Detecting and Defending Against Malicious Attacks to Ship Sensors

Summary

This article describes research to detect phenomena that may degrade or disrupt the performance of an otherwise fully functional sensor. Also examined are methods to potentially mitigate the effects of sensor degradation and develop effective countermeasures that enable naval vessels and unmanned vehicles to continue their missions with degraded capabilities. An emphasis is placed on protecting sensors and sensor systems from cyberattacks and physical attacks aimed at accessing, changing, and/or destroying sensitive and essential information. Sensor degradation that may be attributed to natural causes is also considered, along with the importance of distinguishing between nefarious intent and natural occurrence in mission performance and execution.

Background

Tactical advantages achieved by ships and unmanned vehicles are easily eroded by degrading sensor capabilities through jamming and exploiting vulnerabilities as well as environmental conditions inherent to sensor use that can compromise sensor performance, corrupt data, and reduce precision, functionality, utility, and overall effectiveness. Workarounds to prevail over such threats and accomplish mission objectives are effective only to the extent their characteristics can be accurately modeled and until adversaries modify their attack methods, forcing subsequent changes to countermeasures [1].

Methods described include the use of machine learning (ML) that can greatly assist in identifying attack characteristics from the frequency, time, and spatial-domain perspectives and train smart sensors to detect and overcome their effects. Fusion of complementary data from diverse sensors can help to maintain situational awareness under degraded sensor conditions. Deep-learning artificial intelligence (AI) also provides a means to enhance long-term sensor immunity and response to attack scenarios and harden future sensor designs against malicious activity and nefarious influences.

Current, completed research has demonstrated the effectiveness of this approach with various surface, undersea, and space-based sensors in an operational environment using a maritime testbed [2]. Conclusions reached, lessons learned, and applications to manned ships, unmanned and autonomous vehicles, and future technology directions are discussed.

Sensor Degradation Defined

Sensor degradation can be defined as deterioration or loss of performance in sensors over time due to various factors such as usage, environmental conditions, and aging. This generally involves gradual deterioration over relatively long periods of time from exposure to environmental conditions, including extreme temperatures and light, exposure to sea water and corrosive chemicals, mechanical wear of parts, electrical stress from excess voltage, current and electromagnetic interference, calibration drift, and material deterioration [3, 4]. Similar effects where partial sensor functionality remains may also occur rapidly and be permanent, long lasting, intermittent or periodic, or temporary, depending upon the cause. For external sensors, these include misalignment and partial blockage of view from being hit with debris as well as from large wave strikes, groundings, and allision that cause physical damage to sensors and transducers. Buildup of contaminants can occur from combustion of nearby vessel components, intense heat from fire, internal overheating caused by lack of ventilation, glancing strikes by laser weapons, and even the application of chemical agents designed to quickly render sensors blind or otherwise inoperable.

Internal sensor performance degradation can originate from failures within the various propulsion, electrical, hydraulic, communication, navigation, and other systems within the vessel or vehicle. A broad subset of navigation sensors was considered in this research, including radar, lidar, sonar, Automated Identification System (AIS), visible and infrared light, inertial navigation, and the Global Positioning System (GPS). Engineering sensors include vehicle temperature, pressure, flow, level, voltage, current, power, and other sensing and measurement characteristics commonly associated with these systems.

However, a critical cause of sensor degradation can be attributed to malicious activity on the part of adversaries from hacking and cyberattacks emulating many manmade and naturally occurring modes. This is the focus of this article.

Scope of the Problem

Sensors on naval vessels extend the human senses to enhance internal and external situational awareness and supplement the intelligence of trained sailors and mission specialists in performing their duties. When integrated into combat management systems (CMSs), these sensors provide multiple functions for detection, identification, command and control (C2), and decision-making. Unmanned and autonomous vehicle sensors provide onboard decision-making capabilities with comprehensive insight into states, conditions, and characteristics within the vehicle and in the surface and subsea operational environment. Natural events and adversarial activities resulting in physical destruction or damage that can render sensors useless and unable to perform their assigned functions are relatively easy to detect and identify. However, the subtleties of sensor degradation are often much more difficult to discern since high-resolution sensors and error detection and correction algorithms can compensate for degraded performance to a large degree.

Malicious actions can also significantly degrade sensor performance by tricking them into seeing things that are not there, not seeing things that are there, which are critical to mission success, and changing specific threat characteristics to disguise their true meaning and render them benign in appearance only.

Examples of successful attempts at sensor degradation by adversaries are many. Most notable was the infiltration of computer systems by the Stuxnet computer virus that changed sensor signals from centrifuges (as shown in Figure 1a) to make operational parameters appear nominal yet they were spinning far above their normal speed and tearing themselves apart [5]. Figures 1b and c illustrate where similar methods can be used to overcome safety features that restrict uncontrolled acceleration of a ship or vehicle engine, a radar antenna turning motor, motorized camera, satellite tracking antenna, or a gyroscope used to maintain heading.

Other examples include placing false echoes onto a radar screen [6], changing sonar target characteristics to obscure or disguise known threats [7], corrupting civilian and military layers of Electronic Chart Display and Information Systems (ECDISs) with false data and soundings to hide hazards to navigation [8], and manipulating AIS identities to misrepresent vessel identity, location, or intentions [9]. However, two of the more sinister threats include spoofing GPS signals on cue to ground a vessel in the middle of a critical waterway [10] and hijacking ship and vehicle controls [11, 12]. Properly timed and triggered, either of these scenarios could result in significant loss of life and catastrophic damage to vessels and critical infrastructure.

Vulnerability to Cyberattacks

The heart of all modern naval and commercial vessels is the Integrated Bridge System (IBS), defined as a combination of interconnected systems allowing centralized access to sensor information or C2 from workstations, with the aim of increasing safe and efficient ship’s management by suitably qualified personnel [13]. Analogous systems exist onboard surface and underwater unmanned and autonomous vehicles. The Safety of Life at Sea Convention [14] states that IBS “shall be so arranged that failure of one sub-system is brought to immediate attention of the officer in charge of the navigational watch by audible and visual alarms and does not cause failure to any other sub-system. In case of failure in one part of an integrated navigational system, it shall be possible to operate each other individual item of equipment or part of the system separately” [15].

However, as previously mentioned, sensor degradation involves inhibiting or reducing the performance of an otherwise fully functional sensor without causing outright failure. This nuance will generally cause built-in-test and parametric testing methods to overlook characteristics not specifically defined as representing failure. Degraded sensors may also propagate errors throughout interconnected systems. Similar conditions exist for CMSs that acquire and display a ship’s own sensor information with geographical data for C2, management, manipulation, and tactical information display.

IBSs and CMSs deliver unprecedented capabilities for vessel operation, navigation, and warfighting. However, these same network architectures also can make ships more vulnerable to cyberattacks via various flaws, potential exploits, and weaknesses in system hardware, software, administration, and organizational policies or processes [16]. With the increasing complexity of networked systems, comprehensive and thorough assessments of such vulnerabilities in terms of hardware, software, and human capabilities are essential.

The U.S. Navy is presently developing resources to take advantage of fleetwide connectivity through its Project Overmatch initiative. Integral to this is an Integrated Combat System (ICS) consisting of networked multidomain assets, including sensors, and comprising a common architecture across surface naval assets that all ships can pull from to conduct missions alone or in a group [17]. A February 2023 statement by RADM Fred Pyle at an American Society of Naval Engineers Conference in Arlington, VA, indicated that ICS will enable a decision-maker in the fleet, strike group, maritime operations center, or another ship to pair any sensor to any shooter [18]. Nevertheless, any such system can also pair any sensor to a wide range of hackers.

Sensor System Protection From Cyberattacks

Much of the literature discussing cybersecurity has traditionally discussed the attack surface against which cyberattacks are made and the need to reduce this to as small a footprint as possible. One definition describes it as the total number of all possible entry points for unauthorized access into any system, including vulnerabilities and endpoints that can be exploited [19]. This also represents the entire area of a ship’s networks and sensor systems susceptible to hacking. The smaller the attack surface, the easier it is to protect. However, naval system sensor infrastructure is already massive. As new technologies are introduced, the attack surface continues to expand. Also, with the increasing use of Internet of Ships (IoS) devices and sensors, the attack surface has expanded exponentially.

The U.S. Department of Defense (DoD) Zero Trust Strategy and Roadmap anticipates current and future cyberthreats and attacks that go beyond the traditional perimeter defense approach [20]. Rather than looking at the attack surface from a high level, in Zero Trust, the exact nature of what is needed to be protected is defined as the protect surface, which is the smallest possible reduction of the attack surface. It is defined based upon the protected data, application usage of sensitive data, asset vulnerability, and services that can be exploited to disrupt operations. The protect surface is orders of magnitude smaller than the overall attack surface and always knowable. Firewalls and other controls are moved as close as possible to the protect surface rather than the perimeter at the attack surface where it is decidedly further away from what needs to be protected. In this way, it is possible to determine what traffic moves in and out by a much smaller number of users or resources that need access to sensitive data or assets.

As with the attack surface, organizations must constantly monitor their protect surface to identify and block potential threats as quickly as possible. Theoretically, the smaller footprint makes this process more manageable. However, actual methods to model the protect surface are still being developed. Implementing distinct Zero Trust capabilities and activities is anticipated by 2027, and concerns continue on how this will be accomplished.

Access and Cyberattack Initiation

Four attributes of sensors and sensor systems must be considered in determining how access may be achieved to defend against cyberattacks—sensor, system, and network hardware; software; human interfaces; and communications systems. In the past, sensor networks by their very nature were likely to have no more than a few active human users and real-time interactions with other information technology and operational technology systems. This is rapidly changing, as data sharing between multiple fleet and shore assets increases at exponential rates. Sensor operations should occur through one or more firewalls or equivalent technologies that can detect the legitimacy of access requests. They should also be well segmented and isolated by virtue of access being physically or electronically limited to a few fully vetted people for maintenance and system upgrades. Vulnerabilities encountered through outbound communications from a sensor network are also likely to be greatly minimized, as the destinations for sensor data and analytics products should be well defined and restricted.

Attacks are most likely to occur by insiders introducing malware through software and firmware updates. In the past, this has been accomplished using USB and other devices by maintainers through satellite and other communications systems, as well as “trusted” users whose end-point systems were compromised. Despite today’s better operational procedures, viruses and other malicious code can still be passed along to computer server(s) and/or sensor controller(s) that interact with the server network and directly with the sensors. Security breaches can also occur by monitoring data communicated between sensors and their controllers and between the controllers to the server(s) using middleware that functions as a hidden layer between an operating system and sensor software applications. Middleware increases the possibility to insert malicious software that targets known vulnerabilities or models the sensor environment to devise methods on its own to detect and attack vulnerabilities within the network itself and/or the individual sensors operating within the network.

Malware can be activated immediately after insertion or remain dormant for long periods until activated upon receipt of a stimulus or code. This stimulus may be predicated from a combination of unique operating conditions exhibited by an engine or other ship’s machinery, a specific date and time, or a vessel’s position defined by latitude and longitude. Another method of initiating a cyberattack is by recognizing known unique visual, digital, acoustic, radio frequency, or directed energy signature(s) introduced external to the ship, which is then communicated through a ship’s networks to sensor systems or the sensors themselves. This includes the medium being sensed, such as the air or water, to an onboard camera, radar, sonar, or other external sensors.

Essential to maintaining cybersecurity is the need to ensure conformance and interoperability of methods and processes to provide resilient and high-performance network capabilities that include service quality, prioritization, and avoiding service preemption from accidental and malicious sources. Cybersecurity technologies and products integrated onboard naval vessels and vehicles must be strictly validated to ensure communications and interoperability at the end point applications and the servers that manage these applications to overcome multiple vulnerabilities and opportunities to infiltrate, disrupt, and otherwise compromise sensor operations.

Distinguishing Between the Natural and Nefarious

Analysis of sensor data is accomplished using combinations of characteristics represented within frequency, time, and spatial domain sensor signal representations. Differences are detected through contrast with degrees of variation from nominal sensor operation as embodied within training data sets tailored to the data characteristics of specific sensor types and models. The training requirements presume a very large volume of data across the time, frequency, and spatial (image) domains over long periods, preferably with continuous learning so that the training data will continue to improve throughout the useful life of the sensor(s). This data must contain representative samples of nominal operating conditions for all sensors involved and be tailored to individual sensor functionality. Examples may include day, night, and twilight; calm to rough seas; fair to stormy weather; empty screens to ones filled with targets; and shallow to deep waters. This is an area where deep-learning AI takes a lead role to ensure that robust and comprehensive training is achieved with minimal false indications of degradation.

Sensor degradation caused by human agencies with nefarious intent include physical and cyberattacks that are also widespread and problematic. Many such events can often be diagnosed by a watchstander or technician who has gained a high level of expertise using onboard systems. However, watchstanders with learned knowledge of sensor theory but little practical experience in sensor operation will not necessarily be able to reliably discern degradation. More importantly, unmanned vessels and autonomous vehicles have even less ability to do so. Less obvious examples of sensor degradation include gradual loss of pixels in a camera over time that steadily reduce resolution and accuracy, the effects of precipitation and fog on light propagation and infrared night vision performance, and inadequately documented performance anomalies in new types of sensors recently introduced onto the bridge. Similar effects can be identified in engineering applications such as optical devices that become contaminated and grow cloudy, proximity sensors that shift from optimal alignment, and resistive transducers that age prematurely and exceed specifications due to environmental and other factors.

Physical and cyberattack characteristics can display distinctly different and unique sensor data and signal characteristics than naturally occurring phenomena. In such cases, identification is possible by including attack data in training data sets. However, instances such as increasing degradation of individual camera pixels caused by malware leading to resolution loss can also be determined through fusing and analyzing complementary sensors’ data as well as individual sensor instruction sets, much of which can be accomplished in real time.

The capability to identify specific causes of sensor degradation requires a significant investment in further training using the specific characteristics of degraded sensor signals with one or more of the three available domain signal representations. For example, visual camera images representing spatial domain imagery illustrate the occurrence of specific causes of sufficiently different degradation to enable positive identification based upon their unique characteristics. Specific degradation classes and types can then be created for training using ML techniques.

Two different classes of degradation can be defined as being partial or full based upon the extent of the affected sensor surface area. Partial degradation affects only part of the sensor image or reduced/changed spectral frequency range and content of sensor signals, while full degradation generally affects 50% or more of the sensor image. Types of degradation for imaging sensors include damage, obstruction, obscurant, smoke, fog, and precipitation. Identifying the causes of degradation based only upon the unique characteristics presented from spatial domain perspectives may be sufficient to accurately and consistently identify degradation types and sources.

Other cases may require additional insight to determine proper classification and type. In such instances, unique characteristics associated with each degradation event may also exist within the frequency domain. The properties of each causal agent may filter specific frequencies of light entering, for example, into a camera and possibly introduce new frequencies not naturally present. These interdomain correlations can help to provide greater specificity in identification. Examining time domain characteristics can help identify the exact circumstances of degradation initiation (whether it occurred quickly or gradually), the extent of degradation, and whether it continues unchanged or if the sensor is recovering from the effects of the degradation agent through evaporation, dislocation, or another scenario.

The Faces of Sensor Degradation

Many of the effects of sensor degradation cannot be readily discerned by human operators, as training may not be available for visual clues that appear on their displays (spatial domain) and the characteristics of degradation may only appear and be detected in the frequency and/or time domain representations. However, many such examples exist and are shown in Figure 2. Detecting these sensor degradation examples and many others not shown was achieved with high reliability and reproducibility [20].

Camera images shown in Figures 2a and b depict two of many types of natural and manmade degradation that include fog, heavy precipitation, physical damage, chemical obscurants, and physical obstructions. These are just a few of the many images obtained under different weather conditions at different times of day used in testing image degradation detection capabilities. Imagery obtained from drone aircraft operating off the research vessel also provide live video, audio, and other sensor telemetry data feeds amenable to using sensor degradation detection technology.

Figure 2c illustrates degraded imagery obtained from an infrared camera. Sensor degradation was accomplished because of heavy rain showers. The degree of degradation encountered was generally proportional to shower intensity. Degradation was also achieved using chemical obscurants, physical obstructions, and conditions like smoke and fog. Additional degradation was accomplished using many of the same techniques performed for visual camera degradation, with similar results.

Figure 2d illustrates degraded radar imagery representative of different types of degradation that takes advantage of various combinations of events, timing, and signal characteristics to facilitate detection. The degraded image depicts the result of electromagnetic interference targeting the heading sensor the radar system depends on for proper geospatial orientation. The primary effect of this experiment was to cause the radar display to jitter and gyrate wildly, resulting in small-to-extreme distortion of the displayed imagery. Similar results were obtained from both digital and analog radars from different manufacturers. Many degraded states not shown were also detected.

Figures 2e and f represent two of the many different types of degradation that may be experienced by the various types of sonar sensors likely to be found on conventional and unmanned vessels. These sensors included side-scan, traditional echosounders, forward-looking navigation, and chirp sonars produced by various commercial manufacturers.

An example of lidar sensor degradation is shown in Figure 2g, which represents an image spanning 360°, with the vessel centered at the middle. Nominal operation is illustrated, where there is a natural blind spot on the navigation light array in the lower left field of view. The imagery in the red circle illustrates the area that has been degraded because of an obstruction placed in the laser propagation path that causes a shadow effect where detail is lost. Additional degradation was attempted by inflicting strikes on the sensor with several low-power (3–30 mW) lasers utilizing different wavelengths (450–905 nM). Results included shadows casted from the direction of origin, interference lines, and occasional display blinking.

Figure 2h shows an example of onboard weather sensor degradation performed by introducing external stimulus and situations that would interfere with normal operation. Illustrated are the effects of splashed and streams of water that resulted in maximum readings in wind speed and relative humidity, abnormal temperature readings, and other measurements uncharacteristic and unrepresentative of the physical environment. Anomalies were readily detected and included water and air temperature, humidity, barometric pressure, wind speed, and direction during a clear, calm day, with steady temperatures and without wind.

Figure 2i illustrates reception degradation of satellite transmissions for weather and other onboard services achieved by antenna shielding and disrupting the reception path between the satellite and the vessel. The loss of signal resulted in the degradation, which was easily detected. Note that in the absence of a signal, the satellite weather display became blank. However, the AIS overlay continued without interruption.

Future Opportunities and Technologies

There are many opportunities to create multiple solutions that enable rapid development and dynamic update of accurate and comprehensive reduced-order models of sensor and sensor system architectures. This is especially important when dealing with complex vessel network infrastructure to detect critical system elements that would make them susceptible to model-based attacks. Novel AI-based and future quantum methods will help autonomously and rapidly build and update these models to represent classes of sensors and sensor network system functionalities that span multiple, different ship and unmanned vehicle infrastructure configurations and defend against a multitude of diverse threat vectors. Such methods must minimize human-guided domain knowledge and training requirements and feature dynamic development, adaptation, and reconfiguration to overcome vulnerabilities exposed by destabilization cyberattacks. Order-of-magnitude reduction in development time over existing methods is needed, along with enhanced resilience to broad-based attacks and reduced brittleness to previously unknown cybertactics and a broad range of natural phenomena such tactics can mimic.

One focus of the 2023 DoD Cyber Strategy is developing and applying new technologies to expand cybercapabilities and prioritizing technologies to confound malicious cyberactors and prevent them from achieving their objectives in and through cyberspace [21]. These DoD capabilities implement the priorities of the National Cybersecurity Strategy to defend critical infrastructure and disrupt and dismantle threat actors [22]. This includes Zero Trust architectures and their associated cybersecurity technologies, advanced endpoint monitoring capabilities, tailored data collection strategies, enhanced cyberforensics, automated data analytics, and systems that enable network automation, network restoration, and network deception. Assisting in this implementation is the recently launched Cyber Operational Readiness Assessment (CORA) Program of the Joint Force Headquarters–Department of Defense Information Network (JFHQ-DODIN) to harden information systems, reduce the attack surface of their cyberterrain, and enhance a more proactive defense [23]. CORA implementation for ships and unmanned vehicles is under the U.S. Fleet Cyber Command, which serves as the Navy’s component command to the U.S. Cyber Command.

Quantum computing is also envisioned to equip the Navy with more secure communications networks, more advanced sensors, and faster threat detection and response that comes with them to improve navigation and result in smarter autonomous systems and more accurate modeling and simulation [24]. The Naval Research Laboratory and Naval Information Warfare Center (NIWC) Pacific have established the Naval Quantum Computing Program Office, where quantum subject matter experts across all 14 naval warfare centers can collaborate on quantum applications for the DoD [25].

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Biography

R. Glenn Wright has over 45 years of experience in aerospace, maritime, and medicine. He has led projects associated with sensor-based systems that include surface vessel, autonomous underwater, and marine remotely operated vehicles; meteorological and oceanographic data systems; and surface navigation. He is a member of the National Academies of Science, Engineering, and Medicine Transportation Research Board committees on maritime safety and AI; an expert contributor to the International Hydrographic Organization Crowd Sourced Bathymetry Working Group; a master mariner; and an author of books on autonomous ships and their sensors and more than 100 journal articles and conference papers. Dr. Wright holds a B.S. in electrical engineering from the New Jersey Institute of Technology, an M.S. in computer science from the Polytechnic Institute of New York University, and a Ph.D. in maritime affairs from the World Maritime University in Malmö, Sweden.