Unmanned Aerial Vehicle security using behavioral profiling

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

doi:10.1109/icuas.2015.7152425

Cited by 35 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the work of [357], the authors present a UAV monitoring system that captures flight data to perform real-time abnormal behavior detection. If an abnormal behavior is detected, the system will raise an alert.…”

Section: Security Challengesmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

View full text Add to dashboard Cite

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

show abstract

Section: Security Challengesmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The model's objective is to detect potential vulnerabilities based on drone behavior evaluation. Alternatively, the deployment of onboard and external IDSs to prevent attacks on the security principals of cybersecurity, namely, confidentiality, integrity, and availability, has been presented in [12][13][14]. Prior researchers [12,13] concentrated more on detecting abnormal flight patterns indicating an unauthorized change of flight trajectory such as GPS spoofing, and structural failure, while the work presented in [14] deployed a combination of IDS techniques combining both rule and anomaly-based detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

Salamh

Karabiyik

Rogers

et al. 2021

Drones

View full text Add to dashboard Cite

The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the `purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates `Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels

show abstract

“…[24], [23], [25] Ranging Manipulation Share incorrect time tags within a vehicular network to disrupt a vehicle's ranging capabilities [39] Sensory channel attack Manipulate the physical environment so as to deceive a vehicle's critical sensors, such as lidar or cameras used by driverless vehicles [4], [54], [46], [55] Adversarial machine learning attack on driverless vehicle…”

Section: Attackmentioning

confidence: 99%

“…has produced promising results in detecting GPS spoofing, denial of service and malicious command injection. The same authors [54] have also argued that instead of looking at flight data in isolation it is preferable to learn to identify the events that correspond to them. For example, aggregating from several data points can help identify the elementary event "sharp left turn", and then detecting "incline" and "turn" can merge into the more complex "spiralling upward" event, and so forth, up to the definition of the UAV's mission.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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

Loukas

Karapistoli²,

Panaousis

et al. 2019

Ad Hoc Networks

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 behavioral profiling

Cited by 35 publications

References 10 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

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

Contact Info

Product

Resources

About