Unmanned Aerial Vehicle security using Recursive parameter estimation

Birnbaum, Zachary; Dolgikh, Andrey; Skormin, Victor A.; O’Brien, Edward E.; Müller, Daniel

doi:10.1109/icuas.2014.6842314

Cited by 29 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We observed that the focus of the investigated literature is mainly on Industry 4.0, even if in this field highly varying scenarios are considered. For example, some articles discuss petrochemical plant management [246], while others focus on drones [177], [192], [199], and so on. Each of these scenarios has its own threat model and will thus also differ in terms of security requirements from the others, to a certain degree.…”

Section: Iiot Security Requirements (Rq1)mentioning

confidence: 99%

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

Tange

Donno

Fafoutis

et al. 2020

IEEE Commun. Surv. Tutorials

296

112

View full text Add to dashboard Cite

A key application of the Internet of Things (IoT) paradigm lies within industrial contexts. Indeed, the emerging Industrial Internet of Things (IIoT), commonly referred to as Industry 4.0, promises to revolutionize production and manufacturing through the use of large numbers of networked embedded sensing devices, and the combination of emerging computing technologies, such as Fog/Cloud Computing and Artificial Intelligence. The IIoT is characterized by an increased degree of inter-connectivity, which not only creates opportunities for the industries that adopt it, but also for cyber-criminals. Indeed, IoT security currently represents one of the major obstacles that prevent the widespread adoption of IIoT technology. Unsurprisingly, such concerns led to an exponential growth of published research over the last few years. To get an overview of the field, we deem it important to systematically survey the academic literature so far, and distill from it various security requirements as well as their popularity. This paper consists of two contributions: our primary contribution is a systematic review of the literature over the period 2011-2019 on IIoT Security, focusing in particular on the security requirements of the IIoT. Our secondary contribution is a reflection on how the relatively new paradigm of Fog computing can be leveraged to address these requirements, and thus improve the security of the IIoT.

show abstract

Section: Iiot Security Requirements (Rq1)mentioning

confidence: 99%

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

Tange

Donno

Fafoutis

et al. 2020

IEEE Commun. Surv. Tutorials

296

112

View full text Add to dashboard Cite

show abstract

“…The paper authored by Zachary Birnbaum et al describes a solution for security in UAVs based on Recursive Parameter Estimation [13]. The authors start by identifying several UAV threats, like malicious hardware, hardware failures, attacks both against the communications channel and the flight control computer and attacks against the ground control station.…”

Section: A Study Of the State Of The Artmentioning

confidence: 99%

SEDIBLOFRA: A Blockchain-Based, Secure Framework for Remote Data Transfer in Unmanned Aerial Vehicles

et al. 2021

View full text Add to dashboard Cite

Security attacks on Cyber-Physical Systems with operations that involve Unmanned Aerial Vehicles (UAVs) are a matter of great concern due to their major impact in the deployed systems, and a deal-breaker for their utilization; if a system is not perceived as secure, either it will not be used or its capabilities will be underutilized, regardless of how good they could be. This happens with particular intensity in missions with UAVs, as they can be hacked to tamper with their collected data or taken away by unauthorized parties. Development of security countermeasures is extensive both in theoretical and practical implementations, but the integration of these measures can be difficult, and performance might be affected because of it. This manuscript puts forward a SEDIBLOFRA (Secure, Distributed, Blockchainbased Framework) for remote maneuvering of UAVs, based on several distributed systems technologies that pile up to provide complementary functionalities. Asymmetric cryptography encrypts the data. Publish/Subscribe communications offer a way to enhance data delivery. Finally, blockchain provides a way to keep all the transferred data in a redundant and immutable manner. The proposed framework is also extensible to other kinds of unmanned vehicles, like Unmanned Ground Vehicles or Autonomous Underwater Vehicles.

show abstract

“…[15], [41], [42], [43], [44] Command Injection Request execution of existing command with malicious intent, typically to affect actuation [30], [15], [31], [32] Impersonation (or masquerade or spoofing) attack An adversary assumes successfully the identity of one of the legitimate nodes in the vehicular network [41], [45], [44], [46], [36], [47], [48], [49] Packet Duplication Transmit unnecessary network messages to exhaust bandwidth or trigger unnecessary processing [24], [23], [25] Selective Forwarding Retransmit data selectively in a vehicular network [24], [23], [25] GPS Jamming Jam legitimate GPS signals; possibly followed by GPS spoofing [28] GPS Spoofing Transmit false GPS signals to disrupt or hijack navigation of a GPSdependent vehicle, such as a UAV [28] Fuzzing (Fuzz testing) Send random messages to the in-vehicle network to trigger critical instructions in a brute force manner) [21] False Data Injection Transmit false data to trigger malicious events or affect situational/environmental awareness [28] False Information Dissemination Transmit false data, e.g. a reputation score, to affect a collaborative process in a network [28], [50] Location Spoofing Share false location coordinates within a vehicular network [39] Malware Infect vehicle with malicious software/firmware by compromising supply chain or hijacking an update [30], [51], [52], [19], [53] Resource exhaustion attack Exhaust a vehicle's battery/fuel, network, processing or other resource by repeating requests, infecting with malware, etc.…”

Section: Attackmentioning

confidence: 99%

“…Maliciously craft input data to sensors specifically aiming to affect its machine learning policies [56] Hardware Tampering Tamper with hardware or gain physical access to modify/damage components or infect with malware [51] Hardware Failure Physical damage or natural degradation of a vehicle's components [51] Fraudulent ADS-B Messages Transmit false ADS-B messages to affect aircraft safety [57], [58] AIS spoofing Transmit false AIS signals to impede vessel tracking [59], [60], [61] Isolation attack Isolate a node from a network by dropping all messages going to or coming from it…”

Section: Attackmentioning

confidence: 99%

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Karapistoli²,

et al. 2019

View full text Add to dashboard Cite

With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research.

show abstract

Unmanned Aerial Vehicle security using Recursive parameter estimation

Cited by 29 publications

References 13 publications

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

SEDIBLOFRA: A Blockchain-Based, Secure Framework for Remote Data Transfer in Unmanned Aerial Vehicles

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Contact Info

Product

Resources

About