industrial applications present significant challenges, particularly in managing fresh water intake and reusing residual quantities. These issues affect various industries,…