Towards Trusted and Efficient UAV-Based Communication

Mohammed, Farhan; Jawhar, Imad; Mohamed, Nader; Idries, Ahmed

doi:10.1109/bigdatasecurity-hpsc-ids.2016.57

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…This IDS ignored other frequently used internet attacks focusing more on UAVs' attacks. Mohammed et al [9] parallelized UAVs with Mobile Ad hoc Networks (MANETs), investigating their similarities and applications. The research identified that, although UAV networks considered as a type of MANET, they require computationally lighter methods due to power constraints.…”

Section: Related Studiesmentioning

confidence: 99%

Autonomous detection of malicious events using machine learning models in drone networks

Moustafa

Jolfaei

2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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Drone systems, the so-called Unmanned Autonomous Vehicles (UAVs), have been widely employed in military and civilian sectors. Drone systems have been used for cyber warfare, warfighting and surveillance purposes of modern military and civilian applications. However, they have increasingly suffered from sophisticated malicious activities that exploit their vulnerabilities through network communications. As drones comprise a complex infrastructure as piloted aircraft but without operators, they still need a reliable security control to assert their safe operations. This paper proposes an autonomous intrusion detection scheme for discovering advanced and sophisticated cyberattacks that exploit drone networks. A testbed was configured to launch malicious events against a drone network for collecting legitimate and malicious observations and evaluate the performances of machine learning in real-time. Machine learning algorithms, including decision tree, k-nearest neighbors, naive Bayes, support vector machine and deep learning multi-layer perceptron, were trained and evaluated using the data collections, with promising results in terms of detection accuracy, false alarm rates, and processing times.

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Section: Related Studiesmentioning

confidence: 99%

Autonomous detection of malicious events using machine learning models in drone networks

Moustafa

Jolfaei

2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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“…Moahmmed et al [ 22 ] analyzed the requirements for efficient UAV communication. They identified the similarities and differences between MANETs and FANETs, and discussed various trust-based protocols and management schemes that can be used in FANET.…”

Section: Background and Related Workmentioning

confidence: 99%

A Trusted Lightweight Communication Strategy for Flying Named Data Networking

Barka

Kerrache

Hussain

et al. 2018

Sensors

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Flying Ad hoc Network (FANET) is a new resource-constrained breed and instantiation of Mobile Ad hoc Network (MANET) employing Unmanned Aerial Vehicles (UAVs) as communicating nodes. These latter follow a predefined path called ’mission’ to provide a wide range of applications/services. Without loss of generality, the services and applications offered by the FANET are based on data/content delivery in various forms such as, but not limited to, pictures, video, status, warnings, and so on. Therefore, a content-centric communication mechanism such as Information Centric Networking (ICN) is essential for FANET. ICN addresses the problems of classical TCP/IP-based Internet. To this end, Content-centric networking (CCN), and Named Data Networking (NDN) are two of the most famous and widely-adapted implementations of ICN due to their intrinsic security mechanism and Interest/Data-based communication. To ensure data security, a signature on the contents is appended to each response/data packet in transit. However, trusted communication is of paramount importance and currently lacks in NDN-driven communication. To fill the gaps, in this paper, we propose a novel trust-aware Monitor-based communication architecture for Flying Named Data Networking (FNDN). We first select the monitors based on their trust and stability, which then become responsible for the interest packets dissemination to avoid broadcast storm problem. Once the interest reaches data producer, the data comes back to the requester through the shortest and most trusted path (which is also the same path through which the interest packet arrived at the producer). Simultaneously, the intermediate UAVs choose whether to check the data authenticity or not, following their subjective belief on its producer’s behavior and thus-forth reducing the computation complexity and delay. Simulation results show that our proposal can sustain the vanilla NDN security levels exceeding the 80% dishonesty detection ratio while reducing the generated end-to-end delay to less than 1 s in the worst case and reducing the average consumed energy by more than two times.

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“…In [8], the authors analyzed the requirements for efficient UAV communications, identifying the similarities and the differences between MANETs and UAV-based networks and protocols. They also discussed the various trust-based protocols and management schemes that can be used in UAV networks.…”

Section: Main Existing Trust-based Solutionsmentioning

confidence: 99%

“…Furthermore, the attacks against service availability are generally related to packet drops and Denial of Service (DoS). Both kinds of attacks can be faced using either cryptographic techniques [7] or trust management techniques [8]. Cryptographic techniques are the best solution against outsider unauthorized entities, but these techniques are known to require high computation overhead and consume much energy, becoming a problem for current commercial UAVs [9].…”

Section: Introductionmentioning

confidence: 99%

UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication

Barka

Kerrache

Lagraa

et al. 2018

Journal of Advanced Transportation

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Ensuring the desired level of security is an important issue in all communicating systems, and it becomes more challenging in wireless environments. Flying Ad Hoc Networks (FANETs) are an emerging type of mobile network that is built using energy-restricted devices. Hence, the communications interface used and that computation complexity are additional factors to consider when designing secure protocols for these networks. In the literature, various solutions have been proposed to ensure secure and reliable internode communications, and these FANET nodes are known as Unmanned Aerial Vehicles (UAVs). In general, these UAVs are often detected as malicious due to an unintentional misbehavior related to the physical features of the UAVs, the communication mediums, or the network interface. In this paper, we propose a new context-aware trust-based solution to distinguish between intentional and unintentional UAV misbehavior. The main goal is to minimize the generated error ratio while meeting the desired security levels. Our proposal simultaneously establishes the inter-UAV trust and estimates the current context in terms of UAV energy, mobility pattern, and enqueued packets, in order to ensure full context awareness in the overall honesty evaluation. In addition, based on computed trust and context metrics, we also propose a new inter-UAV packet delivery strategy. Simulations conducted using NS2.35 evidence the efficiency of our proposal, called UNION, at ensuring high detection ratios > 87% and high accuracy with reduced end-to-end delay, clearly outperforming previous proposals known as RPM, T-CLAIDS, and CATrust.

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Towards Trusted and Efficient UAV-Based Communication

Cited by 23 publications

References 12 publications

Autonomous detection of malicious events using machine learning models in drone networks

Autonomous detection of malicious events using machine learning models in drone networks

A Trusted Lightweight Communication Strategy for Flying Named Data Networking

UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication

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