Sustainability has become a crucial aspect of modern society and research. The emerging fusion of digital spaces with societal functions highlights the importance of sustainability. With digital technologies becoming essential, cybersecurity and digital forensics are gaining prominence. While cybersecurity’s role in sustainability is recognized, sustainable practices in digital forensics are still in their early stages. This paper presents a holistic view of innovative approaches for the sustainable design and management of digital forensics concerning people, processes, and technology. It outlines how these aspects contribute to sustainability, which aligns with the core principles of economic viability, social equity, and environmental responsibility. As a result, this approach provides novel perspectives on the development of sustainability in the field of digital forensics.

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.

In this work, we present SMART-Env (Spatial Multi-Agent Resource search Training Environment), a spatio-temporal multi-agent environment for evaluating and training different kinds of agents on resource search tasks. We explain how to simulate arbitrary spawning distributions on real-world street graphs, compare agents’ behavior and evaluate their performance over time. Finally, we demonstrate SMART-Env in a taxi dispatching scenario with three different kinds of agents.

Collecting spatio-temporal resources is an important goal in many real-world use cases such as finding customers for taxicabs. In this paper, we tackle the resource search problem posed by the GIS Cup 2019 where the objective is to minimize the average search time of taxicabs looking for customers. The main challenge is that the taxicabs may not communicate with each other and the only observation they have is the current time and position. Inspired by radial transit route structures in urban environments, our approach relies on round trips that are used as action space for a downstream reinforcement learning procedure. Our source code is publicly available at https://github.com/Fe18/TripBanditAgent.

Routing to a resource (e.g. a parking spot or charging station) is a probabilistic search problem due to the uncertainty as to whether the resource is available at the time of arrival or not. In recent years, more and more real-time information about the current state of resources has become available in order to facilate this task. Therefore, we consider the case of a driver receiving online updates about the current situation. In this setting, the problem can be described as a fully observable Markov Decision Process (MDP) which can be used to compute an optimal policy minimizing the expected search time. However, current approaches do not scale beyond a dozen resources in a query. In this paper, we suggest to adapt common approximate solutions for solving MDPs. We propose a new re-planning and hindsight planning algorithm that redefine the state space and rely on novel cost estimations to find close to optimal results. Unlike exact solutions for computing MDPs, our approximate planers can scale up to hundreds of resources without prohibitive computational costs. We demonstrate the result quality and the scalability of our approaches on two settings describing the search for parking spots and charging stations in an urban environment.