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With the increasing reliance of smart grids on correctly functioning SCADA systems and their vulnerability to cyberattacks, there is a pressing need for effective security measures. SCADA systems are prone to cyberattacks, posing risks to critical infrastructure. As there is a lack of host-based intrusion detection systems specifically designed for the stable nature of SCADA systems, the objective of this work is to propose a host-based intrusion detection system tailored for SCADA systems in smart grids. The proposed system utilizes USB device identification, flagging, and process memory scanning to monitor and detect anomalies in SCADA systems, providing enhanced security measures. Evaluation in three different scenarios demonstrates the tool's effectiveness in detecting and disabling malware. The proposed approach effectively identifies potential threats and enhances the security of SCADA systems in smart grids, providing a promising solution to protect against cyberattacks.

To meet the requirements of modern production, industrial communication increasingly shifts from wired fieldbus to wireless 5G communication. Besides tremendous benefits, this shift introduces severe novel risks, ranging from limited reliability over new security vulnerabilities to a lack of accountability. To address these risks, we present approaches to (i) prevent attacks through authentication and redundant communication, (ii) detect anomalies and jamming, and (iii) respond to detected attacks through device exclusion and accountability measures., Comment: Accepted to the poster session of the 22nd International Conference on Applied Cryptography and Network Security (ACNS 2024)

Power grids are becoming more digitized, resulting in new opportunities for the grid operation but also new challenges, such as new threats from the cyber-domain. To address these challenges, cybersecurity solutions are being considered in the form of preventive, detective, and reactive measures. Machine learning-based intrusion detection systems are used as part of detection efforts to detect and defend against cyberattacks. However, training and testing data for these systems are often not available or suitable for use in machine learning models for detecting multi-stage cyberattacks in smart grids. In this paper, we propose a method to generate synthetic data using a graph-based approach for training machine learning models in smart grids. We use an abstract form of multi-stage cyberattacks defined via graph formulations and simulate the propagation behavior of attacks in the network. Within the selected scenarios, we observed promising results, but a larger number of scenarios need to be studied to draw a more informed conclusion about the suitability of synthesized data.

The increasing digitization of smart grids has made addressing cybersecurity issues crucial in order to secure the power supply. Anomaly detection has emerged as a key technology for cybersecurity in smart grids, enabling the detection of unknown threats. Many research efforts have proposed various machine-learning-based approaches for anomaly detection in grid operations. However, there is a need for a reproducible and comprehensive evaluation environment to investigate and compare different approaches to anomaly detection. The assessment process is highly dependent on the specific application and requires an evaluation that considers representative datasets from the use case as well as the specific characteristics of the use case. In this work, we present an evaluation environment for anomaly detection methods in smart grids that facilitates reproducible and comprehensive evaluation of different anomaly detection methods., Comment: To be published in Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

The power grid is a critical infrastructure that plays a vital role in modern society. Its availability is of utmost importance, as a loss can endanger human lives. However, with the increasing digitalization of the power grid, it also becomes vulnerable to new cyberattacks that can compromise its availability. To counter these threats, intrusion detection systems are developed and deployed to detect cyberattacks targeting the power grid. Among intrusion detection systems, anomaly detection models based on machine learning have shown potential in detecting unknown attack vectors. However, the scarcity of data for training these models remains a challenge due to confidentiality concerns. To overcome this challenge, this study proposes a model for generating synthetic data of multi-stage cyber attacks in the power grid, using attack trees to model the attacker's sequence of steps and a game-theoretic approach to incorporate the defender's actions. This model aims to create diverse attack data on which machine learning algorithms can be trained.

Aggregation of message authentication codes (MACs) is a proven and efficient method to preserve valuable bandwidth in resource-constrained environments: Instead of appending a long authentication tag to each message, the integrity protection of multiple messages is aggregated into a single tag. However, while such aggregation saves bandwidth, a single lost message typically means that authentication information for multiple messages cannot be verified anymore. With the significant increase of bandwidth-constrained lossy communication, as applications shift towards wireless channels, it thus becomes paramount to study the impact of packet loss on the diverse MAC aggregation schemes proposed over the past 15 years to assess when and how to aggregate message authentication. Therefore, we empirically study all relevant MAC aggregation schemes in the context of lossy channels, investigating achievable goodput improvements, the resulting verification delays, processing overhead, and resilience to denial-of-service attacks. Our analysis shows the importance of carefully choosing and configuring MAC aggregation, as selecting and correctly parameterizing the right scheme can, e.g., improve goodput by 39% to 444%, depending on the scenario. However, since no aggregation scheme performs best in all scenarios, we provide guidelines for network operators to select optimal schemes and parameterizations suiting specific network settings., Comment: accepted at ACNS'24

Industrial control systems increasingly rely on middlebox functionality such as intrusion detection or in-network processing. However, traditional end-to-end security protocols interfere with the necessary access to in-flight data. While recent work on middlebox-aware end-to-end security protocols for the traditional Internet promises to address the dilemma between end-to-end security guarantees and middleboxes, the current state-of-the-art lacks critical features for industrial communication. Most importantly, industrial settings require fine-grained access control for middleboxes to truly operate in a least-privilege mode. Likewise, advanced applications even require that middleboxes can inject specific messages (e.g., emergency shutdowns). Meanwhile, industrial scenarios often expose tight latency and bandwidth constraints not found in the traditional Internet. As the current state-of-the-art misses critical features, we propose Middlebox-aware DTLS (Madtls), a middlebox-aware end-to-end security protocol specifically tailored to the needs of industrial networks. Madtls provides bit-level read and write access control of middleboxes to communicated data with minimal bandwidth and processing overhead, even on constrained hardware., Comment: accepted at AsiaCCS'24

Cyberattacks have grown into a major risk for organizations, with common consequences being data theft, sabotage, and extortion. Since preventive measures do not suffice to repel attacks, timely detection of successful intruders is crucial to stop them from reaching their final goals. For this purpose, many organizations utilize Security Information and Event Management (SIEM) systems to centrally collect security-related events and scan them for attack indicators using expert-written detection rules. However, as we show by analyzing a set of widespread SIEM detection rules, adversaries can evade almost half of them easily, allowing them to perform common malicious actions within an enterprise network without being detected. To remedy these critical detection blind spots, we propose the idea of adaptive misuse detection, which utilizes machine learning to compare incoming events to SIEM rules on the one hand and known-benign events on the other hand to discover successful evasions. Based on this idea, we present AMIDES, an open-source proof-of-concept adaptive misuse detection system. Using four weeks of SIEM events from a large enterprise network and more than 500 hand-crafted evasions, we show that AMIDES successfully detects a majority of these evasions without any false alerts. In addition, AMIDES eases alert analysis by assessing which rules were evaded. Its computational efficiency qualifies AMIDES for real-world operation and hence enables organizations to significantly reduce detection blind spots with moderate effort., Comment: To be published in Proceedings of the 33rd USENIX Security Symposium (USENIX Security 2024)

Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.

Critical infrastructure constitutes the foundation of every society. While traditionally solely relying on dedicated cable-based communication, this infrastructure rapidly transforms to highly digitized and interconnected systems which increasingly rely on wireless communication. Besides providing tremendous benefits, especially affording the easy, cheap, and flexible interconnection of a large number of assets spread over larger geographic areas, wireless communication in critical infrastructure also raises unique security challenges. Most importantly, the shift from dedicated private wired networks to heterogeneous wireless communication over public and shared networks requires significantly more involved security measures. In this paper, we identify the most relevant challenges resulting from the use of wireless communication in critical infrastructure and use those to identify a comprehensive set of promising opportunities to preserve the high security standards of critical infrastructure even when switching from wired to wireless communication., Comment: Author's version of a paper accepted for publication in Proceedings of the 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2023)

The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today's production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today's production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL., Comment: Published in: Brecher, C., Schuh, G., van der Aalst, W., Jarke, M., Piller, F.T., Padberg, M. (eds) Internet of Production. Interdisciplinary Excellence Accelerator Series. Springer, Cham

The increasing digitalization of power grids and especially the shift towards IP-based communication drastically increase the susceptibility to cyberattacks, potentially leading to blackouts and physical damage. Understanding the involved risks, the interplay of communication and physical assets, and the effects of cyberattacks are paramount for the uninterrupted operation of this critical infrastructure. However, as the impact of cyberattacks cannot be researched in real-world power grids, current efforts tend to focus on analyzing isolated aspects at small scales, often covering only either physical or communication assets. To fill this gap, we present WATTSON, a comprehensive research environment that facilitates reproducing, implementing, and analyzing cyberattacks against power grids and, in particular, their impact on both communication and physical processes. We validate WATTSON's accuracy against a physical testbed and show its scalability to realistic power grid sizes. We then perform authentic cyberattacks, such as Industroyer, within the environment and study their impact on the power grid's energy and communication side. Besides known vulnerabilities, our results reveal the ripple effects of susceptible communication on complex cyber-physical processes and thus lay the foundation for effective countermeasures., Comment: 14 pages, 13 figures, accepted at EuroS&P 2023

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication ows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner.

Power grids worldwide are increasingly victims of cyberattacks, where attackers can cause immense damage to critical infrastructure. The growing digitalization and networking in power grids combined with insufficient protection against cyberattacks further exacerbate this trend. Hence, security engineers and researchers must counter these new risks by continuously improving security measures. Data sets of real network traffic during cyberattacks play a decisive role in analyzing and understanding such attacks. Therefore, this paper presents PowerDuck, a publicly available security data set containing network traces of GOOSE communication in a physical substation testbed. The data set includes recordings of various scenarios with and without the presence of attacks. Furthermore, all network packets originating from the attacker are clearly labeled to facilitate their identification. We thus envision PowerDuck improving and complementing existing data sets of substations, which are often generated synthetically, thus enhancing the security of power grids., Comment: Cyber Security Experimentation and Test Workshop (CSET 2022)

The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2% vs. 0.4%), the overall adoption of TLS is comparably low (6.5% of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42% of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security., Comment: 15 pages, 6 figures

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products., Comment: IEEE ICBC'22

Resource-constrained devices increasingly rely on wireless communication for the reliable and low-latency transmission of short messages. However, especially the implementation of adequate integrity protection of time-critical messages places a significant burden on these devices. We address this issue by proposing BP-MAC, a fast and memory-efficient approach for computing message authentication codes based on the well-established Carter-Wegman construction. Our key idea is to offload resource-intensive computations to idle phases and thus save valuable time in latency-critical phases, i.e., when new data awaits processing. Therefore, BP-MAC leverages a universal hash function designed for the bitwise preprocessing of integrity protection to later only require a few XOR operations during the latency-critical phase. Our evaluation on embedded hardware shows that BP-MAC outperforms the state-of-the-art in terms of latency and memory overhead, notably for small messages, as required to adequately protect resource-constrained devices with stringent security and latency requirements., Comment: ACM WiSec'22

Anomaly-based intrusion detection promises to detect novel or unknown attacks on industrial control systems by modeling expected system behavior and raising corresponding alarms for any deviations.As manually creating these behavioral models is tedious and error-prone, research focuses on machine learning to train them automatically, achieving detection rates upwards of 99%. However, these approaches are typically trained not only on benign traffic but also on attacks and then evaluated against the same type of attack used for training. Hence, their actual, real-world performance on unknown (not trained on) attacks remains unclear. In turn, the reported near-perfect detection rates of machine learning-based intrusion detection might create a false sense of security. To assess this situation and clarify the real potential of machine learning-based industrial intrusion detection, we develop an evaluation methodology and examine multiple approaches from literature for their performance on unknown attacks (excluded from training). Our results highlight an ineffectiveness in detecting unknown attacks, with detection rates dropping to between 3.2% and 14.7% for some types of attacks. Moving forward, we derive recommendations for further research on machine learning-based approaches to ensure clarity on their ability to detect unknown attacks., Comment: ACM CPSS'22

Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research., Comment: 16 pages, 2 figures

Popular cryptocurrencies continue to face serious scalability issues due to their ever-growing blockchains. Thus, modern blockchain designs began to prune old blocks and rely on recent snapshots for their bootstrapping processes instead. Unfortunately, established systems are often considered incapable of adopting these improvements. In this work, we present CoinPrune, our block-pruning scheme with full Bitcoin compatibility, to revise this popular belief. CoinPrune bootstraps joining nodes via snapshots that are periodically created from Bitcoin's set of unspent transaction outputs (UTXO set). Our scheme establishes trust in these snapshots by relying on CoinPrune-supporting miners to mutually reaffirm a snapshot's correctness on the blockchain. This way, snapshots remain trustworthy even if adversaries attempt to tamper with them. Our scheme maintains its retrospective deployability by relying on positive feedback only, i.e., blocks containing invalid reaffirmations are not rejected, but invalid reaffirmations are outpaced by the benign ones created by an honest majority among CoinPrune-supporting miners. Already today, CoinPrune reduces the storage requirements for Bitcoin nodes by two orders of magnitude, as joining nodes need to fetch and process only 6 GiB instead of 271 GiB of data in our evaluation, reducing the synchronization time of powerful devices from currently 7 h to 51 min, with even larger potential drops for less powerful devices. CoinPrune is further aware of higher-level application data, i.e., it conserves otherwise pruned application data and allows nodes to obfuscate objectionable and potentially illegal blockchain content from their UTXO set and the snapshots they distribute., Comment: arXiv admin note: substantial text overlap with arXiv:2004.06911

Given the tremendous success of the Internet of Things in interconnecting consumer devices, we observe a natural trend to likewise interconnect devices in industrial settings, referred to as Industrial Internet of Things or Industry 4.0. While this coupling of industrial components provides many benefits, it also introduces serious security challenges. Although sharing many similarities with the consumer Internet of Things, securing the Industrial Internet of Things introduces its own challenges but also opportunities, mainly resulting from a longer lifetime of components and a larger scale of networks. In this paper, we identify the unique security goals and challenges of the Industrial Internet of Things, which, unlike consumer deployments, mainly follow from safety and productivity requirements. To address these security goals and challenges, we provide a comprehensive survey of research efforts to secure the Industrial Internet of Things, discuss their applicability, and analyze their security benefits., Comment: 12 pages, 2 figures, published in IEEE Transactions on Industrial Informatics

Artifacts such as log data and network traffic are fundamental for cybersecurity research, e.g., in the area of intrusion detection. Yet, most research is based on artifacts that are not available to others or cannot be adapted to own purposes, thus making it difficult to reproduce and build on existing work. In this paper, we identify the challenges of artifact generation with the goal of conducting sound experiments that are valid, controlled, and reproducible. We argue that testbeds for artifact generation have to be designed specifically with reproducibility and adaptability in mind. To achieve this goal, we present SOCBED, our proof-of-concept implementation and the first testbed with a focus on generating realistic log data for cybersecurity experiments in a reproducible and adaptable manner. SOCBED enables researchers to reproduce testbed instances on commodity computers, adapt them according to own requirements, and verify their correct functionality. We evaluate SOCBED with an exemplary, practical experiment on detecting a multi-step intrusion of an enterprise network and show that the resulting experiment is indeed valid, controlled, and reproducible. Both SOCBED and the log dataset underlying our evaluation are freely available., Comment: To be published in Proceedings of the 2021 Annual Computer Security Applications Conference (ACSAC '21)

The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL's correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos.

With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and insight into realistic attack scenarios in an appropriate and practical setting. In this paper, we present a man-in-the-middle-based attack scenario that intercepts process communication between control systems and field devices, employs false data injection techniques, and performs data corruption such as sending false commands to field devices. We demonstrate the applicability of the presented attack scenario in a physical smart grid laboratory environment and analyze the generated data under normal and attack conditions to extract domain-specific knowledge for detection mechanisms., Comment: To be published in Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

While the digitization of power distribution grids brings many benefits, it also introduces new vulnerabilities for cyber-attacks. To maintain secure operations in the emerging threat landscape, detecting and implementing countermeasures against cyber-attacks are paramount. However, due to the lack of publicly available attack data against Smart Grids (SGs) for countermeasure development, simulation-based data generation approaches offer the potential to provide the needed data foundation. Therefore, our proposed approach provides flexible and scalable replication of multi-staged cyber-attacks in an SG Co-Simulation Environment (COSE). The COSE consists of an energy grid simulator, simulators for Operation Technology (OT) devices, and a network emulator for realistic IT process networks. Focusing on defensive and offensive use cases in COSE, our simulated attacker can perform network scans, find vulnerabilities, exploit them, gain administrative privileges, and execute malicious commands on OT devices. As an exemplary countermeasure, we present a built-in Intrusion Detection System (IDS) that analyzes generated network traffic using anomaly detection with Machine Learning (ML) approaches. In this work, we provide an overview of the SG COSE, present a multi-stage attack model with the potential to disrupt grid operations, and show exemplary performance evaluations of the IDS in specific scenarios., Comment: To be published in Proceedings of the CIRED 2021 Conference

Urban LoRa networks promise to provide a cost-efficient and scalable communication backbone for smart cities. One core challenge in rolling out and operating these networks is radio network planning, i.e., precise predictions about possible new locations and their impact on network coverage. Path loss models aid in this task, but evaluating and comparing different models requires a sufficiently large set of high-quality received packet power samples. In this paper, we report on a corresponding large-scale measurement study covering an urban area of 200km2 over a period of 230 days using sensors deployed on garbage trucks, resulting in more than 112 thousand high-quality samples for received packet power. Using this data, we compare eleven previously proposed path loss models and additionally provide new coefficients for the Log-distance model. Our results reveal that the Log-distance model and other well-known empirical models such as Okumura or Winner+ provide reasonable estimations in an urban environment, and terrain based models such as ITM or ITWOM have no advantages. In addition, we derive estimations for the needed sample size in similar measurement campaigns. To stimulate further research in this direction, we make all our data publicly available., Comment: Author's version of a paper accepted for publication in Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

Electric power grids are at risk of being compromised by high-impact cyber-security threats such as coordinated, timed attacks. Navigating this new threat landscape requires a deep understanding of the potential risks and complex attack processes in energy information systems, which in turn demands an unmanageable manual effort to timely process a large amount of cross-domain information. To provide an adequate basis to contextually assess and understand the situation of smart grids in case of coordinated cyber-attacks, we need a systematic and coherent approach to identify cyber incidents. In this paper, we present an approach that collects and correlates cross-domain cyber threat information to detect multi-stage cyber-attacks in energy information systems. We investigate the applicability and performance of the presented correlation approach and discuss the results to highlight challenges in domain-specific detection mechanisms., Comment: To be published in Proceedings of the 2021 International Conference on Smart Energy Systems and Technologies (SEST)

We examine four high resolution reflection grating spectrometers (RGS) spectra of the February 2009 outburst of the luminous recurrent nova LMC 2009a. They were very complex and rich in intricate absorption and emission features. The continuum was consistent with a dominant component originating in the atmosphere of a shell burning white dwarf (WD) with peak effective temperature between 810,000 K and a million K, and mass in the 1.2-1.4 M$_\odot$ range. A moderate blue shift of the absorption features of a few hundred km s$^{-1}$ can be explained with a residual nova wind depleting the WD surface at a rate of about 10$^{-8}$ M$_\odot$ yr$^{-1}$. The emission spectrum seems to be due to both photoionization and shock ionization in the ejecta. The supersoft X-ray flux was irregularly variable on time scales of hours, with decreasing amplitude of the variability. We find that both the period and the amplitude of another, already known 33.3 s modulation, varied within timescales of hours. We compared N LMC 2009a with other Magellanic Clouds novae, including 4 serendipitously discovered as supersoft X-ray sources (SSS) among 13 observed within 16 years after the eruption. The new detected targets were much less luminous than expected: we suggest that they were partially obscured by the accretion disk. Lack of SSS detections in the Magellanic Clouds novae more than 5.5 years after the eruption constrains the average duration of the nuclear burning phase., Comment: Accepted for publication in MNRAS

Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identify a broad set of resulting attack vectors and attack scenarios that threaten the security of power grids. To address these challenges, we propose to rely on a defense-in-depth strategy, which encompasses measures for (i) device and application security, (ii) network security, (iii) physical security, as well as (iv) policies, procedures, and awareness. For each of these categories, we distill and discuss a comprehensive set of state-of-the art approaches, and identify further opportunities to strengthen cybersecurity in interconnected power grids., Comment: 19 pages, 2 figures, 1 table

Message authentication guarantees the integrity of messages exchanged over untrusted channels. However, to achieve this goal, message authentication considerably expands packet sizes, which is especially problematic in constrained wireless environments. To address this issue, progressive message authentication provides initially reduced integrity protection that is often sufficient to process messages upon reception. This reduced security is then successively improved with subsequent messages to uphold the strong guarantees of traditional integrity protection. However, contrary to previous claims, we show in this paper that existing progressive message authentication schemes are highly susceptible to packet loss induced by poor channel conditions or jamming attacks. Thus, we consider it imperative to rethink how authentication tags depend on the successful reception of surrounding packets. To this end, we propose R2-D2, which uses randomized dependencies with parameterized security guarantees to increase the resilience of progressive authentication against packet loss. To deploy our approach to resource-constrained devices, we introduce SP-MAC, which implements R2-D2 using efficient XOR operations. Our evaluation shows that SP-MAC is resilient to sophisticated network-level attacks and operates as resources-conscious and fast as existing, yet insecure, progressive message authentication schemes., Comment: ACM WiSec'22

Due to increasing digitalization, formerly isolated industrial networks, e.g., for factory and process automation, move closer and closer to the Internet, mandating secure communication. However, securely setting up OPC UA, the prime candidate for secure industrial communication, is challenging due to a large variety of insecure options. To study whether Internet-facing OPC UA appliances are configured securely, we actively scan the IPv4 address space for publicly reachable OPC UA systems and assess the security of their configurations. We observe problematic security configurations such as missing access control (on 24% of hosts), disabled security functionality (24%), or use of deprecated cryptographic primitives (25%) on in total 92% of the reachable deployments. Furthermore, we discover several hundred devices in multiple autonomous systems sharing the same security certificate, opening the door for impersonation attacks. Overall, in this paper, we highlight commonly found security misconfigurations and underline the importance of appropriate configuration for security-featuring protocols., Comment: 10 pages, 7 figures

We propose a theory for the MMRD relation of novae, using free-free emission model light curves built on the optically thick wind theory. We calculated $(t_3,M_{V,\rm max})$ for various sets of $(\dot M_{\rm acc}, M_{\rm WD})$, where $M_{V,\rm max}$ is the peak absolute $V$ magnitude, $t_3$ is the 3-mag decay time from the peak, and $\dot M_{\rm acc}$ is the mass accretion rate on to the white dwarf (WD) of mass $M_{\rm WD}$. The model light curves are uniquely characterized by $x\equiv M_{\rm env}/M_{\rm sc}$, where $M_{\rm env}$ is the hydrogen-rich envelope mass and $M_{\rm sc}$ is the scaling mass at which the wind has a certain wind mass-loss rate. For a given ignition mass $M_{\rm ig}$, we can specify the first point $x_0= M_{\rm ig}/M_{\rm sc}$ on the model light curve, and calculate the corresponding peak brightness and $t_3$ time from this first point. Our $(t_3, M_{V,\rm max})$ points cover well the distribution of existing novae. The lower the mass accretion rate, the brighter the peak. The maximum brightness is limited to $M_{V,\rm max} \gtrsim -10.4$ by the lowest mass-accretion rate of $\dot M_{\rm acc} \gtrsim1 \times 10^{-11}~M_\odot$ yr$^{-1}$. A significant part of the observational MMRD trend corresponds to the $\dot M_{\rm acc}\sim5\times10^{-9}~M_\odot$ yr$^{-1}$ line with different WD masses. A scatter from the trend line indicates a variation in their mass-accretion rates. Thus, the global trend of an MMRD relation does exist, but its scatter is too large for it to be a precision distance indicator of individual novae. We tabulate $(t_3, M_{V,\rm max})$ for many sets of $(\dot M_{\rm acc},M_{\rm WD})$., Comment: 23 pages, 9 figures, to appear in ApJ

The rising use of information and communication technology in smart grids likewise increases the risk of failures that endanger the security of power supply, e.g., due to errors in the communication configuration, faulty control algorithms, or cyber-attacks. Co-simulations can be used to investigate such effects, but require precise modeling of the energy, communication, and information domain within an integrated smart grid infrastructure model. Given the complexity and lack of detailed publicly available communication network models for smart grid scenarios, there is a need for an automated and systematic approach to creating such coupled models. In this paper, we present an approach to automatically generate smart grid infrastructure models based on an arbitrary electrical distribution grid model using a generic architectural template. We demonstrate the applicability and unique features of our approach alongside examples concerning network planning, co-simulation setup, and specification of domain-specific intrusion detection systems., Comment: 6 pages, 5 figures, to be published in Proceedings of the 3rd International Conference on Smart Energy Systems and Technologies (SEST)

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots' correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5 GiB instead of 230 GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5 h to 46 min, with even more savings for less powerful devices., Comment: To be published in the proceedings of the 2020 IFIP Networking Conference (NETWORKING'20)

To address the increasing security demands of industrial deployments, OPC UA is one of the first industrial protocols explicitly designed with security in mind. However, deploying it securely requires a thorough configuration of a wide range of options. Thus, assessing the security of OPC UA deployments and their configuration is necessary to ensure secure operation, most importantly confidentiality and integrity of industrial processes. In this work, we present extensions to the popular Metasploit Framework to ease network-based security assessments of OPC UA deployments. To this end, we discuss methods to discover OPC UA servers, test their authentication, obtain their configuration, and check for vulnerabilities. Ultimately, our work enables operators to verify the (security) configuration of their systems and identify potential attack vectors., Comment: 2 pages, 1 figure, to be published in Proceedings of the 1st ITG Workshop on IT Security (ITSec)

The fundamental changes in power supply and increasing decentralization require more active grid operation and an increased integration of ICT at all power system actors. This trend raises complexity and increasingly leads to interactions between primary grid operation and ICT as well as different power system actors. For example, virtual power plants control various assets in the distribution grid via ICT to jointly market existing flexibilities. Failures of ICT or targeted attacks can thus have serious effects on security of supply and system stability. This paper presents a holistic approach to providing methods specifically for actors in the power system for prevention, detection, and reaction to ICT attacks and failures. The focus of our measures are solutions for ICT monitoring, systems for the detection of ICT attacks and intrusions in the process network, and the provision of actionable guidelines as well as a practice environment for the response to potential ICT security incidents., Comment: 6 pages, 4 figures, to be published in Proceedings of the 2020 6th IEEE International Energy Conference (ENERGYCon)

Aims: We use new deep XMM-Newton observations of the northern disk of M 31 to trace the hot interstellar medium (ISM) in unprecedented detail and to characterise the physical properties of the X-ray emitting plasmas. Methods: We used all XMM-Newton data up to and including our new observations to produce the most detailed image yet of the hot ISM plasma in a grand design spiral galaxy such as our own. We compared the X-ray morphology to multi-wavelength studies in the literature to set it in the context of the multi-phase ISM. We performed spectral analyses on the extended emission using our new observations as they offer sufficient depth and count statistics to constrain the plasma properties. Data from the Panchromatic Hubble Andromeda Treasury were used to estimate the energy injected by massive stars and their supernovae. We compared these results to the hot gas properties. Results: The brightest emission regions were found to be correlated with populations of massive stars, notably in the 10 kpc star-forming ring. The plasma temperatures in the ring regions are ~0.2 keV up to ~0.6 keV. We suggest this emission is hot ISM heated in massive stellar clusters and superbubbles. We derived X-ray luminosities, densities, and pressures for the gas in each region. We also found large extended emission filling low density gaps in the dust morphology of the northern disk, notably between the 5 kpc and 10 kpc star-forming rings. We propose that the hot gas was heated and expelled into the gaps by the populations of massive stars in the rings. Conclusions. It is clear that the massive stellar populations are responsible for heating the ISM to X-ray emitting temperatures, filling their surroundings, and possibly driving the hot gas into the low density regions. Overall, the morphology and spectra of the hot gas in the northern disk of M 31 is similar to other galaxy disks., Comment: 23 pages, 14 figures, accepted for publication in Astronomy & Astrophysics

Novae are the observable outcome of a transient thermonuclear runaway on the surface of an accreting white dwarf in a close binary system. Their high peak luminosity renders them visible in galaxies out beyond the distance of the Virgo Cluster. Over the past century, surveys of extragalactic novae, particularly within the nearby Andromeda Galaxy, have yielded substantial insights regarding the properties of their populations and sub-populations. The recent decade has seen the first detailed panchromatic studies of individual extragalactic novae and the discovery of two probably related sub-groups: the 'faint-fast' and the 'rapid recurrent' novae. In this review we summarise the past 100 years of extragalactic efforts, introduce the rapid recurrent sub-group, and look in detail at the remarkable faint-fast, and rapid recurrent, nova M31N 2008-12a. We end with a brief look forward, not to the next 100 years, but the next few decades, and the study of novae in the upcoming era of wide-field and multi-messenger time-domain surveys., Comment: 21 pages, 5 figures, 6 tables. Accepted for publication in a Special Issue of Advances in Space Research, entitled "Nova Eruptions, Cataclysmic Variables and Related Systems: observational vs theoretical challenges in the 2020 era", following COSPAR 2018, edited by Solen Balman

M31 is an ideal laboratory for observing and studying recurrent novae. To date, there have been 18 recurrent nova discovered in M31, six of which have recurrence periods less than nine years. M31N 2017-12a (AT2017jdm) is a transient that was reported near the center of M31, with the time of outburst estimated to be 2017 December 24.141 +/- 0.42 based on constraints from previous data. Here we present the photometric observations of M31N 2017-12a, which we confirm as a recurrence of M31N 2007-10b.