Digital twins are taking a central role in the industry 4.0 narrative. How- ever, they are still illusive. Many aspects of the digital-twins have yet to materialize. For example, to what degree will they be integrated into cloud and industry 4.0 sys- tems as well as how and if they should augment their physical counterpart. Those choices are accompanied by challenging security aspects, many of which have to be studied partially. In this paper, we present a novel digital-twin demonstrator that en- ables experimentation and advanced research on such systems. The demonstrator is cloud-native, has a distributed adaptive control system, incorporates edge and public clouds, a PLC, intrusion detection, a wireless network emulator, and an attacker.
Nowadays IT infrastructures have to supply a flexible and dynamic platform for the provision of modern applications. Kubernetes is one of the most notable environments for the provisioning of small and independently running microservices used by modern applications. With Kubernetes, these microservices can be developed, deployed, updated and scaled in a continuous process. This flexibility is a huge advantage to older and more static environments. But whereas these old infrastructures lack in dynamics, necessary digital investigation are easier to accomplish. This need is still existing in modern environments, hence this paper presents a novel approach for the lawful interception of network packets in a Kubernetes cluster. The approach improves the dynamic capture processes by monitoring involved devices assigned to a defined application without hampering the environment or capturing unwanted network packets. Keywords: Kubernetes, network
A casualty of disasters is the communication infrastructure. Rescuers, in the aftermath of the disaster, require solutions to maintain communications in order to communicate critical information gathered by them. Despite the numerous solutions proposed, a drawback is the communication range. In this work, we propose a communication system based on LoRa, a long-range, low-power communication technology. We use the commercially available, off-the-shelf LoRa based PyCom LoPy4 platform with opportunistic networking to demonstrate the viability of using LoRa for post-disaster recovery operations.
While many organizations share threat intelligence, there is still a lack of actionable data for organizations to proactively and effectively respond to emerging identity threats to mitigate a wide range of crimes. There currently exists no solution for organizations to access current trends and intelligence to understand emerging threats and how to appropriately respond to them. This research project delivers I-WARN to help bridge that gap. Using a wide range of open-source information, I-WARN gathers, analyzes, and reports on threats related to the theft, fraud, and abuse of Personally Identifiable Information (PII). I-WARN then maps those threats to the MITRE ATT&CK -- a framework that helps understand lateral movement of an attack -- to offer mitigation and risk reduction tactics. I-WARN aims to deliver actionable intelligence, offering early warning into threat behaviors, and mitigation responses. This paper discusses the technical details of I-WARN, non-exhaustive current solutions for threat intelligence sharing, and future work.
As robots get damaged or security compromised, their components will increasingly require updates and replacements. Contrary to the expectations, most manufacturers employ planned obsolescence practices and discourage repairs to evade competition. We introduce and advocate for robot teardownas an approach to study robot hardware architectures and fuel security research. We show how our approach helps uncovering security vulnerabilities, and provide evidence of planned obsolescence practices
Upcoming communication systems increasingly often tackle the spectrum scarcity problem through the coexistence with legacy systems in the same frequency band. Cognitive Radio presents popular methods for Dynamic Spectrum Access (DSA) that enable coexistence. Historically, DSA meant a separation solely in the frequency domain, while in recent years it has been extended through the dimension of time, by employing Machine Learning to learn semi-deterministic and cyclic medium access patterns of the legacy system that are observed through channel sensing. When this pattern is learnable, then a new system can utilize a neural network and predict future medium accesses, thus steering its own medium access. We investigate this novel and more fine-grained version of DSA, propose a predictor and show its capability of reliably predicting future medium accesses of a legacy system in an aeronautical coexistence scenario. We extend the predictor to the case of partial observability, where only a narrowband receiver is available, s.t. observations are limited to a single sensed channel per time slot. In particular, we propose a custom loss function that is tailored to partially observable environments. In the spirit of Open Science, all implementation files are released under an open license.
Due to impediments associated with cable-based seismic survey, Wireless Seismic Data Acquisition (WSDA) has recently gained much attention from contractors, exploration companies, and researchers to layout enabling wireless technology and architecture for Wireless Geophone Networks (WGN) in seismic explorations. A potential approach is to employ multi-hop wireless ad-hoc communication. In this study, we propose a multi-hop WGN architecture consisting of several subnetworks to realize the expected network performance. We investigate the performance of proactive and reactive routing protocols to examine the optimal number of geophones that could be effectively supported within a subnetwork. The performance metrics used are packet delivery ratio (PDR) and average end-to-end delay.
