Empirical Analysis of MAVLink Protocol Vulnerability for Attacking Unmanned Aerial Vehicles

Kwon, Young-Hyuk; Yu, Jeong Sik; Cho, Byeong-Moon; Eun, Yongsoon; Park, Kyung‐Joon

doi:10.1109/access.2018.2863237

Cited by 88 publications

(38 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other practical challenges to deployment of aerial cellular 5G networks include the restrictive capacity of current battery technologies which limit flight times [20] and the potential for security attacks against the UAVs [21], [22]. While the new work in this manuscript does not specifically address these issues directly, the problems of limited battery capacity of the drone was noted during testing (three heavy-duty battery power packs were consumed during the testing) and the matter of drone security must be noted as a genuine threat against service delivery.…”

Section: Introductionmentioning

confidence: 99%

Radio Channel Characterization of Mid-Band 5G Service Delivery for Ultra-Low Altitude Aerial Base Stations

et al. 2019

View full text Add to dashboard Cite

This paper presents a study which evaluated the potential for using ultra-low altitude, unmanned aerial vehicles to deliver fifth-generation (5G) cellular connectivity, particularly into areas requiring short-term enhancement in coverage. Such short-term enhancement requirements may include large gatherings of people or during disaster scenarios where there may be service outages or a need for increased bandwidth. An evaluation of this approach was conducted with empirically generated results regarding signal quality and cellular coverage-illustrating the potential of using unmanned ultra-low altitude aerial vehicles to deliver 5G cellular mobile services. Specifically, channel gain, mean time delay of the received signals (τ mean), and the root-mean-square spread of the delay (τ rms) were investigated for two distinct user modes at three different drone heights for three selected environments-an open area (field), a tree-lined environment, and an enclosed area. Maximum likelihood estimates for the various drone heights, user modes, and operational environments were found to be Rician distributed for the received signal strength measurements, whereas τ mean and τ rms for the open and tree-lined environments were Weibull distributed with the enclosed area tests being lognormally distributed. The paper also investigates how the channel gain may be affected when operating in each of the various global bands allocated for mid-5G communications, namely, Europe, China, Japan, South Korea, and North America. These regional mid-5G band allocations were found to yield minimal variance for all the environments considered.

show abstract

Section: Introductionmentioning

confidence: 99%

Radio Channel Characterization of Mid-Band 5G Service Delivery for Ultra-Low Altitude Aerial Base Stations

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For the secure communication using WiFi, it can be protected via unique passwords. The analysis of the vulnerabilities of micro-air-vehicle communication (MAVLink) protocol was included in [93]. In the MAVLink protocol, two specific issues can arise.…”

Section: Wifi Insecurity and Jamming Attackmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

View full text Add to dashboard Cite

With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

show abstract

“…(1) An adversary can manipulate the configuration parameters of an RV by either overriding them before a flight or changing them after the drone takes off (similar to [41]). (2) An adversary can replay or spoof user commands sent to the RV by exploiting known vulnerabilities in the RV's communication protocol [43], [58]. (3) An adversary can manipulate the environmental conditions (or wait until suitable conditions are met) before conducting their attack (similar to [23], [41]).…”

Section: Motivating Examplementioning

confidence: 99%

PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Kim¹,

Ozmen²,

Bianchi³

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

Unfortunately, previous fuzzing approaches cannot discover this type of violations for the following two reasons. First, they do not consider the entire input space of the RV's control software, including user commands, configuration parameters, and environmental factors. Second, they only focus on finding memory corruption bugs or RV's control stability issues. Therefore, they cannot detect safety policy violations, e.g., a drone is deploying the parachute at a too-low altitude. We develop PGFUZZ, a policy-based fuzzing framework designed to address these challenges. PGFUZZ includes three interconnected components: (1) Pre-Processing, (2) Policy-Guided Fuzzing, and (3) Bug Post-Processing. In the Pre-Processing component, we express the correct operation of an RV through policies denoted by a metric temporal logic (MTL). Thereafter, we minimize the fuzzing space via finding inputs related to the tested policies that, when mutated, could potentially trigger policy violations. For example, given a policy in natural language stating that "the fail-safe mode must be triggered when the engine temperature is higher than 100°C", PGFUZZ expresses this policy with the MTL formula: {(temperature>100°C) → (failsafe=on)}. It then decomposes this formula into the temperature and the fail-safe mode states, and identifies fuzzing inputs such as user commands (e.g., increasing temperature) and configuration parameters (e.g., units of temperature), influencing the policy states. Then, the Policy-Guided Fuzzing mutates inputs identified by the Pre-Processing component. It implements two kinds of distance metrics, propositional distances to guide the mutation engine, and a global distance to detect when a policy violation occurs. The distance metrics quantify how close the current system states are to a policy violation. Positive distances indicate the policy holds, whereas negative distances indicate the policy is violated. Therefore, PGFUZZ mutates inputs to minimize the global distance. After each input is sent to the control software, which runs in an RV simulator, PGFUZZ collects the system states and computes the distance metrics. The input's impact on the distance metric (whether it increases or decreases) is leveraged to decide on the next inputs. When the global distance becomes negative, a policy violation is detected. Turning to the fail-safe mode example, PGFUZZ mutates inputs to increase the temperature to be larger than 100°C, and checks whether, at the same time, the fail-safe mode is activated. The last component, Bug Post-Processing, minimizes the input sequence triggering the bugs by excluding inputs irrelevant to the policy violation. The minimized input sequence is then used to identify the root cause of each violated policy. To verify the correctness and effectiveness of PGFUZZ, we Abstract-Robotic vehicles (RVs) are becoming essential tools of modern systems, including autonomous delivery services, public transportation, and environment monitoring. Despite their diverse deployment, safety and security issues ...

show abstract

Empirical Analysis of MAVLink Protocol Vulnerability for Attacking Unmanned Aerial Vehicles

Cited by 88 publications

References 11 publications

Radio Channel Characterization of Mid-Band 5G Service Delivery for Ultra-Low Altitude Aerial Base Stations

Radio Channel Characterization of Mid-Band 5G Service Delivery for Ultra-Low Altitude Aerial Base Stations

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Contact Info

Product

Resources

About