Jan Bauer

Controller Area Networks (CANs) are the backbone for reliable intra-vehicular communication. Recent cyberattacks have, however, exposed the weaknesses of CAN, which was designed without any security considerations in the 1980s. Current efforts to retrofit security via intrusion detection or message authentication codes are insufficient to fully secure CAN as they cannot adequately protect against masquerading attacks, where a compromised communication device, a so-called electronic control units, imitates another device. To remedy this situation, multicast source authentication is required to reliably identify the senders of messages. In this paper, we present CAIBA, a novel multicast source authentication scheme specifically designed for communication buses like CAN. CAIBA relies on an authenticator overwriting authentication tags on-the-fly, such that a receiver only reads a valid tag if not only the integrity of a message but also its source can be verified. To integrate CAIBA into CAN, we devise a special message authentication scheme and a reactive bit overwriting mechanism. We achieve interoperability with legacy CAN devices, while protecting receivers implementing the AUTOSAR SecOC standard against masquerading attacks without communication overhead or verification delays.

While autonomy of unmanned systems is advancing rapidly, human oversight remains essential due to legal and operational constraints, making communication a critical factor. This paper explores the impact of communication delays and bandwidth limitations on the remote operation of those systems, focusing on long-range offshore underwater missions conducted with extra-large unmanned underwater vehicles as mobile operation platforms. In such scenarios, communication impairments, such as delays, jitter, bandwidth restrictions, and packet loss, can severely affect human remote control of the system. We address this challenge in the context of uncrewed underwater vehicles, which often rely on multi-hop communication via buoys to extend the range of remote control, and propose a methodology to empirically investigate the communication impact. Through preliminary experiments with remotely operated vehicles, we collect real-world data for further analysis and simulation.

Marine radar systems are a core technical instrument for collision avoidance in shipping and an indispensable decision-making aid for navigators on the ship’s bridge in limited visibility conditions at sea, in straits, and harbors. While electromagnetic attacks against radars can be carried out externally, primarily by military actors, research has recently shown that marine radar is also vulnerable to attacks from cyberspace. These can be carried out internally, less “loudly”, and with significantly less effort and know-how, thus posing a general threat to the shipping industry, the global maritime transport system, and world trade. Based on cyberattacks discussed in the scientific community and a simulation environment for marine radar systems, we investigate in this work to which extent existing Intrusion Detection System (IDS) solutions can secure vessels’ radar systems, how effective their detection capability is, and where their limits lie. From this, we derive a research gap for radar-specific methods and present the first two approaches in that direction. Thus, we pave the way for necessary future developments of anomaly detection specific for marine navigation radars.

The Maritime Transportation System (MTS) is the foundation of global trade, a crucial pillar of our economy’s supply chains, but also critical to energy and food security. At the same time, it is increasingly exposed to new types of threats. These include attacks from the cyber and electromagnetic spectrum against various information and telecommunications systems on board vessels as the backbone of the MTS. The radio-based Automatic Identification System (AIS) is one of these systems, used to monitor ship routes and for traffic planning, and supports collision avoidance on the vessel side. The technical vulnerabilities of the system have long been known and caution is therefore advised. Conventional attacks are usually easy to recognize, so that disruptions to operations can occur, but serious damage can be limited. In this paper, however, we present a novel radio-based attack on AIS that enables to selectively suppress identification messages of individual ships and thus to manipulate the situational awareness on the vessel’s bridge in a stealthy manner, which can have catastrophic impacts in critical situations. We demonstrate the technical feasibility of this attack in a laboratory environment with real hardware and, by elaborating this vulnerability, we are contributing to increasing the resilience of the maritime domain against evolving hybrid threats.

Today’s maritime transportation relies on global navigation satellite systems (GNSSs) for accurate navigation. The high-precision GNSS receivers on board modern vessels are often considered trustworthy. However, due to technological advances and malicious activities, this assumption is no longer always true. Numerous incidents of tampered GNSS signals have been reported. Furthermore, researchers have demonstrated that manipulations can be carried out even with inexpensive hardware and little expert knowledge, lowering the barrier for malicious attacks with far-reaching consequences. Hence, exclusive trust in GNSS is misplaced, and methods for reliable detection are urgently needed. However, many of the proposed solutions require expensive replacement of existing hardware. In this paper, therefore, we present MAritime Nmea-based Anomaly detection (MANA), a novel low-cost framework for GPS spoofing detection. MANA monitors NMEA-0183 data and advantageously combines several software-based methods. Using simulations supported by real-world experiments that generate an extensive dataset, we investigate our approach and finally evaluate its effectiveness.

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.

Today’s shipping industry is largely digitalized and networked, but by no means immune to cyber attacks. As recent incidents show, attacks, particularly those targeting on the misleading of navigation, not only pose a serious risk from an economic perspective when disrupting maritime value chains, but can also cause collisions and endanger the environment and humans. However, cyber defense has not yet been an integral part of maritime systems engineering, nor are there any automated tools to systematically assess their security level as well-established in other domains. In this paper, we therefore present a holistic BRidge Attack Tool (BRAT) that interactively offers various attack implementations targeting the communication of nautical data in maritime systems. This provides system engineers with a tool for security assessments of integrated bridge systems, enabling the identification of potential cyber vulnerabilities during the design phase. Moreover, it facilitates the development and validation of an effective cyber defense.